http://www.bibsonomy.org/burst/user/bernulyBibSonomy BuRST Feed for /user/bernuly2008-11-19T05:53:23+01:00http://www.bibsonomy.org/bibtex/25d4dea1b5c111bd98b28555b039e6024/bernulybernuly2007-09-22T23:49:04+02:00cognitive frame modeling <span style="color:#555555;">William J. <a href="http://www.bibsonomy.org/author/Clancey">Clancey</a> </span>(<em>1989</em>)Sat Sep 22 23:49:04 CEST 2007107--114The frame of reference problem in cognitive modeling1989cognitive frame modeling Since at least the mid-70's there has been widespread agreement among cognitive science researchers that models of a problem-solving agent should incorporate its knowledge about the world and an inference procedure for interpreting this knowledge to construct plans and take actions. Research questions have focused on how knowledge is represented in computer programs and how such cognitive models can be verified in psychological experiments. We are now experiencing increasing confusion and misunderstanding as different critiques are leveled against this methodology and new jargon is introduced (e.g., "not rules," "ready-to-hand," "background," "situated," "subsymbolic"). Such divergent approaches put a premium on improving our understanding of past modeling methods, allowing us to more sharply contrast proposed alternatives. This paper compares and synthesizes new robotic research that is founded on the idea that knowledge does not consist of objective representations of the world. This research develops a new view of planning that distinguishes between a robot designer's ontological preconceptions, the dynamics of a robot's interaction with an environment, and an observer's descriptive theories of patterns in the robot's behavior. These frame-of-reference problems are illustrated here and unified by a new framework for describing cognitive models.http://www.bibsonomy.org/bibtex/2f0bb53290115f13f411351a3ec17dfd6/bernulybernuly2007-05-05T21:46:40+02:00imported <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Metta">Metta</a> und P. <a href="http://www.bibsonomy.org/author/Fitzpatrick">Fitzpatrick</a> und L. <a href="http://www.bibsonomy.org/author/Natale">Natale</a> </span><em>International Journal of Advanced Robotics Systems, special issue on Software Development and Integration in Robotics</em>(<em>2006</em>)Sat May 05 21:46:40 CEST 2007International Journal of Advanced Robotics Systems, special issue on Software Development and Integration in Robotics1YARP: Yet Another Robot Platform32006imported http://www.bibsonomy.org/bibtex/20cbaee07ef43fdc73ef0ea584132063c/bernulybernuly2007-05-05T21:38:34+02:00imported <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Thrun">Thrun</a> und M. <a href="http://www.bibsonomy.org/author/Montemerlo">Montemerlo</a> und H. <a href="http://www.bibsonomy.org/author/Dahlkamp">Dahlkamp</a> und D. <a href="http://www.bibsonomy.org/author/Stavens">Stavens</a> und A. <a href="http://www.bibsonomy.org/author/Aron">Aron</a> und J. <a href="http://www.bibsonomy.org/author/Diebel">Diebel</a> und P. <a href="http://www.bibsonomy.org/author/Fong">Fong</a> und J. <a href="http://www.bibsonomy.org/author/Gale">Gale</a> und M. <a href="http://www.bibsonomy.org/author/Halpenny">Halpenny</a> und G. <a href="http://www.bibsonomy.org/author/Hoffmann">Hoffmann</a> und K. <a href="http://www.bibsonomy.org/author/Lau">Lau</a> und C. <a href="http://www.bibsonomy.org/author/Oakley">Oakley</a> und M. <a href="http://www.bibsonomy.org/author/Palatucci">Palatucci</a> und V. <a href="http://www.bibsonomy.org/author/Pratt">Pratt</a> und P. <a href="http://www.bibsonomy.org/author/Stang">Stang</a> und S. <a href="http://www.bibsonomy.org/author/Strohband">Strohband</a> und C. <a href="http://www.bibsonomy.org/author/Dupont">Dupont</a> und L.-E. <a href="http://www.bibsonomy.org/author/Jendrossek">Jendrossek</a> und C. <a href="http://www.bibsonomy.org/author/Koelen">Koelen</a> und C. <a href="http://www.bibsonomy.org/author/Markey">Markey</a> und C. <a href="http://www.bibsonomy.org/author/Rummel">Rummel</a> und J. <a href="http://www.bibsonomy.org/author/van Niekerk">van Niekerk</a> und E. <a href="http://www.bibsonomy.org/author/Jensen">Jensen</a> und P. <a href="http://www.bibsonomy.org/author/Alessandrini">Alessandrini</a> und G. <a href="http://www.bibsonomy.org/author/Bradski">Bradski</a> und B. <a href="http://www.bibsonomy.org/author/Davies">Davies</a> und S. <a href="http://www.bibsonomy.org/author/Ettinger">Ettinger</a> und A. <a href="http://www.bibsonomy.org/author/Kaehler">Kaehler</a> und A. <a href="http://www.bibsonomy.org/author/Nefian">Nefian</a> und P. <a href="http://www.bibsonomy.org/author/Mahoney">Mahoney</a> </span><em>Journal of Field Robotics</em>(<em>2006</em>) <em>accepted for publication . </em>Sat May 05 21:38:34 CEST 2007Journal of Field Roboticsaccepted for publicationWinning the DARPA Grand Challenge2006imported http://www.bibsonomy.org/bibtex/26c3bfdbc005c2e7813a464f9d0a63915/bernulybernuly2007-05-05T18:37:29+02:00imported <span style="color:#555555;">A <a href="http://www.bibsonomy.org/author/Morrison">Morrison</a> und C <a href="http://www.bibsonomy.org/author/Mehring">Mehring</a> und T <a href="http://www.bibsonomy.org/author/Geisel">Geisel</a> und AD <a href="http://www.bibsonomy.org/author/Aertsen">Aertsen</a> und M <a href="http://www.bibsonomy.org/author/Diesmann">Diesmann</a> </span><em>Neural Comput</em>(<em>2005</em>)Sat May 05 18:37:29 CEST 2007Neural Comput1776--1801{Advancing the boundaries of high-connectivity network simulation with distributed computing}172005imported The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The software scales excellently on a wide range of tested hardware, so it can be used in an interactive and iterative fashion for the development of ideas, and results can be produced quickly even for very large networks. In contrast to earlier approaches, a wide class of neuron models and synaptic dynamics can be represented.http://www.bibsonomy.org/bibtex/2114ece6f87f34a92660990e6d5caa171/bernulybernuly2007-05-05T18:37:29+02:00imported <span style="color:#555555;">ML <a href="http://www.bibsonomy.org/author/Hines">Hines</a> und NT <a href="http://www.bibsonomy.org/author/Carnevale">Carnevale</a> </span><em>Neural Comput</em>(<em>1997</em>)Sat May 05 18:37:29 CEST 2007Neural Comput1179--1209{The NEURON simulation environment}91997imported The moment-to-moment processing of information by the nervous system involves the propagation and interaction of electrical and chemical signals that are distributed in space and time. Biologically realistic modeling is needed to test hypotheses about the mechanisms that govern these signals and how nervous system function emerges from the operation of these mechanisms. The NEURON simulation program provides a powerful and flexible environment for implementing such models of individual neurons and small networks of neurons. It is particularly useful when membrane potential is nonuniform and membrane currents are complex. We present the basic ideas that would help informed users make the most efficient use of NEURON.http://www.bibsonomy.org/bibtex/236cd63cc48ce85b02a7dcc8532ddd9ae/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">B <a href="http://www.bibsonomy.org/author/Webb">Webb</a> </span><em>Behav Brain Sci</em>(<em>2001</em>)Sat May 05 18:36:59 CEST 2007Behav Brain Sci1033--1094{Can robots make good models of biological behaviour? }242001imported How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models--the representation of a hypothesis about a target system--are distinguished from several other relationships also termed "modelling" in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments.http://www.bibsonomy.org/bibtex/2b73acda1e9d13dd0f2e7761af004d6d4/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">P.F.M.J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>Proceedings 1998 IEEE World Conference on Computational Intelligence 1998</em>(<em>1998</em>)Sat May 05 18:36:59 CEST 2007AnchorageProceedings 1998 IEEE World Conference on Computational Intelligence 1998147--153{Synthetic Epistemology: The acquisition, retention, and expression of knowledge in natural and synthetic systems}1998imported http://www.bibsonomy.org/bibtex/2dd1fa9e70e60e4bfee5ee6d8756facdf/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>chapter Connectionist Explanation: taking positions in the Mind-Brain dilemma, </em><em>Seite125--181. </em><em>Chapman &amp; Hall., </em><em>London, </em>(<em>1997</em>)Sat May 05 18:36:59 CEST 2007London{Connectionist Explanation: taking positions in the Mind-Brain dilemma}125--181{Neural networks and a New AI}1997imported http://www.bibsonomy.org/bibtex/2edc7ef96535526a08f6fa61675595fa8/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">Shun-ichi <a href="http://www.bibsonomy.org/author/Amari">Amari</a> und Francesco <a href="http://www.bibsonomy.org/author/Beltrame">Beltrame</a> und Jan G. <a href="http://www.bibsonomy.org/author/Bjaalie">Bjaalie</a> und Turgay <a href="http://www.bibsonomy.org/author/Dalkara">Dalkara</a> und Erik De <a href="http://www.bibsonomy.org/author/Schutter">Schutter</a> und Gary F. <a href="http://www.bibsonomy.org/author/Egan">Egan</a> und Nigel H. <a href="http://www.bibsonomy.org/author/Goddard">Goddard</a> und Carmen <a href="http://www.bibsonomy.org/author/Gonzalez">Gonzalez</a> und Sten <a href="http://www.bibsonomy.org/author/Grillner">Grillner</a> und Andreas <a href="http://www.bibsonomy.org/author/Herz">Herz</a> und K.-Peter <a href="http://www.bibsonomy.org/author/Hoffmann">Hoffmann</a> und Iiro <a href="http://www.bibsonomy.org/author/Jaaskelainen">Jaaskelainen</a> und Stephen H. <a href="http://www.bibsonomy.org/author/Koslow">Koslow</a> und Soo-Young <a href="http://www.bibsonomy.org/author/Lee">Lee</a> und Line <a href="http://www.bibsonomy.org/author/Matthiessen">Matthiessen</a> und Perry L. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> und Fernando Mira Da <a href="http://www.bibsonomy.org/author/Silva">Silva</a> und Mirko <a href="http://www.bibsonomy.org/author/Novak">Novak</a> und Viji <a href="http://www.bibsonomy.org/author/Ravindranath">Ravindranath</a> und Raphael <a href="http://www.bibsonomy.org/author/Ritz">Ritz</a> und Ulla <a href="http://www.bibsonomy.org/author/Ruotsalainen">Ruotsalainen</a> und Vaclav <a href="http://www.bibsonomy.org/author/Sebestra">Sebestra</a> und Shankar <a href="http://www.bibsonomy.org/author/Subramaniam">Subramaniam</a> und Yiyuan <a href="http://www.bibsonomy.org/author/Tang">Tang</a> und Arthur W. <a href="http://www.bibsonomy.org/author/Toga">Toga</a> und Shiro <a href="http://www.bibsonomy.org/author/Usui">Usui</a> und Jaap Van <a href="http://www.bibsonomy.org/author/Pelt">Pelt</a> und Paul <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> und David <a href="http://www.bibsonomy.org/author/Willshaw">Willshaw</a> und Andrzej Wrobel (The OECD Neuroinformatics Working <a href="http://www.bibsonomy.org/author/Group)">Group)</a> </span><em>Neuroinformatics Journal</em>(<em>2003</em>)Sat May 05 18:36:59 CEST 2007Neuroinformatics Journal149---166{Neuroscience data and tool sharing: A legal and policy framework for neuroinformatics }12003 imported http://www.bibsonomy.org/bibtex/2d4eff99ebe621a111ec3ca1bf970ef2a/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> und T. <a href="http://www.bibsonomy.org/author/Voegtlin">Voegtlin</a> und R. J. <a href="http://www.bibsonomy.org/author/Douglas">Douglas</a> </span><em>Nature</em>(<em>2003</em>)Sat May 05 18:36:59 CEST 2007Nature620--624{Environmentally mediated synergy between perception and behaviour in mobile robots}4252003imported The notion that behaviour influences perception seems self-evident, but the mechanism of their interaction is not known. Perception and behaviour are usually considered to be separate processes. In this view, perceptual learning constructs compact representations of sensory events, reflecting their statistical properties, independently of behavioural relevance. Behavioural learning, however, forms associations between perception and action, organized by reinforcement, without regard for the construction of perception. It is generally assumed that the interaction between these two processes is internal to the agent, and can be explained solely in terms of the neuronal substrate. Here we show, instead, that perception and behaviour can interact synergistically via the environment. Using simulated and real mobile robots, we demonstrate that perceptual learning directly supports behavioural learning and so promotes a progressive structuring of behaviour. This structuring leads to a systematic bias in input sampling, which directly affects the organization of the perceptual system. This external, environmentally mediated feedback matches the perceptual system to the emerging behavioural structure, so that the behaviour is stabilized.http://www.bibsonomy.org/bibtex/2effe543b7de88254aebf67f16eccd408/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> und T. <a href="http://www.bibsonomy.org/author/Voegtlin">Voegtlin</a> </span><em>Neural Netw</em>(<em>1998</em>)Sat May 05 18:36:59 CEST 2007Neural Netw1531--1549{A bottom up approach towards the acquisition and expression of sequential representations applied to a behaving real-world device: Distributed Adaptive Control III}111998imported Biological systems display a high degree of flexibility in problem solving. In this paper a model is presented, Distributed Adaptive Control III (DACIII), which is aimed at understanding these forms of behavior. DACIII is part of a larger modeling series directed at understanding how biological systems acquire, retain, and express knowledge of the world. This modeling series has its roots, on one hand, in the methodological consideration that brain and behavior need to be modeled from a multi-level perspective. On the other, the importance of the acquisition of representations of events in the world, as opposed to an a priori specification, is emphasized. DACIII is presented against the background of the paradigms of classical and operant conditioning. On the basis of an analysis of these experimental approaches towards the study of animal behavior a theoretical framework is defined aimed at identifying the minimal requirements of a control structure which could display these behaviors. The proposed model is evaluated in different configurations using both simulated and real robots. It is demonstrated that DACIII is able to fully bootstrap itself from a mode of control which solely relies on proximal sensors and predefined reflexes, to a level of control which is dominated by acquired representations of events transduced by distal sensors. This transition is reflected in the performance of the behaving device, from strongly variable trajectories to highly structured behavioral sequences. The results are compared with alternative models of classical and operant conditioning and models which attempt to capture the properties of its underlying neural substrate.http://www.bibsonomy.org/bibtex/2726268708568ebbf570907edb79fd48c/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>Technical report, </em><em>Institute of Neuroinformatics, </em><em>June1997. </em>Sat May 05 18:36:59 CEST 2007JuneXmorph: a software tool for the synthesis and analysis of neural systemsTechnical report1997imported http://www.bibsonomy.org/bibtex/28d28c7719ef663071016bbb3b05747ff/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">Gordon M. <a href="http://www.bibsonomy.org/author/Shepherd">Shepherd</a> </span><em>Oxford University Press, </em>(<em>2003</em>)Sat May 05 18:36:59 CEST 2007The Synaptic Organization of the Brain2003imported http://www.bibsonomy.org/bibtex/2e8486e67804f3ff0f42dc7112076929e/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">M. A. <a href="http://www.bibsonomy.org/author/S\&#039;anchez-Monta\ n\&#039;es">S'anchez-Monta n'es</a> und P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> und P. <a href="http://www.bibsonomy.org/author/K{\&#034;o}nig">K&#246;nig</a> </span><em>Neural Comput</em>(<em>2000</em>)Sat May 05 18:36:59 CEST 2007Neural Comput519--529{Local and global gating of synaptic plasticity}122000imported Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.http://www.bibsonomy.org/bibtex/2a495a6372e23f1ca3acce32518a696e8/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">W. E. <a href="http://www.bibsonomy.org/author/Reichardt">Reichardt</a> </span><em>chapter Autocorrelation, a principle for the evaluation of sensory information by the central nervous system., </em><em>Seite303-317. </em><em>Wiley, </em><em>New York, </em>(<em>1961</em>)Sat May 05 18:36:59 CEST 2007New YorkAutocorrelation, a principle for the evaluation of sensory information by the central nervous system.303-317Sensory Communication1961imported http://www.bibsonomy.org/bibtex/229641b5bf150d1d041c6ec60a5017792/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">Pawel <a href="http://www.bibsonomy.org/author/Pyk">Pyk</a> und Sergi Berm'udez i <a href="http://www.bibsonomy.org/author/Badia">Badia</a> und Ulysses <a href="http://www.bibsonomy.org/author/Bernardet">Bernardet</a> und Philipp <a href="http://www.bibsonomy.org/author/Kn{\&#034;u}sel">Kn&#252;sel</a> und Mikael <a href="http://www.bibsonomy.org/author/Carlsson">Carlsson</a> und Jing <a href="http://www.bibsonomy.org/author/Gu">Gu</a> und Eric <a href="http://www.bibsonomy.org/author/Chanie">Chanie</a> und Bill S. <a href="http://www.bibsonomy.org/author/Hansson">Hansson</a> und Tim C. <a href="http://www.bibsonomy.org/author/Pearce">Pearce</a> und Paul F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>Autonomous Robotics</em><em>2(3):197--213</em>(<em>2006</em>)Sat May 05 18:36:59 CEST 2007Autonomous Robotics3197--213An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search22006imported http://www.bibsonomy.org/bibtex/2f030ccbd8029d7839296445bebcf5706/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Manzolli">Manzolli</a> und P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>Computer Music Journal</em>(<em>2005</em>)Sat May 05 18:36:59 CEST 2007Computer Music Journal55 -- 74Roboser: a Real-World Composition System292005imported http://www.bibsonomy.org/bibtex/2107fc264caf4ae348f272ea100ec9a68/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">C. <a href="http://www.bibsonomy.org/author/Hofstoetter">Hofstoetter</a> und M. <a href="http://www.bibsonomy.org/author/Mintz">Mintz</a> und P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>European Journal of Neuroscience</em>(<em>2002</em>)Sat May 05 18:36:59 CEST 2007European Journal of Neuroscience1361--1376{The Cerebellum in Action: A Simulation and Robotics Study}162002imported http://www.bibsonomy.org/bibtex/2d0c18805e0dcbd4573efc7f9a2494530/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">K. <a href="http://www.bibsonomy.org/author/Eng">Eng</a> und D. <a href="http://www.bibsonomy.org/author/Klein">Klein</a> und A. <a href="http://www.bibsonomy.org/author/B{\&#034;a}bler">B&#228;bler</a> und U. <a href="http://www.bibsonomy.org/author/Bernardet">Bernardet</a> und M. <a href="http://www.bibsonomy.org/author/Blanchard">Blanchard</a> und M. <a href="http://www.bibsonomy.org/author/Costa">Costa</a> und T. <a href="http://www.bibsonomy.org/author/Delbr{\&#034;u}ck">Delbr&#252;ck</a> und R. J. <a href="http://www.bibsonomy.org/author/Douglas">Douglas</a> und K. <a href="http://www.bibsonomy.org/author/Hepp">Hepp</a> und J. <a href="http://www.bibsonomy.org/author/Manzo lli">Manzo lli</a> und M. <a href="http://www.bibsonomy.org/author/Mintz">Mintz</a> und F. <a href="http://www.bibsonomy.org/author/Roth">Roth</a> und U. <a href="http://www.bibsonomy.org/author/Rutishauser">Rutishauser</a> und K. <a href="http://www.bibsonomy.org/author/Wassermann">Wassermann</a> und A. M. <a href="http://www.bibsonomy.org/author/Whatley">Whatley</a> und A. <a href="http://www.bibsonomy.org/author/Wittmann">Wittmann</a> und R. <a href="http://www.bibsonomy.org/author/Wyss">Wyss</a> und P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> </span><em>Rev Neurosci</em>(<em>2003</em>)Sat May 05 18:36:59 CEST 2007Rev Neurosci145--80{Design for a brain revisited: the neuromorphic design and functionality of the interactive space 'Ada'}142003imported While much is now known about the operation and organisation of the brain at the neuronal and microcircuit level, we are still some wa y from understanding it as a complete system from the lowest to the highest levels of description. One way to gain such an integrative understanding of neural systems is to construct them. We have built the largest neuromorphic system yet known, an interactive space called 'Ada' that is able to in teract with many people simultaneously using a wide variety of sensory and behavioural modalities. 'She' received 553,700 visitors over 5 months duri ng the Swiss Expo.02 in 2002. In this paper we present the broad motivations, design and technologies behind Ada, and discuss the construction and an alysis of the system.http://www.bibsonomy.org/bibtex/2f21ad7a5fe0c315b83b39d7909c37d11/bernulybernuly2007-05-05T18:36:59+02:00imported <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Blanchard">Blanchard</a> und P. F. M. J. <a href="http://www.bibsonomy.org/author/Verschure">Verschure</a> und F. C. <a href="http://www.bibsonomy.org/author/Rind">Rind</a> </span><em>Int J Neural Syst</em>(<em>1999</em>)Sat May 05 18:36:59 CEST 2007Int J Neural Syst405--410{Using a mobile robot to study locust collision avoidance responses}91999imported The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.