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Tate 2007 Planning and Doing Thigns Edinburgh Research Explorer Planning and Doing Things Citation for published version: Tate, A 2007, 'Planning and Doing Things', AISB Quarterly, vol. 125, pp. 7-8. <http://www.aisb.org.uk/publications/aisbq/AISBQ125.pdf> Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: AISB Quarterly 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: 07. Oct. 2021 Quarterly No.125 The Newsletter of the Society for the Study of Artificial Intelligence and Simulation of Behaviour In this issue: Features Three-Dimensional • Daniel Myatt and Slavomir Nasuto 1 Three Dimensional Recon- Reconstruction of Neurons struction of neurons • Nicholas Jennings 3 with Neuromantic Decentralised Control of Complex Systems • Austin Tate 7 Despite the tendency of traditional artificial neural nected cylinders of varying radius and length. The Planning and Doing Things networks to simplify the role of dendritic and axonal tree is thus stored as a series of points, where each morphology to simple connections between neurons, point is associated with a 3D position and radius, as Society in reality neurites have an important role to play in well as having a corresponding ‘parent point’. From • AISB Symposia 9 determining neuronal behaviour. For example, the such a model, the behaviour of the neuron may be AISB’08 and call for pro- posals for AISB’09 diameter significantly affects the oscillatory behaviour estimated using a compartmental simulator such as of a neurite. Similarly, signals take time to travel NEURON [2] or GENESIS [3]. Also, it is possible Reviews down the length of dendrites, adding a spatiotemporal to calculate a variety of useful statistics from such • Ariel Procaccia 9 Cooperative Information characteristic into neuronal operation. models to either classify them or differentiate between Agents a control and experimental group. Father Hacker There are two main motivations for the creation • Computational Theology 12 of models of neuronal morphology. Primarily, such The raw data required for a reconstruction is a models are required in order to investigate the role stack comprised of a series of images taken of the that the morphology of a neuron plays in determin- tissue sample at varying distances away from the ing function by allowing validation of computational microscope lens. In each image a different planar simulations with the results of in vitro electrophysi- section of the sample is in focus, and thus the image ological experiments. Additionally, it is thought that stack as a whole forms a pseudo-3D representation aberrant neuronal morphology may play a role in of the neuron. (continued on p. 2) brain diseases such as epilepsy[1], and thus being able to meaningfully characterise abnormalities may yield novel insight into such conditions. The standard way to model neuronal morphology is to represent the dendritic tree as a series of con- Figure 1. The basic process of neuronal reconstruction: from the original image stack (with resolution 2862 by 1649 and 86 images) in the top left panel, points are marked out sequentially down the dendrites (top right panel). This process culminates in a full 3D model of the basal tree converted to metric coordinates (bottom panels). No. 125, Spring 2007 1 Three-Dimensional Simulation of Neurons The AISB Quarterly is published by the Society for the Study of Typically, neuronal reconstruction starts at the References Artificial Intelligence and soma and sequentially marks out points down Simulation of Behaviour. the dendritic tree and their corresponding 1. B.J. Whalley, M. Postlethwaite and A. Constanti, Further ISSN 0268-4179 radii, either manually or automatically. The characterization of muscarinic agonist-induced epileptiform bursting activity in immature rat piriform cortex, in vitro, most manual form of reconstruction requires Copyright © AISB 2007 Neuroscience 134(2), p 549-66. 2005. the user to explicitly identifying points along Articles may be reproduced as long as each dendrite, whereas more advanced meth- 2. N.T. Carnevale and M.L. Hines, The NEURON Book, the copyright notice is ods can take distant points on a dendrite and Cambridge University Press, 2006. included. The item should automatically trace between them. The image be attributed to the AISB 3. U.S. Bhalla, The Book of GENESIS: Exploring Realistic Quarterly and contact slice for which a given short section of den- information should be Neural Models with the GEneral NEural SImulation System, listed. drite is most in focus yields the correspond- second edition, New York: Springer-Verlag, 1998. ing Z-coordinate. Hence, the reconstruction is Quarterly articles do not 4. K.M. Brown, D.E. Donohue, G. D’Alessandro and G.A. necessarily reflect the discretised in the Z axis. official AISB position on Ascoli, A Cross-Platform Freeware Tool for Digital Recon- issues. struction of Neuronal Arborizations From Image Stacks, Neuromantic, a freeware tool that is currently Neuroinformatics 3(4), p 343-359, 2005. Editor under development at the University of Read- Colin G. Johnson. Computing Laboratory, ing, is designed to facilitate the reconstruc- 5. E. Meijering, M. Jacob, J.C.F. Sarria, P. Steiner, H. Hirling University of Kent at tion of neurons across a variety of microscopy and M. Unser, Design and Validation of a Tool for Neurite Canterbury. techniques. Figure 1 illustrates the basic Tracing and Analysis in Fluorescence Microscopy Images, [email protected] Cytometry 58A, p 167-176, 2004. http://www.aisb.org. process of reconstruction using an image stack uk/aisbq/index.shtml of the basal tree of a pyramidal neuron [4]. 6. W.T. Freeman and E.H. Adelson, The Design and Use of Advertising and Recent experiments with Neuromantic show Steerable Filters, IEEE Transations on Pattern Analysis and Administration Machine Intelligence 13(9), p 891-906, 1991. Therie Hendrey-Seabrook that it is more time efficient for semi-manual [email protected] reconstruction than other comparable applica- School of Science and Technology tions such as Neurolucida and Neuron_Mor- Darren Myatt and Slawomir Nasuto University of Sussex pho. Work is currently ongoing to complete School of Systems Engineering Falmer, Brighton UK, BN1 9QH the semi-automatic tracing capabilities of the The University of Reading Tel: (01273) 678448 program, which employs a 3D extension of Email: [email protected] Fax: (01273) 671320 the algorithm used by NeuronJ [5]: steerable http://www.rdg.ac.uk/neuromantic AISB PATRON gaussian filters [6] are employed to identify John Barnden University of Birmingham neurites and a modification of Djikstra’s algo- rithm is used to calculate optimal cost routing AISB FELLOWS Prof Harry Barrow in real-time. Schlumberger Prof Margaret Boden University of Sussex The laborious task of reconstructing neurons Prof Mike Brady has resulted in a significant bottleneck in University of Oxford Prof Alan Bundy computational neurobiology, as it is generally University of Edinburgh difficult to obtain the sample size necessary Prof Tony Cohn University of Leeds for large-scale statistical comparisons. It is Prof John Fox hoped that by providing a freeware tool that Imperial Cancer Research Fund significantly reduces the user effort required, Prof Jim Howe further research into this interesting area can University of Edinburgh Prof Nick Jennings be stimulated. University of Southhampton Prof Aaron Sloman University of Birmingham Dr Richard Young University of Hertfordshire Prof Austin Tate University of Edinburgh 2 AISB Quarterly Decentralised Control of Complex Systems Many modern computing systems have Jennings 2001). In particular, autonomous 2007), automated negotiation (Fatima et to operate in environments that are highly agents are a natural way of viewing flexible al., 2004; Fatima et al., 2006), coordination interconnected, highly unpredictable, in a service providers and consumers and the (Rogers et al., 2007b) and computational constant state of flux, have no centralized interactions between these autonomous mechanism design (Dash et al., 2003). In point of control, and have constituent com- components are naturally modeled as some the latter case, we have built applications ponents owned by a variety of stakeholders form of economic trading process that, if using these techniques in areas such as: that each have their own aims and objec- successful, results in a service contract virtual organizations (Norman et al., 2004), tives. Relevant exemplars include the Web, (or service level agreement) between the sensor networks (Padhy et al., 2006; Rog- Grid Computing, Peer-to-Peer systems, agents involved. ers et al., 2005; Rogers et al., 2006) and Sensor Networks, Pervasive Computing personalized recommendations (Wei et al., and many eCommerce applications. Now, With the team here at Southampton, 2005; Payne et al., 2006). I believe that all of
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