A Short History of Studies on Intelligence and Brain in Honeybees Randolf Menzel
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Memory-Prediction Framework for Pattern
Memory–Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex Saulius J. Garalevicius Department of Computer and Information Sciences, Temple University Room 303, Wachman Hall, 1805 N. Broad St., Philadelphia, PA, 19122, USA [email protected] Abstract The neocortex learns sequences of patterns by storing This paper explores an inferential system for recognizing them in an invariant form in a hierarchical neural network. visual patterns. The system is inspired by a recent memory- It recalls the patterns auto-associatively when given only prediction theory and models the high-level architecture of partial or distorted inputs. The structure of stored invariant the human neocortex. The paper describes the hierarchical representations captures the important relationships in the architecture and recognition performance of this Bayesian world, independent of the details. The primary function of model. A number of possibilities are analyzed for bringing the neocortex is to make predictions by comparing the the model closer to the theory, making it uniform, scalable, knowledge of the invariant structure with the most recent less biased and able to learn a larger variety of images and observed details. their transformations. The effect of these modifications on The regions in the hierarchy are connected by multiple recognition accuracy is explored. We identify and discuss a number of both conceptual and practical challenges to the feedforward and feedback connections. Prediction requires Bayesian approach as well as missing details in the theory a comparison between what is happening (feedforward) that are needed to design a scalable and universal model. and what you expect to happen (feedback). -
Unsupervised Anomaly Detection in Time Series with Recurrent Neural Networks
DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2019 Unsupervised anomaly detection in time series with recurrent neural networks JOSEF HADDAD CARL PIEHL KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Unsupervised anomaly detection in time series with recurrent neural networks JOSEF HADDAD, CARL PIEHL Bachelor in Computer Science Date: June 7, 2019 Supervisor: Pawel Herman Examiner: Örjan Ekeberg School of Electrical Engineering and Computer Science Swedish title: Oövervakad avvikelsedetektion i tidsserier med neurala nätverk iii Abstract Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these networks for unsupervised anomaly detection in time series are used in this study. We primarily use our own synthetic data sets in order to discover the networks’ robustness to noise and how they compare to each other regarding different characteristics in the time series. Our results shows that both networks can handle noisy time series and the difference in performance regarding noise robustness is not significant for the time series used in the study. LSTM out- performs HTM in detecting point anomalies on our synthetic time series with sine curve trend but a conclusion about the overall best performing network among these two remains inconclusive. -
Neuromorphic Architecture for the Hierarchical Temporal Memory Abdullah M
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 1 Neuromorphic Architecture for the Hierarchical Temporal Memory Abdullah M. Zyarah, Student Member, IEEE, Dhireesha Kudithipudi, Senior Member, IEEE, Neuromorphic AI Laboratory, Rochester Institute of Technology Abstract—A biomimetic machine intelligence algorithm, that recognition and classification [3]–[5], prediction [6], natural holds promise in creating invariant representations of spatiotem- language processing, and anomaly detection [7], [8]. At a poral input streams is the hierarchical temporal memory (HTM). higher abstraction, HTM is basically a memory based system This unsupervised online algorithm has been demonstrated on several machine-learning tasks, including anomaly detection. that can be trained on a sequence of events that vary over time. Significant effort has been made in formalizing and applying the In the algorithmic model, this is achieved using two core units, HTM algorithm to different classes of problems. There are few spatial pooler (SP) and temporal memory (TM), called cortical early explorations of the HTM hardware architecture, especially learning algorithm (CLA). The SP is responsible for trans- for the earlier version of the spatial pooler of HTM algorithm. forming the input data into sparse distributed representation In this article, we present a full-scale HTM architecture for both spatial pooler and temporal memory. Synthetic synapse design is (SDR) with fixed sparsity, whereas the TM learns sequences proposed to address the potential and dynamic interconnections and makes predictions [9]. occurring during learning. The architecture is interweaved with A few research groups have implemented the first generation parallel cells and columns that enable high processing speed for Bayesian HTM. Kenneth et al., in 2007, implemented the the HTM. -
Effects of Variation in Hunger Levels on Begging Behavior of Nestlings
Eastern Kentucky University Encompass Online Theses and Dissertations Student Scholarship January 2013 Effects of Variation in Hunger Levels on Begging Behavior of Nestlings and the Provsioning Behavior of Male and Female Eastern Phoebes Christopher Adam Heist Eastern Kentucky University Follow this and additional works at: https://encompass.eku.edu/etd Part of the Ornithology Commons Recommended Citation Heist, Christopher Adam, "Effects of Variation in Hunger Levels on Begging Behavior of Nestlings and the Provsioning Behavior of Male and Female Eastern Phoebes" (2013). Online Theses and Dissertations. 177. https://encompass.eku.edu/etd/177 This Open Access Thesis is brought to you for free and open access by the Student Scholarship at Encompass. It has been accepted for inclusion in Online Theses and Dissertations by an authorized administrator of Encompass. For more information, please contact [email protected]. EFFECTS OF VARIATION IN HUNGER LEVELS ON BEGGING BEHAVIOR OF NESTLINGS AND THE PROVISIONING BEHAVIOR OF MALE AND FEMALE EASTERN PHOEBES By Christopher Adam Heist Bachelor of Science Bowling Green State University Bowling Green, Ohio 2010 Submitted to the Faculty of the Graduate School of Eastern Kentucky University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December, 2013 Copyright © Christopher Adam Heist, 2013 All rights reserved ii DEDICATION This thesis is dedicated to Rebecca. iii ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Gary Ritchison, for his time, advice, and assistance, both in the field and in preparing this thesis. I would also like to thank my committee members, Dr. Charles Elliott and Dr. Paul Cupp, for their assistance during my tenure at EKU, as well as the Blue Grass Army Depot for access to the field site. -
Memory-Prediction Framework for Pattern Recognition: Performance
Memory–Prediction Framework for Pattern Recognition: Performance and Suitability of the Bayesian Model of Visual Cortex Saulius J. Garalevicius Department of Computer and Information Sciences, Temple University Room 303, Wachman Hall, 1805 N. Broad St., Philadelphia, PA, 19122, USA [email protected] Abstract The neocortex learns sequences of patterns by storing This paper explores an inferential system for recognizing them in an invariant form in a hierarchical neural network. visual patterns. The system is inspired by a recent memory- It recalls the patterns auto-associatively when given only prediction theory and models the high-level architecture of partial or distorted inputs. The structure of stored invariant the human neocortex. The paper describes the hierarchical representations captures the important relationships in the architecture and recognition performance of this Bayesian world, independent of the details. The primary function of model. A number of possibilities are analyzed for bringing the neocortex is to make predictions by comparing the the model closer to the theory, making it uniform, scalable, knowledge of the invariant structure with the most recent less biased and able to learn a larger variety of images and observed details. their transformations. The effect of these modifications on The regions in the hierarchy are connected by multiple recognition accuracy is explored. We identify and discuss a number of both conceptual and practical challenges to the feedforward and feedback connections. Prediction requires Bayesian approach as well as missing details in the theory a comparison between what is happening (feedforward) that are needed to design a scalable and universal model. and what you expect to happen (feedback). -
Living with Animals Conference Co-Organized by Robert W. Mitchell, Radhika N
Living with Animals Conference Co-organized by Robert W. Mitchell, Radhika N. Makecha, & Michał Piotr Pręgowski Eastern Kentucky University, 19-21 March 2015 Cover design: Kasey L. Morris Conference overview Each day begins with a keynote speaker, and follows with two tracks (in separate locations) that will run concurrently. Breakfast foods and coffee/tea/water will be available prior to the morning keynotes. Coffee breaks (i.e., snacks and coffee/tea/water) are scheduled between sequential groups of talks. Thus, for example, if one session is from 9:05-10:15, and the next session is 10:40-11:40, there is a coffee break from 10:15-10:40. Drinks and edibles should be visible at or near the entry to the rooms where talks are held. Book display: Throughout the conference in Library Room 201, there is a book display. Several university presses have generously provided books for your perusal (as well as order sheets), and some conference participants will be displaying their books as well. Thursday features the “Living with Horses” sessions, as well as concurrent sessions, and has an optional (pre-paid) trip to Berea for shopping and dinner at the Historic Boone Tavern Restaurant. Friday features the “Teaching with Animals” sessions throughout the morning and early afternoon (which includes a boxed lunch during panel discussions and a movie showing and discussion); “Living with Animals” sessions continuing in the late afternoon, and a Conference Dinner at Masala Indian restaurant. Saturday includes “Living with Animals” sessions throughout the day and Poster Presentations during a buffet lunch. In addition, there is the optional trip to the White Hall State Historic Site (you pay when you arrive at the site). -
The Neuroscience of Human Intelligence Differences
Edinburgh Research Explorer The neuroscience of human intelligence differences Citation for published version: Deary, IJ, Penke, L & Johnson, W 2010, 'The neuroscience of human intelligence differences', Nature Reviews Neuroscience, vol. 11, pp. 201-211. https://doi.org/10.1038/nrn2793 Digital Object Identifier (DOI): 10.1038/nrn2793 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Nature Reviews Neuroscience Publisher Rights Statement: This is an author's accepted manuscript of the following article: Deary, I. J., Penke, L. & Johnson, W. (2010), "The neuroscience of human intelligence differences", in Nature Reviews Neuroscience 11, p. 201-211. The final publication is available at http://dx.doi.org/10.1038/nrn2793 General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Nature Reviews Neuroscience in press The neuroscience of human intelligence differences Ian J. Deary*, Lars Penke* and Wendy Johnson* *Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH4 2EE, Scotland, UK. All authors contributed equally to the work. -
Way-Finding in Displaced Clock-Shifted Bees Proves Bees Use a Cognitive Map
Way-finding in displaced clock-shifted bees proves bees use a cognitive map James F. Cheesemana,b,1, Craig D. Millarb,c, Uwe Greggersd, Konstantin Lehmannd, Matthew D. M. Pawleya,e, Charles R. Gallistelf,1, Guy R. Warmana,b, and Randolf Menzeld aDepartment of Anaesthesiology, School of Medicine, and bSchool of Biological Sciences, University of Auckland, Auckland, 1142, New Zealand; cAllan Wilson Centre for Molecular Ecology and Evolution, Palmerston North, PN4442, New Zealand; dInstitute of Biology–Neurobiology, Free University of Berlin, 14195 Berlin, Germany; eInstitute of Natural and Mathematical Sciences, Massey University, Auckland, 0745, New Zealand; and fDepartment of Psychology and Center for Cognitive Science, Rutgers University, Piscataway, NJ 08854-8020 Contributed by Charles R. Gallistel, May 1, 2014 (sent for review January 22, 2014) Mammals navigate by means of a metric cognitive map. Insects, whether these abilities rest on the construction and use of most notably bees and ants, are also impressive navigators. The a metric cognitive map (11–18). question whether they, too, have a metric cognitive map is Way-finding, the ability to set a course from one familiar but important to cognitive science and neuroscience. Experimentally otherwise arbitrarily chosen location to another location that is captured and displaced bees often depart from the release site in not perceptible from the first, is the signature of a metric cog- the compass direction they were bent on before their capture, nitive map. Displacing an animal from its current location to an even though this no longer heads them toward their goal. When experimenter-chosen location within the territory the animal is they discover their error, however, the bees set off more or less presumed to be familiar with, and then observing its subsequent directly toward their goal. -
The Honey Bee Dance Language
The Honey Bee Dance Language Background Components of the dance language Honey bee dancing, perhaps the most intriguing aspect of their biology, is also one of the most fascinating behaviors When an experienced forager returns to the colony with in animal life. Performed by a worker bee that has returned a load of nectar or pollen that is sufficiently nutritious to to the honey comb with pollen or nectar, the dances, in warrant a return to the source, she performs a dance on the essence, constitute a language that “tells” other workers surface of the honey comb to tell other foragers where the where the food is. By signaling both distance and direction food is. The dancer “spells out” two items of information— with particular movements, the worker bee uses the dance distance and direction—to the target food patch. Recruits language to recruit and direct other workers in gathering then leave the hive to find the nectar or pollen. pollen and nectar. Distance and direction are presented in separate compo- The late Karl von Frisch, a professor of zoology at the Uni- nents of the dance. versity of Munich in Germany, is credited with interpret- ing the meaning of honey bee dance movements. He and Distance his students carried out decades of research in which they carefully described the different components of each dance. When a food source is very close to the hive (less than 50 Their experiments typically used glass-walled observation meters), a forager performs a round dance (Figure 1). She hives and paint-marked bee foragers. -
A Review of Some Aspects of Avian Field Ethology 1
A REVIEW OF SOME ASPECTS OF AVIAN FIELD ETHOLOGY 1 ROBERT W. FICKEN AND MILLICENT S. FICKEN T•E last 30 yearshave seen the developmentof a new approachto the study of behavior, which has increasinglyinterested ornithologists as they have attemptedto understandavian biologyfully. This new ap- proachis now known as ethology. Both amateur and professionalorni- thologistshave contributedto its progressfrom the beginning. A basictenet of ethologyis that all behaviorof animalscan eventually be understood.The ethologistattempts to explainbehavior functionally (what doesit do for the animal?), causally(what are the internal and externalfactors responsible for each given behavior?),and evolutionarily (what is the probablephylogeny of the behavior,how doesit contribute to th'esurvival of the species,and what are the selectivepressures acting uponit?) (seeTinbergen, 1951, 1959; Hinde, 1959b). Somebasic proceduresare essentialin an ethologicalstudy (Hinde, 1959b: 564). The first step is to describeand classifyall the behavior of an animal, at the same time, if possible,dividing the behavior into logicalunits which can be dealt with furtherin other disciplines--particu- larly physiologyand ecology(see Russellet al., 1954). Althoughstudies often beginqualitatively, they eventuallyshould be quantified. Generali- zations,which are to be valid for many species,must be basedon work using closelyrelated speciesfirst, and more distantly related ones later. The animal is studied as an integrated whole. Some workers, notably Konrad Lorenz, keep and breed animalsin captivity under closescrutiny. Most of this paper dealswith communicationof birds. We have stressed particularly the analysisof displays,their evolution,and their relation to taxonomy--topicsof great interest to systematicornithologists as well as to ethologists.In addition, we discusscertain maintenanceactivities. LITERATURE OF ET•OLOC¾ Much of the early ethologicalliterature appearedin Europeanpublications, but it is now also appearingin this country frequently. -
Chapter 51 Animal Behavior
Chapter 51 Animal Behavior Lecture Outline Overview: Shall We Dance? • Red-crowned cranes (Grus japonensis) gather in groups to dance, prance, stretch, bow, and leap. They grab bits of plants, sticks, and feathers with their bills and toss them into the air. • How does a crane decide that it is time to dance? In fact, why does it dance at all? • Animal behavior is based on physiological systems and processes. • An individual behavior is an action carried out by the muscular or hormonal system under the control of the nervous system in response to a stimulus. • Behavior contributes to homeostasis; an animal must acquire nutrients for digestion and find a partner for sexual reproduction. • All of animal physiology contributes to behavior, while animal behavior influences all of physiology. • Being essential for survival and reproduction, animal behavior is subject to substantial selective pressure during evolution. • Behavioral selection also acts on anatomy because body form and appearance contribute directly to the recognition and communication that underlie many behaviors. Concept 51.1: A discrete sensory input is the stimulus for a wide range of animal behaviors. • An animal’s behavior is the sum of its responses to external and internal stimuli. Classical ethology presaged an evolutionary approach to behavioral biology. • In the mid-20th century, pioneering behavioral biologists developed the discipline of ethology, the scientific study of how animals behave in their natural environments. • Niko Tinbergen, of the Netherlands, suggested four questions that must be answered to fully understand any behavior. 1. What stimulus elicits the behavior, and what physiological mechanisms mediate the response? 2. -
What Can Vigilance Tell Us About Fear?
Beauchamp, Guy (2017) What can vigilance tell us about fear?. Animal Sentience 15(1) DOI: 10.51291/2377-7478.1203 This article has appeared in the journal Animal Sentience, a peer-reviewed journal on animal cognition and feeling. It has been made open access, free for all, by WellBeing International and deposited in the WBI Studies Repository. For more information, please contact [email protected]. Animal Sentience 2017.015: Beauchamp on Fear & Vigilance Call for Commentary: Animal Sentience publishes Open Peer Commentary on all accepted target articles. Target articles are peer-reviewed. Commentaries are editorially reviewed. There are submitted commentaries as well as invited commentaries. Commentaries appear as soon as they have been reviewed, revised and accepted. Target article authors may respond to their commentaries individually or in a joint response to multiple commentaries. Instructions: http://animalstudiesrepository.org/animsent/guidelines.html What can vigilance tell us about fear? Guy Beauchamp Independent Researcher, Canada Abstract: Animal vigilance is concerned with the monitoring of potential threats caused by predators and conspecifics. Researchers have argued that threats are part of a landscape of fear tracking the level of risk posed by predators and conspecifics. Vigilance, which is expected to vary with the level of risk, could thus be used as a measure of fear. Here, I explore the relationship between vigilance and fear caused by predators and conspecifics. The joint occurrence of vigilance and other physiological responses to fear, such as increased heart rate and stress hormone release, would bolster the idea that vigilance can be a useful marker of fear. While there is some support for a positive relationship between vigilance and physiological correlates of fear, a common theme in much of the empirical research is that vigilance and physiological correlates of fear are often uncoupled.