Discreteness and Relevance: A Reply to Roman Poznanski
Minds and Machines
Bayesian computation in recurrent neural circuits
Neural Computation
A Unified Approach to Building and Controlling Spiking Attractor Networks
Neural Computation
Solving the problem of negative synaptic weights in cortical models
Neural Computation
Robosemantics: How Stanley the Volkswagen Represents the World
Minds and Machines
The possibility of a pluralist cognitive science
Journal of Experimental & Theoretical Artificial Intelligence - Pluralism and the Future of Cognitive Science
Cortical circuitry implementing graphical models
Neural Computation
Population models of temporal differentiation
Neural Computation
Explanatory Aspirations and the Scandal of Cognitive Neuroscience
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
Neural Symbolic Decision Making: A Scalable and Realistic Foundation for Cognitive Architectures
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
A hypothetical free synaptic energy function and related states of synchrony
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Stochastic amplification of calcium-activated potassium currents in Ca2+ microdomains
Journal of Computational Neuroscience
Artificial neural networks in smart homes
Designing Smart Homes
A hierachical configuration system for a massively parallel neural hardware platform
Proceedings of the 9th conference on Computing Frontiers
The Explanatory Role of Computation in Cognitive Science
Minds and Machines
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes
Journal of Computational Neuroscience
Continuous real-world inputs can open up alternative accelerator designs
Proceedings of the 40th Annual International Symposium on Computer Architecture
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From the Publisher:For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.