The neural control of fish swimming studied through numerical simulations
Adaptive Behavior - Special issue on computational neuroethology
Statistically efficient estimation using population coding
Neural Computation
Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Autoregressive model of the hippocampal representation of events
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Population models of temporal differentiation
Neural Computation
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In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and they are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically.