Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications

Eduardo Alonso City University London, UK

Esther Mondragón Centre for Computational and Animal Learning Research, UK

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Library of Congress Cataloging-in-Publication Data

Computational neuroscience for advancing artificial intelligence : models, methods and applications / Eduardo Alonso and Esther Mondragon, editors. p. cm. Summary: "This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro- psychological models and computational models"-- Provided by publisher. Includes bibliographical references and index. ISBN 978-1-60960-021-1 (hardcover) -- ISBN 978-1-60960-023-5 (ebook) 1. Computational neuroscience. 2. Neurosciences--Mathematical models. 3. Artificial intelligence. I. Alonso, Eduardo, 1967- II. Mondragon, Esther, 1965- QP357.5.C634 2011 612.80285'63--dc22 2010018588

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All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. 214

Chapter 10 Artificial Neural Systems for Robots

Phil Husbands University of Sussex, UK

Andy Philippides University of Sussex, UK

Anil K. Seth University of Sussex, UK

ABSTRACT This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.

INTRODUCTION isolated island. This often dark and dystopian play was a world-wide smash hit capturing the popular The idea of robots is rooted in the dreams and myths imagination as well as sparking much intellectual of old Europe and Ancient Greece in which me- debate (Horáková and Kelemen 2007). In the chanical men acted as either slaves or oppressors. process it helped forge the predominant image Indeed the modern word ‘robot’ was introduced of robots that now permeates our culture – that in Karel Čapek’s 1921 play R.U.R. (Rossum’s of life-like artificial creatures. Universal Robots) which told of artificial men In fact this image refers to what we now call manufactured as a source of cheap labour on an autonomous robots. In contrast to machines that perform precise repetitive tasks ad nauseam (e.g. robots used in manufacturing production lines), DOI: 10.4018/978-1-60960-021-1.ch010

autonomous robots are required to behave in an (2002), Bekey (2005), Bekey and Goldberg(1993) appropriate way in a very broad range of circum- and Floreano and Mattiussi (2008). stances. Like biological creatures, their behaviour must be self-generated, making use of sensory information to moderate their responses to the HISTORY world. With these animal connotations, it is no surprise that when simple autonomous robots be- Despite the construction of many ingenious me- came a reality in the mid 20th century, researchers chanical automata over the centuries (including looked to natural nervous systems for inspiration chess playing Turks and flatulent ducks (Wood as they developed their early control systems. 2003)), it was not until the 1930s that devices The huge advances in our understanding of recognizable as robots (in the present day sense real neural networks over the past few decades, of the term) appeared. Early mobile robots, coupled with our development of increasingly such as Thomas Ross’s ‘robot rat’, completed sophisticated artificial varieties, has led to sig- in 1935, were designed for narrowly focused nificant growth in research on artificial neural single behaviours (often maze running) and em- systems in robotics. This chapter concentrates on ployed highly specific mechanisms to achieve the use of explicitly biologically inspired artificial their intended task (Cordeschi, 2002). These neural networks in autonomous robotics, review- ‘artificial animals’ inspired what were probably ing key applications and forms of neural system the very first examples of more general mobile used. There are a variety of drives underlying such autonomous robots – W. Grey Walter’s tortoises work, ranging from straightforward engineering (Walter, 1950). These robots were also the first to motivations – the desire to build better, smarter employ an early form of neural network as their machines – to purely scientific ones, particularly artificial nervous system. They were born out of in the use of robots as tools to study mechanisms the cybernetics movement, a highly interdisci- underlying the generation of adaptive behaviour plinary endeavour – drawing together pioneers in animals and humans. Often varying degrees of of computing and modern neuroscience – which both these motivations are present in any particular was the forerunner of much of contemporary AI project. This chapter will highlight work that falls and robotics, and the origin of artificial neural at the two extremes of this spectrum, as well as networks and evolutionary computing, as well much that rests in between. as control and information theory (Boden, 2006; The next section gives some historical back- Husbands et al., 2008). ground to the area in order to motivate the remain- In 1949, Walter, a neurologist and cyberneticist der of the chapter. Following that, there are two based at the Burden Institute in Bristol, UK, who major sections that make up the bulk of the paper: was also a world leader in EEG research, com- one on the application of artificial neural systems pleted a pair of revolutionary machines he called to robot control, and one on the use of robots as ‘tortoises’. The devices were three-wheeled and tools in neuroscience. The chapter closes with a sported a protective ‘shell’ (see Figure 1). They general discussion of prospects for such research. had a light sensor, touch sensor, propulsion motor, There is not enough space to give a comprehensive steering motor, and an electronic valve (vacuum review of all major work in the area, instead we tube) based analogue ‘nervous system’. Whereas have concentrated on a few important topics that earlier machines such as Ross’s were constrained to give a good overall flavour of research in the field. run on rails, the tortoises could roam freely around For more detailed coverage see e.g. Siciliano and their environment. Walter’s intention was to show Khatib (2008), Webb and Consi (2001), Ayers et al. that, contrary to the prevailing opinion at the time,

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Figure 1. Grey Walter watches one of his tortoises even a very simple nervous system (the tortoises push aside some wooden blocks on its way back had two artificial neurons) could generate complex to its recharging hutch. Circa 1952 behaviour as long as there was a sufficiently large number of possible interaction patterns between the neurons (Walter, 1950). By studying whole embodied sensorimotor systems acting in the real world, he was pioneering a style of research that was to become very prominent in AI many years later, and remains so today (Brooks, 1999; Hol- land, 2003). Between Easter 1948 and Christmas 1949, he built the first tortoises, Elmer and Elsie. They were rather unreliable and required frequent attention. In 1951, his technician, Mr. W.J. ‘Bunny’ Warren, designed and built six new tortoises to a much higher standard. Three of these tortoises were exhibited at the Festival of Britain in 1951; others were regularly demonstrated in public throughout the 1950s. The robots were capable of phototaxis (steering towards a light source), by which means they could find their way to a recharging station when they ran low on battery power. He referred to the devices as Machina speculatrix after their apparent tendency to speculatively explore their environment. Walter was able to demonstrate a variety of withdrawal movements that resulted in small interesting behaviours as the robots interacted objects being pushed out of the way while large with their environment and each other. In one obstacles and gradients were avoided. Walter experiment he placed a light on the ‘nose’ of a developed a later generation of robots that were tortoise and watched as the robot observed itself equipped with extended, although still relatively in a mirror. “It began flickering,” he wrote. “Twit- simple, artificial nervous systems capable of tering, and jigging like a clumsy Narcissus.” exhibiting learning behaviour: a conditioned reflex Walter argued that if this behaviour was observed (Walter, 1951). With this work Walter demon- in an animal it “might be accepted as evidence of strated, for the first time, an artificial neural some degree of self-awareness.” (Walter, 1953). mechanism that generated interesting learning The behavioural complexity was partly due to the behaviour in an embodied autonomous senso- fact that the neural circuitry exploited the non- rimotor system. While Walter’s motivations were linear properties of the electronic valves and primarily scientific – he used the robots to inves- photoreceptors such that very bright lights were tigate ideas about neural processing in brains – repellent when the battery charge was high and potential practical applications of such technol- attractive when it was low, thus allowing the ogy were not lost on him and his cybernetics tortoise to return to its hutch (marked by a bright colleagues. For instance, Thomas Gold recalled light) when it needed to recharge. Interactions discussion of possible future uses at a meeting of with obstacles and gradients produced oscilla- the Ratio Club, a London-based cybernetics din- tory signals that powered alternating pushing and ing club (Husbands and Holland, 2008), including

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grass cutting and house cleaning – robot applica- problematic if a robot is to be used in less struc- tions that have both come to pass. tured and predictable environments – for instance Walter’s robots became very famous, featuring in exploration applications. In such scenarios, in newsreels, television broadcasts and numerous the robot, usually a mobile free-roaming device, newspaper articles, and did much to generate must interact with its environment in a much more interest in robotics and artificial neural systems. intelligent and adaptive way in order to cope with They have been acknowledged as a major early a noisy, dynamic world. The control methods used influence by many leading robotics and neural for industrial arms are no longer sufficient. network researchers of later generations. The next phase in robot evolution, which at- The appearance of Walter’s (1950; 1951) Sci- tempted to address this issue by developing control entific American articles on his tortoises generated methods for autonomous systems, ran from the mid a global wave of interest in building artificial 1960s to the mid 1980s. It is typified by pioneering animals. Prominent examples include Angyan’s work at the Artificial Intelligence Center at SRI Machina reproducatrix, built in Hungary in International (then Stanford Research Institute) on 1955. This descendent of the tortoises used more a mobile robot system nicknamed ‘Shakey’. The sophisticated neural circuitry and, responding to robot had a vision system which gave it the ability visual and acoustic stimuli, demonstrated a range to perceive and model its environment in a limited of more complex conditioning and habituation way. Shakey could perform tasks that required learning behaviours (Angyan, 1959). planning, route-finding, and the rearrangement From these biologically inspired, and rather of simple objects. It became a paradigm case exploratory, beginnings, the focus of robotics for early AI driven robotics. The robot accepted began to turn to more immediately practical goals from the user, planned how to achieve them applications towards the end of the 1950s. The and then executed those plans (Nilsson 1984). main emphasis was on the development of robot The overall processing loop had at its heart the arms, particularly for use on production lines. sequence of operations shown in Figure 2. Here Hence developers of robot control systems robot intelligence is functionally decomposed into started to focus on methods based on solutions a strict pipeline of operations. Central to this view to equations describing the inverse-kinematics of intelligence is a single explicit internal model problem (finding the angle through which the of the world which must be extended, maintained joints should be rotated to achieve a specific po- and constantly referred to in order to decide what sition of the arm’s end effector). These solutions to do next. In Shakey’s case, as in much of AI at usually rely on precise knowledge of the robot’s the time, the world model was defined in terms mechanics and its environment. Industrial robots of formal logic. The robot was provided with an are programmed to repeat precise manipulations, initial set of axioms and then perceptual routines such as welding or paint spraying, over and over were used to build up and modify the world model again. Although they make use of sensors to help based on sensory information, particularly from control their movements, their behaviour cannot the robot’s vision system. be said to be in any way intelligent – if the car Even though Shakey and robots like it were on the production line is misaligned or is not the controlled by computers the size of a room, the expected shape, generally the robot cannot react demands of the sequential processing model they to the new set of circumstances, it can only repeat employed (Figure 2) were such that they could its pre-programmed movements. While these limi- not operate in real-time. They would often take tations are manageable in the highly controlled tens of minutes or even hours to complete a single environment of a production line, they become task such as navigating across a room avoiding

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Figure 2. Pipeline of functionally decomposed processing used in most classical AI robotics (after Brooks 1986)

obstacles. By the mid 1980s a number of leading the boundaries of the programmed-in knowledge researchers from the main AI robotics centres of the system, leading to poor performance in the were becoming more and more disillusioned with face of noise and uncertainty. This was largely a the approach. consequence of traditional AI ignoring work on Hans Moravec, an influential roboticist who learning and adaptation (Husbands et al., 2008). had carried out important work on the Stanford Hence biologically inspired areas, with their roots Cart, a project similar in spirit and approach to in cybernetics, such as neural networks, adaptive SRI’s Shakey and which ran at about the same systems, artificial evolution, behaviour-based time, summed up such feelings: robotics, artificial life, and the related field of probabilistic robotics, swept to the fore in the mid “For the last fifteen years I have worked with and 1980s and have remained dominant ever since around mobile robots controlled by large programs (Floreano and Mattiussi, 2008). that take in a certain amount of data and mull it Artificial neural networks became particularly over for seconds, minutes or even hours. Although popular in robotics because of a number of key their accomplishments are sometimes impressive, properties, listed below, that had potential to over- they are brittle - if any of many key steps do not come the weaknesses of traditional AI methods. work as planned, the entire process is likely to fail beyond recovery.” (Moravec 1987, p.1) • They could generalise and deal with in- complete data. Moravec goes on to point out how this is in • They could handle noisy data. strange contrast to the pioneering work of Grey • They could, at least in theory, adapt to new Walter and the projects that his tortoises inspired; circumstances. such early robots’ simple sensors were connected • By employing parallel distributed process- to their motors via fairly modest circuits and yet ing they offered a potentially more robust they were able to behave very competently and and efficient alternative to the sequential ‘managed to extricate themselves out of many pipeline model of traditional AI. very difficult and confusing situations’ without wasting inordinate amounts of time ‘thinking’. The closely related area of computational neu- The limitations of traditional AI had become roscience also came out of the shadows at about this evident in many other areas as well as in robotics. time. Work in this field, particular the modelling The major problem was brittleness – an inability of behaviour generating circuits in the nervous to deal with circumstances even slightly outside system, also became a rich seam of inspiration

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Figure 3. Left: Genghis, one of the robots produced by Brooks’ group at MIT to demonstrate the efficacy of the subsumption architecture. Right: schematic of the control network. Each box represents an AFSM, those without bands on top are repeated six times, those with solid bands are not repeated and represent ‘central’ control units, those with striped bands are repeated twice and are specific to particular legs. Reproduced with permission

to roboticists for similar reasons: animals can do This work also highlighted the fact that, given all the things traditional AI could not. its inherently noisy nature, robotics applications Key early successes from this era include vari- required carefully thought-out input and output ous applications of supervised learning techniques representations and training regimes. to develop neural network controllers for a variety A more biologically inspired, and highly influ- of robots, from industrial arms (Lewis et al., 1999) ential, example from the mid 1980s is Brook’s de- to Pomerleau’s famous autonomous van that drove velopment of the hexapod robot Ghengis (Brooks, around the campus of Carnegie Melon University 1989). The body and control system for the robot, in the late 1980s (Pomerleau, 1991). In the latter shown in Figure 3, were directly inspired by insect case a feedforward network was trained using a neuroethology. A network of 57 augmented finite backpropagation algorithm to drive the van. The state machines (AFSMs), including six repeated system, known as ALVINN, using input from a sub-networks (one for each leg), enabled the robot camera, was able to learn in under 5 minutes to to walk over rough terrain and follow a person autonomously control the vehicle by watching located by its infrared sensors. The networks can the reactions of a human driver. ALVINN was be thought of as a variety of hardwired dynamical successfully trained to drive in a variety of circum- neural network. They provided highly distributed stances including single-lane paved and unpaved efficient control and coordination of multiple roads, and multilane lined and unlined roads, at parallel behaviours. speeds of up to 20 miles per hour. Although the At the heart of Brooks’ approach was the idea system used a standard backpropagation scheme, of behavioural decomposition as opposed to tra- results were impressive – in no small part due to ditional functional decomposition. The overall the clever on-the-fly training scheme employed, control architecture involves the coordination of which involved ‘additional’ scenarios generated by several loosely coupled behaviour generating deliberately shifting the input images to simulate systems all acting in parallel. Each has access to poor driving. Such a scheme produced robust, sensors and actuators and can act as a standalone general controllers that were far superior to those control system, and usually involved tightly previously achieved using traditional methods. coupled sensorimotor feedback loops going

219 Artificial Neural Systems for Robots

through the environment. Each such layer was ing behaviour. Another notable early example of thought of as a level of competence, with the such work was Arkin’s behaviour-based robotics simpler competences at the bottom of a vertical (Arkin, 1998) which built on Arbib’s schema decomposition and the more complex ones at the theory, developed as a high-level top-down way of top. Brooks’ called his concrete implementation modelling brain function and architecture (Arbib of this idea the subsumption architecture (Brooks, 1972, Arbib et al., 1997). 1986), of which Ghengis’s control system is an The rest of this chapter will review a number example. Higher level layers are able to subsume of key examples of work from the bio-inspired the role of lower levels by taking over control of and bio-modelling robotics that originated in the their motor outputs. The architecture can be par- 1980s movement outlined above. titioned at any level with the layers below always forming a complete control system. The idea was to allow overall competence to be increased by APPLICATIONS OF ARTIFICIAL adding new behaviour generating layers without NEURAL SYSTEMS IN ROBOTICS having to change lower levels. Brooks’ team at MIT developed a wide variety of robots using There is now a large body of work in building these principles (Brooks, 1999), demonstrating neural control systems for robots that takes explicit an efficient and robust alternative to the mono- inspiration from biology. Examples range from lithic representation and reasoning hungry ap- complex motor coordination to visually guided proaches of traditional AI. They are the ancestors navigation in large scale environments. This sec- of the highly successful commercial robots, such tion highlights some important examples from this as the Roomba vacuum cleaner, produced by range but of course cannot cover the whole field. iRobot, the company Brooks and colleagues set For further reading in this area see for instance up to exploit their work. chapters 56-64 of Sciliano and Khatib (2008), As well as being an important example of Bekey (2005) and Ayers et al. (2002). neural-like control in robotics, the philosophy underlying Brooks work was crucial in pushing Locomotion biologically inspired robotics back to centre stage for the first time since the heyday of cybernetics. Locomotion in legged robots, which involves the Echoing the earlier sentiments of Grey Walter, he coordination of multiple actuators, is a non-trivial pointed out that the major part of natural intel- matter. Traditional methods, often employing fi- ligence is closely bound up with the generation nite state machine based central control systems of adaptive behaviour in the harsh unforgiving using parameterised rules governing the relative environments most animals inhabit. Hence he timing of individual leg movements (e.g. Laszlo proposed that just as real creatures should be the et al. 1996), usually result in rather unresponsive, focus of inspiration for autonomous robotics, the ‘clunky’ motion involving limited gait patterns. investigation of complete autonomous senso- Insects, with their wide range of gaits and robust rimotor systems – ‘artificial creatures’ – was the locomotion behaviours, generated despite rather most fruitful way forward in developing theories limited neural resources, have thus become an of intelligence. This triggered a movement, still important source of inspiration for roboticists strong today, involving collaborations between interested in walking machines. Brooks’ distrib- computer scientists, neuroscientists and roboti- uted dynamical network controller for Ghengis, cists in developing both robotic applications and described in the previous section, was an early embodied models of neural mechanisms underly- example of an insect- inspired walking controller.

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Figure 4. LEFT: schematic diagram of a distributed neural network for the control of locomotion as used by Beer et al. (1989). Excitatory connections are denoted by open triangles and inhibitory connections are denoted by filled circles. C, command neuron; P, pacemaker neuron; FT, foot motor neuron; FS and BS, forward swing and backward swing motor neurons; FAS and BAS, forward and backward angle sensors. Reproduced with permission. RIGHT: generalised architecture using a fully connected dynamical network controller for each leg (A), cross coupled as shown (B). Solid lines are cross body connections, dashed lines are intersegmental connections

Figure 4 illustrates another very influential strand characteristics of the legs themselves, endog- of work originating in the late 1980s: the neural enous and reflexive circuitry local to each leg, architecture introduced by Beer et al. (1989) to and additional circuitry that interconnects these control walking in a hexapod robot. local leg controllers. Rather than having a single This architecture was based on prior studies module directing the movements of the legs in a of the neural basis of locomotion in cockroaches master/slave fashion, in a distributed locomotion (Pearson, 1976) and generalisations and extensions controller stable gaits arise from the cooperative of it have been much used ever since. As with interactions between many different components.” Ghengis, a key lesson taken from insect locomo- (Beer et al., 1997 p.33) tion was that of distribution of control. As shown in Figure 4(left), each leg has a local “Rather than concentrating responsibility for control circuit consisting of a pacemaker neuron locomotion in one centralized system, animals (P), two sensory neurons that signal when the leg distribute this responsibility across the physical has reached an extreme forward (FAS) or backward

221 Artificial Neural Systems for Robots

(BAS) angle, and three motor neurons responsible between neural control and embodied physical for controlling the state of the foot (FT), and the processes can give rise to the overall behaviour rate with which the leg swings forward (FS) and (Pfeifer and Bongard, 2007). This architecture backward (BS). Each of these leg controllers re- was successfully used to generate locomotion in ceives identical excitation from a command neuron simulations and on a real robot (Beer et al., 1997). (C). The model neurons were all implemented as A generalised version of the architecture, as leaky integrators with a linear activation function. shown in Figure 4 (right), was also used to produce Each pacemaker neuron produced a rhythmical locomotion in both simulated and real hexapod bursting output whose frequency depended on its robots (Beer and Gallagher, 1992; Gallagher et al., overall level of excitation. In legged locomotion, 1996). This version of the architecture is interesting the movement of each leg can be divided into a because it uses an identical fully-connected recur- stance phase, in which the leg is providing sup- rent dynamical network for local control of each port and propelling the body, and swing phases, leg, dispensing with explicit pacemaker neurons in which the leg is lifted from the ground and by exploiting the rich dynamics of such networks. swinging through the air in order to begin another Each neuron was a continuous time leaky integra- stance phase. With this architecture the basic tor governed by the following equations. generation of leg movement works as follows. The foot motor neuron is active by default and dy N τi = − γ + ω σ()y+ I ()t excitation from the command neuron activates i dt i ∑ ji j i j=1 the backward swing motor neuron, producing a 1 σ()y = stance phase. Periodic bursts of activity from the j −()y +θ 1 + e j j pacemaker neuron, partly controlled by sensory feedback from leg angle sensors, inhibit the foot and backward swing motor neurons and excite Where y is the activation of a neuron (some- the forward swing motor neuron, thus producing times interpreted as representing the mean mem- a swing phase. The command neuron sets the brane potential), τ is a time constant, ω is a con- overall speed of locomotion by controlling both nection weight, I is an external input (e.g. from a the frequency of pacemaker bursts and the rate at sensor), θ is a neuron bias, and σ is of course the which the leg swings backward during the stance sigmoid function. Each leg was controlled by a phase. To produce locomotion the individual leg fully-connected network of five such neurons, each movements must be properly coordinated. This receiving a weighted sensory input from that leg’s was accomplished by two mechanisms. First, angle sensor as shown in Figure 4. The parameters mutually inhibitory connections between the of the symmetrically coupled architecture were pacemaker neurons of adjacent leg controllers set using an evolutionary robotics methodology prevent adjacent legs from stepping at the same (Nolfi and Floreano, 2000; Floreano et al., 2008) time. This mechanism was sufficient to produc- such that the connection weights and neuron time ing a stable tripod gait at relatively high walking constants and biases were under the control of a speeds. At lower speeds, a mechanism based on genetic algorithm. Networks were produced that slightly lowering the natural frequency of the rear successfully generated a range of efficient tripod legs, so that the rear pacemakers entrain the middle gaits for walking on flat surfaces. and front pacemakers in a metachronal sequence, The work of Beer and colleagues inspired a was also needed (Beer et al., 1997). This latter great deal of other evolutionary robotics research mechanism is a good example of how interplay on walking machines using similar networks and

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architectures. For instance, Jakobi (1998) evolved Ijspeert and colleagues have produced a num- modular controllers based on Beer’s recurrent ber of interesting robots based on studies of ani- network architecture to control an eight-legged mal locomotion. A good example is their impres- robot as it engaged in walking about its environ- sive salamander robot that combines three modes ment, avoiding obstacles and seeking out goals. of locomotion - swimming, serpentine crawling The robot could smoothly change gait, move and walking (Ijspeert et al. 2007). The controller backward and forward, and even turn on the spot. is based on central pattern generator (CPG) circuits Kodjabachian and Meyer (1998) extended Beer’s in the salamander spinal cord that underly loco- architecture by incrementally adding other behav- motion. It is implemented as a system of coupled iour generating networks, through an evolutionary nonlinear oscillators and runs on a microcontroller process, developing more sophisticated locomo- on-board the amphibious robot. tion behaviours that incorporated target seeking As these examples show, locomotion, involv- and obstacle avoidance. More recently, related ing complex motor coordination, is an area of approaches have been successfully used to evolve robotic control where neural systems excel. The controllers for more mechanically sophisticated distributed nature of such systems, coupled with robots such as the Sony Aibo (Tllez et al. 2006). the subtle, adaptive dynamics they can gener- In the last few years there has also been successful ate, give them many advantages over traditional work on evolving neural controllers for the highly control methods including efficiency, robustness unstable dynamic problem of bipedal walking and plasticity. The smooth dynamics generated (Reil and Husbands 2002, Vaughan et al. 2004). by the kinds of neural systems outlined result in Holk Cruse and colleagues have also made much more fluid, ‘natural’ gaits and motions than important contributions to biologically influenced with the traditional control systems mentioned at neural systems for robotic locomotion. Using the start of this section. All of the examples dis- detailed data from behavioural experiments on cussed also exploit mechanisms which highlight arthropods, particularly stick insects, they have a general lesson from animal locomotion studies: developed a series of kinematic models of hexapod the importance of subtle interactions between walking that have been used to control real and nervous system, body and environment in the simulated robots. Initially the models were built overall generation of behaviour. from state based algorithms for controlling and coordinating the various phases of leg motion. Navigation This approach was replaced by a distributed neural network model, WALKNET, that has been refined Efficient and robust navigation through complex over the years (Cruse et al., 1995; Durr et al., 2004; terrain using visual cues is an important capability Schilling et al., 2007). The overall architecture, for many animals as well as autonomous robots. which has commonalities with those of Beer and Insects demonstrate remarkable navigational abil- Brooks, is shown in Figure 5. Each leg controller ity despite small brains and low resolution vision contains a number of different subnetworks for which has made them a topic of special interest for controlling stance and swing phases. The various both engineers and biologists. A simple strategy subnets are usually implemented as small recur- used by insects and mimicked by roboticists is rent or feedforward networks of simple artificial view-based homing or snapshot guidance (Cart- neurons. The system is able to generate adaptive wright and Collett, 1983; Wehner et al., 1996). In gaits capable of dealing with irregular surfaces at these strategies, insects navigate back to a location a range of walking speeds, as well as being able to by remembering a retinotopic view of the world, cope with leg disruptions and even amputations. or snapshot, from the goal position. Subsequent

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Figure 5. Representation of the WALKNET architecture. A: Individual leg controllers are coupled to allow coordination. Global signals from a higher control level (dashed lines) model descending signals from the brain. B: each single-leg controller consists of a number of ANN modules. Their outputs set the angular velocities of the leg joints (TC,CT,FT). The selector net gates the stance and swing nets in a mutually exclusive manner. Half circles indicate sensory input. v, y and w are global commands controlling body velocity, yaw and forward walking respectively. The height net controls body clearance and the PEP net encodes the ‘posterior extreme position’ of leg movement. Sensory information is encoded into a target posture by the target net. Reproduced with permission

navigation is then achieved by moving so as to works by first identifying recognisable visual make the current view of the world more closely features, or landmarks, from a 360° panoramic match the remembered goal view. In general, view of the environment. The view of the world algorithmic instantiations of this process work by from that position is then represented as the aver- iteratively comparing a (parameterised) view of age of the unit vectors pointing from the agent to the world from the agent’s current position with a each landmark – the ALV, a two-dimensional view stored at the goal location, with differences vector. The algorithm works since the vector dif- between current and goal views transformed into ference between the current ALV and an ALV an approximate direction to the goal (for reviews stored at the goal position points approximately see: Franz and Mallot, 2000; Vardy and Möller, back to the goal (Lambrinos et al., 2000). Although 2005). this direction is approximate, using an iterative One of the simplest and most elegant imple- process with a sufficiently small step-size achieves mentations of snapshsot homing for robotic robust navigation provided the majority of land- navigation is the Average Landmark Vector (ALV) marks can be reliably identified in both current model (Lambrinos et al., 2000). The ALV model and goal views. In the simplest form of the algo-

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rithm, the heading vector at iteration i, Vi, is ANN model trained with either backpropagation calculated as Ai – G, where Ai is the current ALV or unsupervised learning. and G is the stored ALV taken at the goal location. Many variants of this class of view-based A useful refinement, which smoothes out noise homing algorithm exist (Möller and Vardy, 2006) by using a ‘momentum’ term, works as follows which can be implemented in terms of ANNs or (Smith et al., 2007). The heading direction at step as higher-level algorithms, or a mixture of both. i, θi, is calculated as a weighted average of the However, as with the ALV, successful navigation direction of Vi, ϕi, and the previous heading, θi-1, requires the current view to be similar to the goal with the weight, ωi, of the previous heading in- view (Cartwright & Collett, 1987) and so the region creasing with the value of |θi–1–ϕi|, as described in which they can function is local to the goal. by the following equations. This method prevents Another insect-inspired solution which in- large jumps in direction, ignoring ϕi altogether creases the scale over which visually guided robots π can navigate is route-following. It has long been when |θ− φ | ≥ . i−1 i 2 known that insects demonstrate robust navigation |θ− φ | using visually guided routes without recourse to θ= ω θ + ()1− ω φ, where ω = min( i−1 i ,)1 i i i−1 i i i 0. 5π map-like representations (e.g. Rosengren, 1971; Collett et al., 1992; Wehner et al., 2006) and navigation algorithms in autonomous robotics The ALV thus provides a sparse representa- have used this idea in various forms (Nehmzow, tion of the current visual scene by processing it 2000; Smith, et al., 2007, 2008; Argyros et al., into a single vector which essentially represents 2005; Vardy, 2006; Giovannangeli et al., 2006). In the ‘visual centre-of-mass’ of the surrounding such approaches, the agent learns routes consist- landmarks. Despite minimal computation and ing of multiple goal locations, or waypoints, with memory requirements, the ALV model has been navigation between waypoints achieved using a shown to be effective for visual navigation in both local homing method. The main problems to be computer simulation (Lambrinos et al., 2000) and solved in such approaches is to determine at which using autonomous mobile robots (Möller, 2000; point a waypoint should be set during route con- Hafner and Möller 2001). While the method ap- struction, and deciding when a waypoint has been pears fragile, the idea of encoding a visual scene reached during navigation. Successful schemes as the visual centre-of-mass of objects, or visual therefore require robust solutions to a number of features, or even simply pixel intensities, is surpris- general visual problems, such as place recognition ingly robust and is a general purpose method for and the detection of change (Smith et al., 2007). retinotopic encoding of a visual scene. Although In conjunction with environmental noise, these the standard ALV model requires object extraction, problems make robust route navigation a non- Möller (2001) presented a method which func- trivial aim; particularly as we do not yet properly tions on natural images while the work of Hafner understand how it is achieved by insects. While and Möller (2001) has shown that the model can robotic models have been successful in particular work quite reliably in the real world where every environments, some extra mechanism to reliably pixel of the image is considered a feature which determine where along the route an agent is, that is weighted by its intensity. The latter piece of is, a sense of place, is needed for more robust and work is of particular interest because the ALV general navigation (Smith et al., 2008). model was not pre-specified but was generated Equipping a robot with a sense of place, as the optimal solution in a simple feedforward however, allows for a more complex approach, specifically the use of a map-based strategy. Thus,

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while essentially the same as route-following in rodent brain (Milford et al., 2004). Specifically, that local homing was used between waypoints, it uses a three-dimensional continuous attractor Franz et al. (1998) linked waypoints together into network as a layer of ‘Pose cells’ which ape the a view graph rather than a route. This map-like function of grid and head direction cells by in- organisation allows more sophisticated navigation tegrating odometric information into a map-like behaviour, particularly with respect to dealing representation (Figure 6). Visual information with changes to the environment and recognizing feeds into local view cells which encode the visual when a wrong turn has been taken. scene as a vector, and feed into both the pose- Rats provide the most studied example of cells and the experience map, a third network of map-like navigation in nature. They are capable neurons analogous to place cells. The RatSLAM of navigation through complex changing worlds system navigates reliably on real robots operating over hundreds of metres with memories that per- continuously in both indoor and outdoor environ- sist for several years. This navigational ability is ments, producing impressively accurate maps over underpinned by hippocampal place cells which regions covering tens of kilometres (Milford and reliably fire when the animal is in a specific loca- Wyeth, 2008). tion in the world. Experiments have shown that While aspects of the RatSLAM system can rats use these cells for navigation and they have give insights into the workings of the brain, the therefore been thought of as a form of cognitive project’s main goal was robust navigation rather map. Interestingly, while visual cues (as well as than biological modelling. Similarly, the odometric and vestibular information) have been ‘Psikharpax’ project (Meyer et al., 2005) is an- shown to be necessary to generate reliable place other interesting example of robotics research cells, once established, the cells fire accurately making use of simulations of hippocampal place- even if the rat navigates in the dark. The more cell mechanisms for navigation, this time integrat- recent discovery of other cells which fire when ing learning (based on dopamine reinforcement the rat’s head is at certain angle (head-direction mechanisms). In contrast, see the later section on cells) (Taube et al, 1990a,1990b) or which fire Darwin X for an example of a biological model- periodically as the rat crosses an environment ling approach aimed at understanding how hip- (grid-cells) (Hafting et al., 2005) have led to a pocampal-cortical interactions could underlie deeper understanding of how the rat integrates spatial navigation. vestibular and odometric information to create and utilise the map. Visually Guided Behaviours Inspired by the organisation of spatial memories in the mammalian hippocampus, a number One area in which neuro-inspired robotics has of algorithms for the autonomous construction been particularly successful is in unmanned air of place graphs representing the structure of the vehicle (UAV) guidance, especially for mini- or environment have been developed (Mataric, 1992; micro- air vehicles. Franz et al., 1998). In the engineering sciences, This work has drawn on observations of the state-of-the-art robot navigation is dominated by unparalleled abilities of insects – often bees, probabilistic algorithms which also maintain a fruit-flies and dragonflies – to maintain stability sense of place while simultaneously mapping the and perform complex flight manoeuvres whilst environment (Thrun et al., 2005). An approach operating in complicated cluttered environments. which combines both these methodologies is Aping these abilities, engineers have developed RatSLAM, an autonomous navigation system algorithms capable of stabilising and controlling modelled on the organisation and structures of the flying robots, based on measurements of optic

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Figure 6. The RatSLAM architecture. Each local view cell is associated with a distinct visual scene in the environment and becomes active when the robot sees that scene. The pose cells comprise a 3-D continuous attractor network, where active pose cells encode the estimate of the robot’s pose. Each pose cell is connected to proximal cells by excitatory and inhibitory connections with wrapping across all six faces of the network. Active local view and pose cells drive the creation of experience nodes in the experience map, a semi-metric graphical representation of places in the environment and their interconnectivity. From Wyeth and Milford (2009), (© 2009 IEEE), used with permission

flow. The methodology often followed has been ated by a simple object at a distance d from an dubbed ‘Gibsonian robotics’, after the psycholo- observer moving with an instantaneous velocity gist J. J. Gibson, as the work starts with observation v can be calculated as: of a target behaviour and proceeds by trying to understand what variable(s) should be controlled v F = −ω+ sin θ to achieve such a behaviour. d This approach is exemplified by the work of Srinivasan and colleagues. They have used the where ω is the optic flow generated by self-rotation behaviour of honeybees as inspiration to develop and θ is the angle of the object relative to the controllers for a number of UAVs (Srinivasan, direction of travel. In the absence of self-rotation 2002). Many of these algorithms have come from (often assumed to be subtracted out through mea- observations of experiments in which bees were surement by gyroscopic sensors) it can be seen trained to fly through a tunnel with stripes on the that balancing the optic flow on each side of the walls (and occasionally, the floor). Experiments bee (i.e. at +/- θ) would mean that d would be in which one of the walls was moved showed that the same for both sides (as v is constant for both bees maintained a central position in the tunnel eyes). Similarly, maintaining constant optic flow by balancing the image motion, or optic flow, requires that if the tunnel narrows, the bee must perceived by each eye. The optic flow, F, gener- lower its speed. This means that in more cluttered

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environments, the bee automatically responds ap- Lobula Plate Tangential cells (LPTCs). These propriately. Thus this simple and elegant solution cells take input from across the retina and elec- of balancing optic flow results in a bee being able trophysiological studies indicate that they respond to chart a safe path through a cluttered environment preferentially to particular global patterns of optic without knowing its own speed or measuring the flow, such as an expanding flow-field caused by distance to nearby objects. A similar solution of pure forward-motion (Krapp and Hengstenberg, monitoring and matching optic flow is used by 1996; Borst and Haag, 2002). Humbert and Frye bees to control landing behaviour (Srinivisan et implement this system by an inner product between al., 2004). a flow-field pattern and the instantaneous flow Such simple fixed-point control is clearly at- field. They show that, using the appropriate pat- tractive to engineers and there have been multiple terns, global optic flow cues such as forward and instantiations of corridor–centering robots (e.g. vertical velocities and pitch rate can be extracted Srinivasan et al., 1999). In addition, the observa- and used to control a UAV (Humbert and Frye tion that bees use this type of control to maintain a 2006, Humbert at al., 2009). fixed height above a surface has led to the robust control of UAVs capable of ‘terrain following’ Active Tactile Sensing (Srinivasan et al., 2006). Notice that in the above equation, if the object is directly below an insect Although robots have made use of touch sensors flying parallel to the ground, the optic flow signal for decades, most have employed them as simple will be strong (θ = 90 degrees) and the distance is passive devices. Recently, however, there has been the height of the agent above the ground. A UAV growing interest in the use of touch as a principal can therefore maintain a constant height over and active sensory modality. Such technology undulating terrain by altering its forward velocity has the potential for use in environments where so that the optic flow measured from a down- vision and other modalities are severely limited, ward facing camera remains constant. This type such as underground, in the sea, or in rescue ap- of control has been implemented on free-flying plications in smoke-filled burning buildings. Most helicopters and fixed wing aircraft (Srinivasan, examples of such research take inspiration from 2006; Franceschini et al., 2007). rodent whisker sensor systems. Crucial to the success of these systems is the For instance, Scratchbot – developed in a col- extraction of reliable optic flow signals. In general, laboration between Sheffield University and the these rely on models of neural circuitry implement- Bristol Robotics Lab, is a biologically inspired ing insect elementary motion detectors, or EMDs robotic implementation of the rat whisker sensory (Reichardt, 1961). Many neural and neural-like system. It mimics the sweeping motion of rodent models of EMDs have been developed (Horn whiskers to determine the size and shape of objects and Schunck, 1981; Higgins 2004; Riabinina and in front of it. The system embodies neural control Philippides, 2009) and, particularly interesting algorithms capable of generating contact-mediated from a robotics perspective, they are often suitable adaptation of a whisking pattern displayed by for implementation in parallel hardware leading rats – active touch (Pearson et al., 2007). Sensory to extremely lightweight and fast sensor arrays information derived from the artificial whiskers (Aubepart and Franceschini, 2007). has also been used to enact orientation behaviour A different approach is that of Humbert and in the robot. This involved the integration of neural Frye (2006) who have achieved robust control models of the superior colliculus, basal ganglia by going a stage further down the visual pathway and the trigeminal sensory complex at different and modelling the wide-field integration of the levels of abstraction. Real-time performance was

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achieved by implementing the neural systems in take particular inspiration from nitric oxide (NO) a mix of hardware (FPGAs) and software. signalling (Gally et al., 1990). They comprise a An alternative approach to tactile perception fairly standard artificial neural network augmented via whiskers was explored by Seth et al. (2004b). by a chemical signalling system based on a diffus- In this study, a neurorobotic ‘brain-based device’ ing virtual gas which can modulate the response (Darwin IX) learned to discriminate among tex- of other neurons. A number of GasNet variants, tures by categorizing the spatiotemporal patterns inspired by different aspects of real nervous evoked by whisker deflections. Darwin IX was systems, have been explored in an evolutionary controlled by a simulated nervous system captur- robotics (Floreano et al., 2008) context as artificial ing detailed properties of the rodent somatosensory nervous systems for mobile autonomous robots. system, and its analysis made predictions about They were introduced to explore their potential as the existence of so-called ‘lag cells’ which would robust ‘minimal’ systems for controlling behav- respond to presynaptic input with varying delays. iour in very noisy environments. They have been The brain-based device approach is described in shown to be significantly more evolvable, in terms more detail in the later section on robotics tools of speed to a good solution, than other forms of in neuroscience. neural networks for a variety of robot tasks and behaviours (Husbands et al., 1998; Smith et al., GasNets 2003;McHale and Husbands, 2004; Philippides et al., 2005b; Vargas et al., 2008; Husbands et al., in This section concentrates on the use of a novel press). They are being investigated as potentially form of biologically inspired neural system in useful engineering tools, including as modules robot control. It describes experiments with robot in complex robot control systems (Vargas et al., nervous systems employing a style of artificial 2009), while a related strand of more detailed neural network strongly inspired by those parts modelling work is aimed at gaining helpful in- of contemporary neuroscience that emphasize sights into biological systems (Philippides et al., the complex electrochemical nature of biological 2000, 2003, 2005a). brains. In particular, they make use of an analogue By analogy with biological neuronal networks, of volume signalling, whereby neurotransmitters GasNets incorporate two distinct signalling freely diffuse into a relatively large volume around mechanisms, one ‘electrical’ and one ‘chemical’. a nerve cell, potentially affecting many other The underlying ‘electrical’ network is a discrete neurons irrespective of whether or not they are time step, recurrent neural network with a variable electrically connected (Gally et al., 1990; Wood number of nodes. These nodes are connected by and Garthwaite, 1994). This exotic form of neural either excitatory or inhibitory links with the out- signalling does not sit easily with classical con- t put, Oi , of node i at time step t determined by the nectionist (point-to-point) pictures of brain mecha- following equation. nisms and is forcing a radical re-think of existing theory (Dawson and Snyder, 1994; Philippides et     t t  t−1 t  al., 2000; Philippides et al., 2005a; Bullock et al., O= tanh  k  w O + I + b  i  i ∑ ji j i  i   j∈Γ  2005; Katz, 1999). These systems have been used   i   in a range of robot applications related to some of those described in the preceding sections. where Γi is the set of nodes with connections to The class of artificial neural networks devel- t node i and wji = ±1 is a connection weight. Ii is oped to explore artificial volume signalling are the external (sensory) input to node i at time t, known as GasNets (Husbands et al., 1998) which

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Figure 7. A basic GasNet showing excitatory and b is a genetically set bias. Each node has a i (solid) and inhibitory (dashed) ‘electrical’ con- genetically set default transfer function gain pa- nections and a diffusing virtual gas creating a rameter, k 0 , which can be altered at each time-step i ‘chemical’ gradient according to the concentration of diffusing ‘gas’ t at node i to give ki (as described later). In addition to this underlying network in which positive and negative ‘signals’ flow between units, an abstract process loosely analogous to the diffusion of gaseous modulators is at play. Some units can emit virtual ‘gases’ which diffuse and are capable of modulating the behaviour of other units by changing their transfer functions. The networks occupy a 2D space; the diffusion processes mean that the relative positioning of nodes is crucial to the functioning of the network. Spatially, the gas concentration varies as an inverse exponential of the distance from the emitting node with spread governed by a parameter, r, genetically set for each node, which governs the radius of influence 0.09 of the timescale of ‘electrical’ transmission of the virtual gas from the node as described by (i.e. a little slower to much slower). The total the equations below and illustrated in Figure 7. concentration at a node is then determined by The maximum concentration at the emitting node summing the contributions from all other emitting is 1.0 and the concentration builds up and decay nodes (nodes are not affected by their own emit- linearly as dictated by the time course function, ted gases to avoid runaway positive feedback). T(t), defined below. The diffusion process is modelled in this simple way to provide extreme computational efficiency,  −2d/ r e× T()t d < r allowing arbitrarily large networks to be run very C(, d t) =   0 else fast.  For mathematical convenience, in the original basic GasNet there are two ‘gases’, one whose   0 t = 0 modulatory effect is to increase the transfer func-  tion gain parameter ( k t ) and one whose effect is  1 i T() t = min(,1(( T t − 1)) + ) emitting to decrease it. It is genetically determined wheth-  s  1 er or not any given node will emit one of these max(,0(( T t − 1)) − ) not emmitting  s two gases (gas 1 and gas 2), and under what circumstances emission will occur (either when the ‘electrical’ activation of the node exceeds a thresh- where C(d,t) is the concentration at a distance d old, or the concentration of a genetically deter- from the emitting node at time t and s (controlling mined gas in the vicinity of the node exceeds a the slope of the function T) is genetically deter- threshold; note these emission processes provide mined for each node. The range of s is such that a coupling between the electrical and chemical the gas diffusion timescale can vary from 0.5 to mechanisms). The concentration-dependent modulation is described by the following equation,

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with transfer function parameters updated on processing (Husbands et al., 1998, Smith et al., every time step as the network runs: 2002). GasNets appeared to be particularly suited to noisy sensorimotor behaviours which could not t 0 t t be solved by simple reactive feedforward systems, ki = ki + αCC1− β 2 and to rhythmical behaviours. Two recent extensions of the basic GasNet, where k 0 is the genetically set default value for i the receptor and the plexus models, incorporated t t ki (the transfer function gain), C1 and C2 are the further influence from neuroscience (Philippides concentrations of gas 1 and gas 2 respectively at et al., 2005b). In the receptor model modulation of node i on time step t, and α and β are constants a node is now a function of gas concentration and t such that ki ∈[, −4 4] . Thus the gas does not alter the quantity and type of receptors (if any) at the the electrical activity in the network directly but node. This allows a range of site specific modu- rather acts by continuously changing the mapping lations within the same network. In the plexus between input and output for individual nodes, model, inspired by a type of NO signalling seen either directly or by stimulating the production in the mammalian cerebral cortex (Philippides et of further virtual gas. The general form of diffu- al, 2005a), the emitted gas ‘cloud’, which now has sion is based on the properties of a (real) single a flat concentration, is no longer centred on the source neuron as modelled in detail in Philippides node controlling it but at a distance from it. Both et al. (2000; 2003). The modulation chosen is these extended forms proved to be significantly motivated by what is known of NO modulatory more evolvable again than the basic GasNet. effects at synapses (Baranano et al., 2001). For Other varieties include non-spatial GasNets where further details see (Husbands et al., 1998; Philip- the diffusion process is replaced by explicit gas pides et al., 2005, Husbands et al., in press). connections with complex dynamics (Vargas et When they were first introduced, GasNets were al., 2008, 2009) and version with other forms of demonstrated to be significantly more evolvable modulation and diffusion (Husbands et al., in than a variety of standard ANNs on some noisy press). In order to gain insight into the enhanced visually guided evolutionary robotics tasks (Hus- evolvability of GasNets, detailed comparative bands, 1998; Husbands et al., 1998). Typically studies of these variants with each other, and with the increase in evolvability, in terms of number other forms of ANN, were performed using the of fitness evaluations to a reliable good solution, robot task illustrated in Figure 8 (Philippides et was an order of magnitude or more. The solu- al., 2005b;Husbands et al., in press). tions found were often very lean with few nodes Starting from an arbitrary position and orien- and connections, typically far fewer than were tation in a black-walled arena, a robot equipped needed for other forms of ANN (Husbands et al., with a forward facing camera must navigate under 1998). But the action of the modulatory gases extremely variable lighting conditions to one imbued such networks with intricate dynamics: shape (a white triangle) while ignoring the second they could not be described as simple. Oscillatory shape (a white rectangle). The robot must suc- sub-networks based on interacting ‘electrical’ and cessfully complete the task over a series of trials ‘gas’ feedback mechanisms acting on different in which the relative position and size of the shapes timescales were found to be very easy to evolve varies. Both the robot control network and the and cropped up in many forms, from CPG circuits robot sensor input morphology, i.e. the number for locomotion (McHale and Husbands, 2004) to and positions of the camera pixels used as input noise filters and timing mechanisms for visual and how they were connected into the network, were under evolutionary control as illustrated in

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Figure 8. LEFT: The gantry robot. A CCD camera head moves at the end of a gantry arm allowing full 3D movement. In the study referred to in the text 2D movement was used, equivalent to a wheeled robot with a fixed forward pointing camera. A validated simulation was used: controllers developed in the simulation work at least as well on the real robot. RIGHT: The simulated arena and robot. The bottom right view shows the robot position in the arena with the triangle and rectangle. Fitness is evaluated on how close the robot approaches the triangle. The top right view shows what the robot ‘sees’, along with the pixel positions selected by evolution for visual input. The bottom left view shows how the genetically set pixels are connected into the control network whose gas levels are illustrated. The top left view shows current activity of nodes in the GasNet

Figure 8. The network architecture (including well confer evolutionary advantages by allowing number of nodes) and all properties of the nodes nodes to be sensitive to different ranges of input and connections and gas diffusion parameters (internal and sensory) in different contexts. The were set by an evolutionary search algorithm. spatial embedding of the networks also appears Because of the noise and variation, and limited to play a role in producing the most effective sensory capabilities (only very few pixels are coupling between the two distinct signalling used), this task is challenging, requiring robust, processes (‘electrical’ and ‘chemical’). By exploit- general solutions. The gantry robot shown in the ing a loose, flexible coupling between the two figure was used. Evolution took place in a special processes, it is possible to significantly reduce validated simulation of the robot and its environ- destructive interference between them, allowing ment. one to be ‘tuned’ against the other while search- The comparative studies revealed that the rich ing for good solutions. It has been suggested that dynamics and additional timescales introduced similar forces may be at play in spiking networks, by the gas played an important part in enhanced where sub-threshold and spiking dynamics inter- evolvability, but were not the whole story (Philip- act with each other, which have been evolved to pides et al., 2005; Husbands et al., in press). The drive vision-based robot behaviours (Floreano particular form of modulation was also important and Mattiussi, 2006; Floreano et al., 2008). In the – multiplicative or exponential modulation (in the most successful varieties of GasNet, dynamics, form of changes to the transfer function) were modulation and spatial embedding act in concert found to be effective, but additive modulations to produce highly evolvable degenerate (Tononi were not. The former kind of modulations may et al., 1999) networks.

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ROBOTICS TOOLS IN earlier in the section on locomotion, also has im- NEUROSCIENCE portant biological modelling aspects. Their novel salamander-like robot, Salamandra robotica, is Natural adaptive and intelligent behaviour is the driven by a computational (neural) model of the result of complex interactions between nervous salamander’s spinal cord. This implementation system, body and environment. Biological neural allowed the team to address three fundamental systems are embodied and embedded. Because issues related to vertebrate locomotion: (i) the of this there has been a growing interest in us- modifications undergone by the spinal locomo- ing robots, employing on-board neural circuitry, tor circuits during the evolutionary transition to model aspects of animal behaviour. Such a from aquatic to terrestrial locomotion, (ii) the methodology, the argument goes, can give deeper mechanisms necessary for coordination of limb insights into behaviour generating neural mecha- and axial movements, and (iii) the mechanisms nisms than disembodied models (Webb, 2001), that underlie gait transitions induced by simple as well as fresh perspectives on what it means to electrical stimulation of the brain stem (Ijspeert be a cognitive agent (Wheeler, 2005). Like any et al., 2007). modelling enterprise, there are many issues sur- The concept of action selection is well known rounding how to make robotic models, with their in studies of animal behaviour and has also been inevitably different implementation constraints, used in robotic and computational models of properly relevant to biological enquiry. For a adaptive behaviour (Prescott et al., 1999, 2007). discussion of such matters see Webb (2001,2009) A selection problem arises whenever an animal and AB (2009). has two or more behavioural alternatives which A prominent example of such research is require the same mechanism for execution. It has Barbara Webb’s programme of work on robotic been proposed that the basal ganglia, a group of models of cricket behaviour. Webb and colleagues interconnected sub-cortical brain structures, play have used a series of robot models to investigate a crucial role in vertebrates in resolving such the phonotaxis behaviour of crickets whereby conflicts. An extended computational model of female crickets can recognise and locate the call- the basal ganglia, based on current neurophysi- ing songs of male crickets. The robots have an ological data, was developed and embedded in a analogue electronic auditory system to represent robot control architecture (Prescott et al., 2006). the physical auditory receptor mechanism of the The robot engaged in a variety of behaviours, cricket. The auditory input is translated into spike including food-seeking and wall-following, trains that are fed into a simulation of the neural which are analogous to rat behaviours. (Rats are circuitry under investigation, the output of which the main animal model for basal ganglia studies). is used to drive the wheels of the robot. Cricket These behaviours were successfully selected and neural systems are modelled using spiking leaky switched between by the embedded basal ganglia integrate-and-fire model neurons with suitable model and coherent sequences of behaviour were internal dynamics. Synaptic dynamics are also formed. It was claimed that this is the strongest modelled. The models have been used to test ex- evidence yet that the basal ganglia are directly isting hypotheses about behavioural mechanisms involved in action selection. as well as to propose new ones (Webb and Scutt, Recently techniques and ideas from probabilis- 2000; Reeve et al., 2005). tic robotics (Thrun et al., 2005) have been imported As well as being a good example of a neural into studies of insect behaviour in order to shed architecture for motor control, Ijspeert and col- new light on the behavioural strategies employed leagues’ work on the salamander robot, mentioned by visually navigating animals (Baddeley et al.,

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2009). The ultimate aim of such work is to help neurorobotic approaches (Edelman, 2007). For unravel the neural mechanisms underlying the example, unlike most other neurorobots, BBDs observed behaviour. An approach derived from typically involve simulated nervous systems probabilistic simultaneous localisation and map- having tens of thousands of simulated ‘neuronal ping (SLAM) was used to analyse bee orientation units’, where each unit reflects the mean spiking flights in order to investigate whether they are activity of a group (~1000) of biological neurons. structured to efficiently learn the positions of the Although BBDs can be analysed in terms landmarks that are available in a given terrain. It of Edelman’s ‘theory of neuronal group selec- was shown that the flights are not optimised to tion’ (TNGS, also known as ‘neural Darwinism’ learn about the position of a prominent landmark, (Edelman, 1993)), they should not be seen as but are more suited to learn about objects near implementations or models of this theory per se. the nest. Rather, and in common with other neurorobotic There is not enough space to review in detail approaches, BBDs play a dual role. They have each of the examples mentioned above; instead scientific value as heuristics for unravelling the remainder of this section will focus on the complex multi-level brain-body-environment pioneering and unique research associated with interactions, and they have technological value Edelman’s brain-based devices, which involve as examples of practically useful robotic devices. much larger numbers of simulated neurons than Here we focus on the scientific role of BBDs. Over all the work mentioned to this point. the last ten years, BBDs have been constructed to explore neural mechanisms underlying condi- Brain-Based Devices tioning to visual stimuli (Darwin VII,(Krichmar and Edelman, 2002)), visual binding (Darwin Brain-based devices (BBDs) are neurorobotic VIII, (Seth et al., 2004a)), texture discrimination devices whose development is most closely using artificial whiskers (Darwin IX,(Seth et al., associated with Edelman and colleagues at the 2004b)), spatial navigation and episodic memory Neurosciences Institute in San Diego. Edelman’s (Darwin X,(Krichmar et al., 2005a;Krichmar et ‘Darwin’ series of BBDs has an extensive history al., 2005b)), and predictive motor control (Darwin dating back to 1990 (Reeke et al., 1990) and con- XI,(McKinstry et al., 2006)). We will give brief tinuing to the present day (Fleischer et al., 2007; details of two of these BBDs, Darwin VIII and McKinstry et al., 2008). BBDs are constructed Darwin X. according to the methodology of ‘synthetic neural modelling’, which has four key components Darwin VIII (Krichmar and Edelman, 2005). First, a BBD needs to engage in a behavioural task. Second, Effective visual object recognition requires its behaviour must be controlled by a simulated mechanisms to bind object features (e.g., colour, nervous system having a design that reflects the shape, motion) while distinguishing distinct brain’s architecture and dynamics. Third, it needs objects. Darwin VIII was constructed to test the to be situated in the real world. And fourth, its idea that such a mechanism could be provided by behaviour and the activity of its simulated nervous synchronously active neuronal circuits among re- system must allow comparison with empirical data. entrantly connected cortical areas. Its simulated Of these components, the emphasis on fidelity to nervous system, schematized in Figure 9, has systems-level neuroanatomy and neurophysiol- two key features. First, visual areas are massively ogy, and on the comparison with empirical data, interconnected via re-entrant projections, where together provide the strongest contrast with other re-entry refers to parallel reciprocal connectivity

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Figure 9. A. Darwin VIII in its environment, together with ‘screen shots’ depicting activity in selected neural areas. Each pixel represents a single neuronal unit with brightness reflecting activity and colour indicating phase. B. Darwin VIII’s simulated nervous system, which contains 28 neural areas, 53,450 neuronal units, and ~1.7 million synaptic connections. Topographic visual areas are labelled V1-V4 (interareal connections are not shown); IT (inferotemporal) is a non-topographic visual area; S is the value system; A-left/right are auditory input areas, and the BBDs behaviour is driven by activity in area C. Unlike voltage independent connections, voltage dependent connections require above-threshold post-synaptic activity to have an effect. Plastic connections are modulated according to the Bienenstock- Cooper-Munro (BCM) rule; value-dependent plastic connections are additionally modulated by activity in area S

without connotations of ‘error signals’ (Edelman, after about 10 experimental ‘trials’, even in the 1993). Second, neuronal units in any given area absence of the tone (the conditioning stimulus). had a ‘phase’ value such that units at the same As shown in the figure, Darwin VIII’s nervous phase could be considered to be synchronized in system exhibited the dynamic formation of distinct their activity. synchronous neural circuits corresponding to Darwin VIII was placed in an environment objects in its visual field. In this example, area S containing visual objects with overlapping fea- is firing strongly in phase with the target object tures, namely red and green diamonds and squares. (the red diamond), reflecting a conditioned re- It was trained to approach one ‘target’ object (e.g., sponse. a red diamond), and to ignore potentially confus- A major feature of BBDs is that their neural ing distracters (red squares, green diamonds), by dynamics can be analyzed at all levels of de- association of the target stimulus with an audi- scription in arbitrary detail during behaviour, tory tone which would activate the value system unlike in corresponding animal experiments. In (area S), inducing plasticity in the nervous system the case of Darwin VIII, Seth et al. were able to (Figure 9). Darwin VIII was able to perform the track the phase distributions of all neuronal units task, showing discriminatory orienting behaviour in all neural areas both during normal behaviour

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and in a ‘lesion’ condition in which inter-areal to explore the environment. Darwin X learned to re-entrant connections were removed. A key navigate directly to the hidden platform after ~10 observation was that the dynamically formed trials. Moreover, in a ‘probe’ trial in which the object-specific synchronous circuits broke down hidden platform was removed, Darwin X spent in the lesion condition, leaving only locally (intra- substantial time in the part of the environment areal) synchronized responses to object features. where the platform would have been located. Discriminatory orienting behaviour also disap- These observations strongly resemble the learn- peared in this condition. Thus, intact re-entrant ing patterns exhibited by rodents in the original connections may be a key condition for successful Morris water maze studies. visual binding. The neurorobotic implementation Analysis of Darwin X’s nervous system re- of Darwin VIII also showed that such binding is vealed several interesting features. First, it was feasible in a real-world environment where objects found that units in area CA1 developed ‘place are constantly changing size and position. More fields’ with properties highly similar to those of generally, the operation of Darwin VIII could best ‘place cells’ identified in rodent brains: Each be understood in terms of interacting local and ‘place unit’ showed activity specific to a different global processes, as opposed to standard concepts part of the environment. Further analysis showed of ‘bottom up’ versus ‘top down’ information flow that place units could be categorised as either Finally, the implementation of a phase parameter ‘journey dependent’ (activity depends on where showed how ‘temporal coding’ and ‘rate coding’ the BBD has come from, or where it is going to) could be combined in a single network model in or ‘journey independent’, again in common with a synergetic way. rodent evidence (Fleischer et al., 2007). How- ever, the complexity of Darwin X’s nervous Darwin X system made it difficult to expose the neural pathways driving the activity of these place units. Darwin X was constructed to explore how hippo- To address this challenge, a new analysis based campal-cortical interactions could underlie spatial on ‘Granger causality’ (G-causality) was devel- navigation and episodic memory (Kritchmar et oped. The basic idea of G-causality is that one al., 2005a;Kritchmar et al., 2005b). Darwin X’s time series can be called ‘causal’ to another if it simulated nervous system contained 50 neural contains information that helps predict the other, areas, ~90,000 neuronal units and ~1.4 million where prediction is standardly operationalized simulated synapses (Figure 10). The simulated using vector autoregression (Kritchmar et al., areas included visual and head-direction input 2005a; Seth and Edelman, 2007). Application of streams, a detailed model of the two primary this analysis to Darwin X showed that, as learning pathways through the hippocampus (‘trisynaptic’ progressed, the proportion of CA1 units driven and ‘perforant’) and, as with Darwin VIII, a value by the ‘direct’ (perforant) pathway increased, system that modulated synaptic plasticity. Darwin while the proportion driven by the ‘indirect’ (tri- X was tested in an environment resembling the synaptic) pathway decreased. This observation ‘water maze’ experiments introduced by Mor- generated the interesting hypothesis that trisyn- ris (1984). A ‘hidden platform’ was placed in a aptic interactions may be more important in un- rectangular arena, detectable only when directly familiar environments due to their ability to inte- underneath the robot, and the arena was decorated grate more widely across different input with visual landmarks consisting of stripes of vari- modalities and across time. By contrast, the per- ous colours. In a given trial, Darwin X was placed forant pathway may be more efficient for trans- at one of four different starting points and allowed forming familiar cues into actions.

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Figure 10. Darwin X’s simulated nervous system, consisting of visual input streams (IT, Pr) and odometric (head-direction) input (ATN). These inputs were reciprocally connected with the hippocampus which included entorhinal cortical areas (ECin, ECout), dentate gyrus (DG), and the CA1 and CA3 hippocampal subfields. The size each area is indicated by the adjacent number. Area S is a value system initially activated only by the hidden platform

A second G-causality analysis of Darwin X other G-causal connections leading to the NR. developed the novel concept of a ‘causal core’, Causal cores therefore reflect the set of neuronal with potentially important implications for general interactions that are significant for a given output. neural theories of learning (Seth and Edelman, Remarkably, it turned out that causal cores were 2007). In this analysis, a first step was to identify much smaller as compared to context networks the ‘context’ network associated with a particular (mean sizes of 29 and 723 units respectively), CA1 unit active at a particular time (the ‘neural suggesting that even in large neural populations reference’, NR). This network consisted of all only comparatively small subsets may be caus- interactions that could have potentially contributed ally recruited at a given time for a given function. to the NR and was found by recursively identify- Even more interesting was the observation that ing pre-synaptically active neuronal units over causal cores for a given NR tended to diminish a few time-steps (Kritchmar et al., 2005a). The in size during learning, suggesting that synaptic corresponding causal core was then identified by plasticity underlies learning in Darwin X via preserving only those parts of the context network modulation of causal networks at the population that were (i) causally significant as assessed by level, and not by strengthening of weakening of G-causality, and (ii) resided within chains of

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associative links between neural representations approaches. Although great progress has been of objects and actions. made, and techniques similar to those discussed earlier are used in many of the ever-proliferating BBDs in Context mobile robots we now see in the home (e.g. autonomous vacuum cleaners and toys), or in areas Why are BBDs so complex? A simple response such as planetary exploration, security or military is that the BBD methodology reflects the un- applications, many challenges remain. derlying belief that the secrets of the brain will It is now possible to produce autonomous only be revealed by a detailed consideration of robots that behave in a robust and reliable way neuroanatomy. Brains are large in scale, complex in real environments, engaging in real tasks in in dynamics, and have multiple levels of control. real-time. However, the behaviours involved are According to the BBD approach, useful models still relatively simple. Progress has been slow need to reflect aspects of this multi-level complex- towards more sophisticated tasks such as learning ity. For example, the concept of the ‘causal core’ what to focus attention on in a complex environ- could not have been identified given a neuroro- ment, coordinating many conflicting goals and botic model with only tens of simulated neurons. drives, interacting with other robots in complex BBDs are also designed at a level of complexity group behaviours, learning to communicate in a that encourages direct comparison with empirical sophisticated way about the task at hand, and so data. For example, various statistics relating to on. Perhaps this should not be at all surprising. One place fields of units in CA1 of Darwin X invites lesson that most neuroscientists have understood direct comparison with rodent data, given that the for many decades, but which has often been over- ratios of the relevant neuroanatomical variables looked in AI, is that the generation of intelligent are comparable across both systems. embodied behaviour is an extremely complicated Because BBDs are complex they often require problem. However, progress is being made and the development of novel analysis methods. There there are many promising lines of research. It is is a potential virtuous circularity involved in this likely that directions involving artificial neural dialogue. The application of novel analyses to systems and other biologically inspired methods BBDs can expand theoretical and conceptual will become even more important as attempts to horizons, and can serve to integrate existing em- tackle these hard problems gather momentum. pirical data into broader frameworks. At the same One direction, briefly mentioned earlier, that is time, BBDs can serve as a ‘test bed’ anticipating likely to become increasingly important is the future biological data. For example, the rapid continued dismantling of the line between brain development of high-resolution neural imag- and body that has traditionally been present in ing via voltage-sensitive dyes is giving rise to studies of both natural and artificial intelligence. an avalanche of data which will require novel The topics highlighted in this chapter have, ap- population-based analysis methods. The causal propriately enough, been fairly mature areas of re- core analysis is an example of one such method. search. However, there are a number of potentially important emerging fields that may have a radical impact in the decades to come. These include PROSPECTS developments in interfacing digital electronics to neural tissue. The most frequent motivation for This chapter has concentrated on strands of autono- such work is to allow improved prosthetics to be mous robotics that employ neural systems, focus- directly controlled by the nervous system. This ing particularly on strongly biologically-inspired points to the possibility of an increased merging of

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robotic technology with human bodies –something may develop this suggestion by specifying neces- that a number of people have reflected on recently sary and sufficient neural requirements for such (e.g. Brooks 2002) and which the work of Stelarc, predictive modelling, and by analyzing the model the radical performance artist, has long explored. behavior for patterns that resemble the functional A related area involves attempting to harness the architecture of the corresponding phenomenology sophisticated non-linear adaptive properties of (in this case, interactions between egocentric and cultured (real) neural networks to create hybrid allocentric reference frames) (Seth, 2009b). Owen machines (DeMarse et al. 2001), pointing towards Holland’s ‘CRONOS’ neurorobot is in fact an the possibility of robots that include biological example of exactly this approach (Holland, 2007). matter in their control systems – a development The field of robotics has massively expanded that would echo the imagined landscapes of dozens since the days when it was dominated by cumber- of sci-fi books and movies. It is possible that in the some industrial arms; it is now quite possible that long-run that kind of approach may prove more in the not too distant future robots will become powerful than attempting to understand biological as widespread and as common as computers are systems in sufficient detail to be able to abstract now. If such a technological revolution comes to general mechanisms underlying the generation pass, it is highly likely that artificial neural systems of intelligent behaviour. However, such research will play an important part as there will be greater is at an extremely early stage, so we cannot yet demands for robust, reliable adaptation and learn- properly assess its potential. ing, as well as sophisticated pattern recognition Another possible long-range goal for neuroro- and sensory processing – all areas in which neural botics is the construction of a conscious artifact. systems have great potential. Justification for this point of view – which can be called ‘strong artificial consciousness’ – de- rives from the idea that biological consciousness REFERENCES comes from (complex) brain-body-environment interactions, and that there is no reason in prin- A.B. (2009)... Adaptive Behavior, 17(4). ciple why such interactions cannot be instantiated Angyan, A. J. (1959). Machina Reproducatrix. In in synthetically constructed systems. However, D. V. Blake and A. M. Uttley (Eds.), The Mecha- there are as yet no credible instances of strong nization of Thought Processes: Proceedings of a artificial consciousness, nor any on the horizon Symposium held at the National Physical Labora- (Seth, 2009a). A more modest goal is described tory on 24-27 November 1958, (vol. 2, pp. 933- by ‘weak artificial consciousness’ according to 944). London: Her Majesty’s Stationery Office. which neurorobotic devices are treated as models of their target phenomenon, in much the same Arbib, M. A. (1972). The Metaphorical Brain: way that a computational model of a hurricane An Introduction to Cybernetics as Artificial In- can shed light on meteorology without itself telligence and Brain Theory. New York: Wiley- being windy. According to the weak approach, Interscience. neurorobots can be used to derive and explore the Arbib, M. A., Érdi, P., & Szentágothai, J. (1997). consequences of hypotheses linking phenomenal Neural Organization: Structure, Function, and aspects of conscious experience to underlying Dynamics. Cambridge, MA: MIT Press. mechanisms. For example, it has been suggested that a subjective first-person perspective could be engendered by predictive forward modelling of sensorimotor interactions. Neurorobotic models

239 Artificial Neural Systems for Robots

Argyros, A., Bekris, K., Orphanoudakis, S., & Boden, M. (2006). Mind as Machine: A History Kavraki, L. (2005). Robot homing by exploiting of Cognitive Science. Oxford, UK: OUP. panoramic vision. Autonomous Robots, 19, 7–25. Borst, A., & Haag, J. (2002). Neural networks doi:10.1007/s10514-005-0603-7 in the cockpit of the fly. Journal of Comparative Arkin, R. (1998). Behavior-based robotics. Cam- Physiology. A, Neuroethology, Sensory, Neu- bridge, MA: MIT Press. ral, and Behavioral Physiology, 188, 419–437. doi:10.1007/s00359-002-0316-8 Aubepart, F., & Franceschini, N. (2007). Bio- inspired optic flow sensors based on FPGA: ap- Brooks, R. A. (1986). A Robust Layered Control plication to micro-air-vehicles. Microprocessors System for a Mobile Robot. IEEE Journal on and Microsystems, 31, 408–419. doi:10.1016/j. Robotics and Automation, 2(1), 14–23. micpro.2007.02.004 Brooks, R. A. (1989). A Robot that Walks; Emer- Ayers, J., Davis, J., & Rudolph, A. (2002). Neu- gent Behaviors from a Carefully Evolved Network. rotechnology for Biomimetic Robots. Cambridge, Neural Computation, 1(2), 253–262. doi:10.1162/ MA: MIT Press. neco.1989.1.2.253 Baddeley, B., Graham, P., Philippides, A., Hempel Brooks, R. A. (1999). Cambrian Intelligence: de Ibarra, N., Collett, T., & Husbands, P. (2009). The Early History of the New AI. Cambridge, What can be learnt from analysing insect orienta- MA: MIT Press. tion flights using probabilistic SLAM? Biologi- Brooks, R. A. (2002). Flesh and Machines: How cal Cybernetics, 101(3), 169–182. doi:10.1007/ Robots Will Change Us. New York: Pantheon s00422-009-0327-4 Books. Baranano, D., Ferris, C., & Snyder, S. (2001). Bullock, T., Bennett, M., Johnston, D., Joseph- Atypical neural messengers. Trends in Neuro- son, R., Marder, E., & Fields, R. (2005). The sciences, 24(2), 99–106. doi:10.1016/S0166- neuron doctrine, redux. Science, 310(5749), 791. 2236(00)01716-1 doi:10.1126/science.1114394 Beer, R. D., & Gallagher, J. C. (1992). Evolv- Cartwright, B., & Collett, T. (1983). Landmark ing dynamical neural networks for adaptive learning in bees. Journal of Comparative Physiol- behavior. Adaptive Behavior, 1, 94–110. ogy. A, Neuroethology, Sensory, Neural, and Be- doi:10.1177/105971239200100105 havioral Physiology, 151, 521–543. doi:10.1007/ Beer, R. D., Quinn, R. D., Chiel, H. J., & Ritzmann, BF00605469 R. E. (1997). Biologically-inspired approaches Cartwright, B., & Collett, T. (1987). Landmark to robotics. Communications of the ACM, 40(3), maps for honeybees. Biological Cybernetics, 30–38. doi:10.1145/245108.245118 57(1-2), 85–93. doi:10.1007/BF00318718 Bekey, G. (2005). Autonomous Robots: From Collett, T., Dillmann, E., Giger, A., & Wehner, R. Biological Inspiration to Implementation and (1992). Visual landmarks and route following in Control. Cambridge, MA: MIT Press. desert ants. Journal of Comparative Physiology. Bekey, G., & Goldberg, K. (Eds.). (1993). Neural A, Neuroethology, Sensory, Neural, and Behav- Networks in Robotics, (Springer International ioral Physiology, 170, 435–442. doi:10.1007/ Series in Engineering and Computer Science, Vol. BF00191460 202). Berlin: Springer.

240 Artificial Neural Systems for Robots

Cordeschi, R. (2002). The Discovery of the Ar- Floreano, D., Husbands, P., & Nolfi, S. (2008). tificial: Behavior, Mind and Machines Before Evolutionary Robotics. In Siciliano, B., & Khatib, and Beyond Cybernetics. Dordrecht: Kluwer O. (Eds.), Springer Handbook of Robotics (pp. Academic Publishers. 1423–1451). Berlin: Springer. doi:10.1007/978- 3-540-30301-5_62 Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., & Dreifert, M. (1995). A modular artificial Floreano, D., & Mattiussi, C. (2001). Evolution neural net for controlling a six-legged walking of Spiking Neural Controllers for Autonomous system. Biological Cybernetics, 72(5), 421–430. Vision-Based Robots. In Gomi, T. (Ed.), Evolu- doi:10.1007/BF00201417 tionary Robotic: From Intelligent Robotics to Arti- ficial Life. Tokyo: Springer Verlag. doi:10.1007/3- Dawson, T., & Snyder, S. (1994). Gases as biologi- 540-45502-7_2 cal messengers: nitric oxide and carbon monoxide in the brain. The Journal of Neuroscience, 14(9), Floreano, D., & Mattiussi, C. (2008). Bio-Inspired 5147–5159. Artificial Intelligence. Cambridge, MA: MIT Press. DeMarse, T., Wagenaar, D., Blau, A., & Potter, S. (2001). The Neurally Controlled Franceschini, N., Ruffier, F., & Serres, J. (2007). Animat: Biological Brains Acting with Simu- A bio-inspired flying robot sheds light on insect lated Bodies. Autonomous Robots, 11, 305–310. piloting abilities. Current Biology, 17, 329–335. doi:10.1023/A:1012407611130 doi:10.1016/j.cub.2006.12.032 Durr, V., Schmitz, J., & Cruse, H. (2004). Franz, M., & Mallot, H. (2000). Biomimetic robot Behaviour-based modelling of hexapod locomo- navigation. Robotics and Autonomous Systems, 30, tion: linking biology and technical application. 133–153. doi:10.1016/S0921-8890(99)00069-X Arthropod Structure & Development, 33, 237–250. Franz, M., Schölkopf, B., Mallot, H., & Bülthoff, doi:10.1016/j.asd.2004.05.004 H. (1998). Learning View Graphs for Robot Edelman, G. M. (1993). Neural Darwinism: selec- Navigation. Autonomous Robots, 5, 111–125. tion and reentrant signaling in higher brain func- doi:10.1023/A:1008821210922 tion. Neuron, 10(2), 115–125. doi:10.1016/0896- Gallagher, J., Beer, R., Espenschiel, M., & Quinn, 6273(93)90304-A R. (1996). Application of evolved locomotion Edelman, G. M. (2007). Learning in and from controllers to a hexapod robot. Robotics and Au- brain-based devices. Science, 318(5853), 1103– tonomous Systems, 19(1), 95–103. doi:10.1016/ 1105. doi:10.1126/science.1148677 S0921-8890(96)00036-X Fleischer, J. G. (2007). Retrospective and prospec- Gally, J., Montague, P., Reeke, G., & Edelman, tive responses arising in a modeled hippocampus G. (1990). The NO hypothesis: possible effects during maze navigation by a brain-based device. of a short-lived, rapidly diffusible signal in the Proceedings of the National Academy of Sci- development and function of the nervous system. ences of the United States of America, 104(9), Proceedings of the National Academy of Sciences 3556–3561. doi:10.1073/pnas.0611571104 of the United States of America, 87, 3547–3551. doi:10.1073/pnas.87.9.3547

241 Artificial Neural Systems for Robots

Giovannangeli, C., Gaussier, P., & Désilles, G. Husbands, P., & Holland, O. (2008). The Ratio (2006). Robust Mapless Outdoor Vision-Based Club: A Hub of British Cybernetics. In P. Hus- Navigation. In Intelligent Robots and Systems, bands, O. Holland & M. Wheeler, 2008, (pp. 2006 IEEE/RSJ International Conference, (pp. 91-148). 3293-3300). Husbands, P., Holland, O., & Wheeler, M. (Eds.). Hafner, V., & Moller, R. (2001). Learning of Visual (2008). The Mechanical Mind in History. Cam- Navigation Strategies. In Quoy, M. & Gaussier, P. bridge, MA: MIT Press. & Wyatt, J. (Eds.), Proceedings of the European Husbands, P., Philippides, A., Vargas, P., Buckley, Workshop on Learning Robots, 1, (pp. 47-56). C., Fine, P., Di Paolo, E., & O’Shea, M. (in press). Hafting, T., Fyhn, M., Molden, S., Moser, M. Spatial, temporal and modulatory factors affecting B., & Moser, E. I. (2005). Microstructure of a GasNet evolvability in a visually guided robotics spatial map in the entorhinal cortex. [REMOVED task. Complexity. HYPERLINK FIELD]. Nature, 436, 801–806. Husbands, P., Smith, T., Jakobi, N., & O’Shea, doi:10.1038/nature03721 M. (1998). Better Living through Chem- Higgins, C. (2004). Nondirectional motion may istry: Evolving GasNets for Robot Con- underlie insect behavioral dependence on image trol. Connection Science, 10(3&4), 185–210. speed. Biological Cybernetics, 91, 326–332. doi:10.1080/095400998116404 doi:10.1007/s00422-004-0519-x Ijspeert, A., Crespi, A., Ryczko, D., & Cabelguen, Holland, O. (2003). Exploration and High Adven- J.-M. (2007). From Swimming to Walking with a ture: The Legacy of Grey Walter. Philosophical Salamander Robot Driven by a Spinal Cord Model. Transactions of the Royal Society of London. Science, 315(5817), 1416–1420. doi:10.1126/ Series A: Mathematical and Physical Sciences, science.1138353 361, 2085–2121. doi:10.1098/rsta.2003.1260 Jakobi, N. (1998). Running across the reality Holland, O. (2007). Strongly embodied approach gap: Octopod locomotion evolved in a minimal to machine consciousness. Journal of Conscious- simulation. In P. Husbands & J.-A. Meyer, (Eds), ness Studies, 14(7), 97–110. Evolutionary Robotics: First European Workshop, EvoRobot98 (pp. 39–58). Berlin: Springer. Horáková, J., & Kelemen, J. (2008). The Robot Story: Why Robots Were Born and How They Katz, P. (Ed.). (1999). Beyond Neurotransmis- Grew Up. In Husbands, P., Holland, O., & Wheeler, sion: Neuromodulation and its Importance for M. (Eds.), The Mechanical Mind in History (pp. Information Processing. Oxford, UK: Oxford 283–306). Cambridge, MA: MIT Press. University Press. Horn, B., & Schunck, B. (1981). Determining Kodjabachian, J., & Meyer, J.-A. (1998). Evo- optical flow. Artificial Intelligence, 17, 185–203. lution and development of neural networks doi:10.1016/0004-3702(81)90024-2 controlling locomotion, gradient following and obstacle avoidance in artificial insects. IEEE Humbert, J., Conroy, J., Neely, C., & Barrows, Transactions on Neural Networks, 9, 796–812. G. (2009). Wide-Field Integration Methods for doi:10.1109/72.712153 Visuomotor Control. In Floreano, D., & Zufferey, J.-C. (Eds.), Flying Insects and Robots (pp. 63–72). Berlin: Springer Verlag. doi:10.1007/978-3-540- 89393-6_5

242 Artificial Neural Systems for Robots

Kohler, M., & Wehner, R. (2005). Idiosyncratic Lehrer, M., & Bianco, G. (2000). The turn-back- route-based memories in desert ants, Melophorus and-look behaviour: bee versus robot. Biologi- bagoti: How do they interact with path-integration cal Cybernetics, 83(3), 211–229. doi:10.1007/ vectors? Neurobiology of Learning and Memory, s004220000165 83, 1–12. doi:10.1016/j.nlm.2004.05.011 Lewis, F., Jagannathan, S., & Yesildirek, A. (1999). Krapp, H., & Hengstenberg, R. (1996). Estima- Neural Network Control of Robot manipulators tion of self-motion by optic flow processing in a and Nonlinear Systems. London: Taylor & Francis. single visual interneuron. Nature, 384, 463–466. Matarić, M. (1992). Integration of Representation doi:10.1038/384463a0 Into Goal-Driven Behavior-Based Robots. IEEE Krichmar, J. L., & Edelman, G. M. (2002). Transactions on Robotics and Automation, 8(3), Machine psychology: autonomous behavior, 304–312. doi:10.1109/70.143349 perceptual categorization and conditioning in a McHale, G., & Husbands, P. (2004). GasNets brain-based device. Cereb Cortex, 12(8), 818–830. and other evovalble neural networks applied to doi:10.1093/cercor/12.8.818 bipedal locomotion. In S. Schaal et al. (Eds), Krichmar, J. L., & Edelman, G. M. (2005). From Animals to Animats 8: Proceedings of the Brain-based devices for the study of nervous Eigth International Conference on Simulation of systems and the development of intelligent Adaptive Behaviour (SAB’2004) (pp. 163-172). machines. Artificial Life, 11(1-2), 63–77. Cambridge, MA: MIT Press. doi:10.1162/1064546053278946 McKinstry, J. L. (2008). Embodied models of Krichmar, J. L., Nitz, D. A., Gally, J. A., & Edel- delayed neural responses: spatiotemporal cat- man, G. M. (2005a). Characterizing functional egorization and predictive motor control in brain hippocampal pathways in a brain-based device as based devices. Neural Networks, 21(4), 553–561. it solves a spatial memory task. Proceedings of the doi:10.1016/j.neunet.2008.01.004 National Academy of Sciences of the United States McKinstry, J. L., Edelman, G. M., & Krichmar, J. of America, 102(6), 2111–2116. doi:10.1073/ L. (2006). A cerebellar model for predictive motor pnas.0409792102 control tested in a brain-based device. Proceed- Krichmar, J. L., Seth, A. K., Nitz, D. A., Fleischer, ings of the National Academy of Sciences of the J. G., & Edelman, G. M. (2005b). Spatial naviga- United States of America, 103(9), 3387–3392. tion and causal analysis in a brain-based device doi:10.1073/pnas.0511281103 modeling cortical-hippocampal interactions. Meyer, J. A., Guillot, A., Girard, B., Khamassi, M., Neuroinformatics, 3(3), 197–222. doi:10.1385/ Pirim, P., & Berthoz, A. (2005). The Psikharpax NI:3:3:197 project: Towards building an artificial rat. Robot- Lambrinos, D., Möller, R., Pfeifer, R., Wehner, R., ics and Autonomous Systems, 50(4), 211–223. & Labhart, T. (2000). A Mobile Robot Employing doi:10.1016/j.robot.2004.09.018 Insect Strategies for Navigation. Robotics and Milford, M., & Wyeth, G. (2008). Mapping a Autonomous Systems, 30, 39–64. doi:10.1016/ Suburb with a Single Camera using a Biologi- S0921-8890(99)00064-0 cally Inspired SLAM System. IEEE Transactions on Robotics, 24(5), 1038–1053. doi:10.1109/ TRO.2008.2004520

243 Artificial Neural Systems for Robots

Milford, M., Wyeth, G., & Prasser, D. (2004). Rat- Pearson, M., Pipe, A., Melhuish, C., Mitchinson, SLAM: A Hippocampal Model for Simultaneous B., & Prescott, T. (2007). Whiskerbot: A Robotic Localization and Mapping. Proc. IEEE Interna- Active Touch System Modeled on the Rat Whis- tional Conference on Robotics and Automation ker Sensory System. Adaptive Behavior, 15(3), (pp. 403-408). Pasadena, CA: IEEE Press. 223–240. doi:10.1177/1059712307082089 Möller, R. (2000). Modelling the Landmark Navi- Pfeifer, R., & Bongard, J. (2007). How the body gation Behavior of the Desert Ant Cataglyphis. shapes the way we think: a new view of intelli- Technical Report IFI-AI-00.24, Artificial Intel- gence. Cambridge, MA: MIT Press. ligence Lab, Dept. Computer Science, University Philippides, A., Husbands, P., & O’Shea, M. of Zurich. Zurich. (2000). Four Dimensional Neuronal Signaling Möller, R. (2001). Do Insects Use Templates or by Nitric Oxide: A Computational Analysis. The Parameters for Landmark Navigation. Journal of Journal of Neuroscience, 20(3), 1199–1207. Theoretical Biology, 210(1), 33–45. doi:10.1006/ Philippides, A., Husbands, P., Smith, T., & O’Shea, jtbi.2001.2295 M. (2003). Structure based models of NO diffusion Möller, R., & Vardy, A. (2006). Local visual in the nervous system. In Feng, J. (Ed.), Computa- homing by matched-filter descent in image dis- tional Neuroscience: a comprehensive approach tances. Biological Cybernetics, 95(5), 413–430. (pp. 97–130). London: Chapman and Hall/CRC doi:10.1007/s00422-006-0095-3 Press. doi:10.1201/9780203494462.ch4 Morris, R. (1984). Developments of a water-maze Philippides, A., Husbands, P., Smith, T., & procedure for studying spatial learning in the rat. O’Shea, M. (2005b). Flexible couplings: Journal of Neuroscience Methods, 11(1), 47–60. diffusing neuromodulators and adaptive ro- doi:10.1016/0165-0270(84)90007-4 botics. Artificial Life, 11(1&2), 139–160. doi:10.1162/1064546053279044 Nehmzow, U., & Owen, C. (2000). Robot Naviga- tion in the Real World: Experiments with Man- Philippides, A., Ott, S., Husbands, P., Lovick, T., chester’s FortyTwo in Unmodified, Large Envi- & O’Shea, M. (2005a). Modeling co-operative ronments. Robotics and Autonomous Systems, 33, volume signaling in a plexus of nitric oxide 223–242. doi:10.1016/S0921-8890(00)00098-1 synthase-expressing neurons. The Journal of Neuroscience, 25(28), 6520–6532. doi:10.1523/ Nilsson, N. J. (Ed.). (1984). Shakey The Robot, JNEUROSCI.1264-05.2005 Technical Note 323. AI Center, SRI International, Menlo Park CA., Nolfi, S., & Floreano, D. (2000) Pomerleau, D. (1991). Efficient Training of Arti- Evolutionary Robotics: The Biology, Intelligence, ficial Neural Networks for Autonomous Naviga- and Technology of Self-Organizing Machines. tion. Neural Computation, 3, 88–97. doi:10.1162/ Cambridge, MA: MIT Press. neco.1991.3.1.88 Pearson, K. G. (1976). The control of walking. Prescott, T. J., Bryson, J. J., & Seth, A. K. (2007). Scientific American, 235, 72–86. doi:10.1038/ Modelling natural action selection: Introduction scientificamerican1276-72 to the theme issue. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1485), 1521–1529. doi:10.1098/ rstb.2007.2050

244 Artificial Neural Systems for Robots

Prescott, T. J., Montes-Gonzalez, F., Gurney, Schilling, M., Cruse, H., & Arena, P. (2007). K., Humphries, M. D., & Redgrave, P. (2006). A Hexapod Walking: an expansion to Walknet deal- robot model of the basal ganglia: Behavior and ing with leg amputations and force oscillations. intrinsic processing. Neural Networks, 19, 31–61. Biological Cybernetics, 96, 323–340. doi:10.1007/ doi:10.1016/j.neunet.2005.06.049 s00422-006-0117-1 Prescott, T. J., Redgrave, P., & Gurney, K. N. Seth, A. K. (2009a). The strength of weak artifi- (1999). Layered control architectures in robots cial consciousness. International Journal of Ma- and vertebrates. Adaptive Behavior, 7, 99–127. chine Consciousness, 1(1), 71–82. doi:10.1142/ doi:10.1177/105971239900700105 S1793843009000086 Reeke, G. N., Sporns, O., & Edelman, G. M. Seth, A. K. (2009b). Explanatory correlates of (1990). Synthetic neural modeling: The “Darwin” consciousness: Theoretical and computational series of recognition automata. Proceedings of the challenges. Cognitive Computation, 1(1), 50–63. IEEE, 78(9), 1498–1530. doi:10.1109/5.58327 doi:10.1007/s12559-009-9007-x Reeve, R., Webb, B., Indiveri, G., Horchler, A., & Seth, A. K., & Edelman, G. M. (2007). Distinguish- Quinn, R. (2005). New technologies for testing a ing causal interactions in neural populations. Neu- model of cricket phonotaxis on an outdoor robot. ral Computation, 19(4), 910–933. doi:10.1162/ Robotics and Autonomous Systems, 51(1), 41–54. neco.2007.19.4.910 doi:10.1016/j.robot.2004.08.010 Seth, A. K., McKinstry, J. L., Edelman, G. M., & Reichardt, W. (1961). Autocorrelation, a prin- Krichmar, J. L. (2004a). Visual binding through ciple for the evaluation of sensory informationby reentrant connectivity and dynamic synchroniza- the central nervous system. In Rosenblith, W. tion in a brain-based device. Cerebral Cortex, (Ed.), Principles of sensory communication (pp. 14(11), 1185–1199. doi:10.1093/cercor/bhh079 303–317). New York: Wiley. Seth, A. K., McKinstry, J. L., Edelman, G. M., Reil, T., & Husbands, P. (2002). Evolution of & Krichmar, J. L. (2004b). Active sensing of central pattern generators for bipedal walking in visual and tactile stimuli by brain-based de- real-time physics environments. IEEE Transac- vices. International Journal of Robotics and tions on Evolutionary Computation, 6(2), 10–21. Automation, 19(4), 222–238. doi:10.2316/Jour- doi:10.1109/4235.996015 nal.206.2004.4.206-2802 Riabinina, O., & Philippides, A. (2009). A model Siciliano, B., & Khatib, O. (Eds.). (2008). of visual detection of angular speed for bees. Springer Handbook of Robotics. Berlin: Springer. Journal of Theoretical Biology, 257(1), 61–72. doi:10.1007/978-3-540-30301-5 doi:10.1016/j.jtbi.2008.11.002 Smith, L., Philippides, A., Graham, P., Baddeley, Rosengren, R. (1971). Route Fidelity, Visual B., & Husbands, P. (2007). Linked local naviga- Memory and Recruitment Behaviour in Foraging tion for visual route guidance. Adaptive Behavior, Wood Ants of the Genus Formica (Hymenopteran 15(3), 257–271. doi:10.1177/1059712307082091 Formicidae). Acta Zoologica Fennica, 133, 1–150.

245 Artificial Neural Systems for Robots

Smith, L., Philippides, A., Graham, P., & Hus- Srinivasan, M. V. (2002). Visual Flight Control bands, P. (2008). Linked Local Visual Naviga- and Navigation in Honeybees, and Applications tion and Robustness to Motor Noise and Route to Robotics. In Ayers, J., Davis, J. L., & Rudolph, Displacement. In M.Asada et al. (Eds.), Proc. A. (Eds.), Neurotechnology for Biomimetic Robots SAB’08: Animals to Animats X (LNCS 5040, pp. (pp. 593–610). Cambridge, MA: MIT Press. 179-188). Berlin: Springer. Taube, J. S., Muller, R. U., & Ranck, J. B. (1990a). Smith, T. M. C., Husbands, P., & O’Shea, M. Head-direction cells recorded from the postsu- (2003). Local evolvability of statistically neu- biculum in freely moving rats, I: Description and tral GasNet robot controllers. Bio Systems, 69, quantitative analysis. The Journal of Neurosci- 223–243. doi:10.1016/S0303-2647(02)00139-9 ence, 10(2), 420–435. Smith, T. M. C., Husbands, P., Philippides, A., & Taube, J. S., Muller, R. U., & Ranck, J. B. O’Shea, M. (2002). Neuronal plasticity and tem- (1990b). Head-direction cells recorded from the poral adaptivity: GasNet robot control networks. postsubiculum in freely moving rats, II: Effects Adaptive Behavior, 10(3/4), 161–184. of environmental manipulations. The Journal of Neuroscience, 10(2), 436–447. Srinivasan, M. (2006). Small brains, smart computations: Vision and navigation in honey- Thrun, T., Burgard, W., & Fox, D. (2005). Proba- bees, and applications to robotics. International bilistic Robotics. Cambridge, MA: MIT Press. Congress Series, 1291, 30–37. doi:10.1016/j. Tononi, G., Sporns, O., & Edelman, G. (1999). ics.2006.01.055 Measures of degeneracy and redundancy in bio- Srinivasan, M., Chahl, J., Weber, K., Venkatesh, S., logical networks. Proceedings of the National Nagle, M., & Zhang, S. (1999). Robot navigation Academy of Sciences of the United States of inspired by principles of insect vision. Robotics America, 96, 3257. doi:10.1073/pnas.96.6.3257 and Autonomous Systems, 26(2-3), 203–216. Vardy, A. (2006). Long-Range Visual Homing. doi:10.1016/S0921-8890(98)00069-4 In Proceedings of the 2006 IEEE International Srinivasan, M., Thurrowgood, S., & Soccol, D. Conference on Robotics and Biomimetics (pp. (2006). An optical system for guidance of terrain 220-226). following in UAVs. Proc IEEE International Vardy, A., & Moller, R. (2005). Biologically Conference on Advanced Video and Signal Based plausible visual homing methods based on opti- Surveillance (AVSS ’06), Sydney (pp. 51-56). cal flow techniques. Connection Science, 17(1), Pasadena, CA: IEEE Press. 47–90. doi:10.1080/09540090500140958 Srinivasan, M., Zhang, S., Chahl, J., Stange, Vargas, P., Di Paolo, E., & Husbands, P. (2008). G., & Garratt, M. (2004). An overview of insect Exploring non-spatial GasNets in a delayed re- inspired guidance for application in ground and sponse robot task. In Bullock, S. (Eds.), Proc. airborne platforms. Proceedings of the Institu- Artificial Life XI (pp. 640–647). Cambridge, MA: tion of Mechanical Engineers. Part G, Jour- MIT Press. nal of Aerospace Engineering, 218, 375–388. doi:10.1243/0954410042794966

246 Artificial Neural Systems for Robots

Vargas, P., Moioli, R., Von Zuben, F., & Hus- Wehner, R., Michel, B., & Antonsen, P. (1996). bands, P. (2009). Homeostasis and Evolution Visual Navigation in Insects: Coupling of Ego- Together Dealing with Novelties and Managing centric and Geocentric Information. The Journal Disruptions. International Journal of Intelligent of Experimental Biology, 199, 129–140. Computing and Cybernetics, 2(3), 435–454. Wheeler, M. (2005). Reconstructing the Cognitive doi:10.1108/17563780910982680 World. Cambridge, MA: MIT Press. Vaughan, E., Di Paolo, E., & Harvey, I. (2004) Wood, G. (2003). Living Dolls: A Magical His- The evolution of control and adaptation in a 3D tory of the Quest for Mechanical Life. London: powered passive dynamic walker. In J. Pollack, Faber and Faber. M. Bedau, P. Husbands, T. Ikegami, & R. Watson, (Eds), Proceedings of the Ninth International Wood, J., & Garthwaite, J. (1994). Model of the Conference on the Simulation and Synthesis of diffusional spread of nitric oxide - implications Living Systems, Artificial Life IX (pp. 139–145). for neural nitric oxide signaling and its pharma- Cambridge, MA: MIT Press. cological properties. Neuropharmacology, 33, 1235–1244. doi:10.1016/0028-3908(94)90022-1 Walter, W. G. (1950). An imitation of life. Sci- entific American, 182(5), 42–45. doi:10.1038/ Wyeth, G., & Milford, M. (2009). Spatial scientificamerican0550-42 Cognition for Robots: Robot Navigation from Biological Inspiration. IEEE Robotics & Auto- Walter, W. G. (1951). A machine that learns. mation Magazine, 16(3), 24–32. doi:10.1109/ Scientific American, 185(2), 60–63. MRA.2009.933620 Walter, W. G. (1953). The Living Brain. London: Duckworth. Webb, B. (2001). Can robots make good models kEY TERMS AND DEFINITIONS of biological behaviour? The Behavioral and Brain Sciences, 24(6), 1033–1050. doi:10.1017/ Autonomous Robot: a device that interacts S0140525X01000127 with the world via sensors and actuators in an autonomous way. Such robots are usually mo- Webb, B. (2009). Animals versus animats: or why bile and exhibit sensorimotor behaviours such not model the real iguana? Adaptive Behavior, as navigation. 17(4), 269–286. doi:10.1177/1059712309339867 Biologically-Inspired Artificial Neural Sys- Webb, B., & Consi, T. (Eds.). (2001). Biorobotics. tems: an artificial neural system, usually organised Cambridge, MA: MIT Press. as one or more networks of artificial neurons, inspired by neural architectures and mechanisms Webb, B., & Scutt, T. (2000). A simple latency found in biology. Such systems are often used as dependent spiking neuron model of cricket pho- artificial nervous systems for robots. notaxis. Biological Cybernetics, 82(3), 247–269. Brain-Based Device: neurorobotic (see be- doi:10.1007/s004220050024 low) devices whose development is most closely Wehner, R., Boyer, M., Loertscher, F., Sommer, associated with Edelman and colleagues at the S., & Menzi, U. (2006). Ant Navigation: One-Way Neurosciences Institute in San Diego. Routes Rather Than Maps. Current Biology, 16, Evolutionary Robotics: an approach to ro- 75–79. doi:10.1016/j.cub.2005.11.035 botics in which some or all aspects of a robot’s

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design (control system, body morphology etc) is haviours, including route learning and landmark achieved using evolutionary search. detection. GasNet: a form of artificial neural network Neurorobotics: an area of research in which incorporating an analogue of volume signalling autonomous robots are used to study embodied whereby gaseous neurotransmitters diffuse from situated models of biological neural systems. A a neural site so to as affect other neurons in the form of biological modelling using robots. vicinity, even if they are not electrically connected View-Based Homing: local visual navigation to the source neuron. methods and algorithm for returning ‘home’ based Insect-Inspired Robotics: a branch of au- on information gleaned from the current view of tonomous robotics inspired by studies of insect the world. Often the current view of the world is behaviours, neural mechanisms and biomechanics. compared with a stored view taken from ‘home’ Neural Systems for Navigation: neural in order to calculate which direction to move in. circuits involved in generating navigation be-

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