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Our group is developing a general computational framework for modeling the function of complex neural ensembles. The framework is grounded in the well-established principles of signal processing, statistical inference, and good engineering design. It provides a rational and robust strategy for simulating and evaluating the function of a wide variety of specific neural circuits. We are currently applying this framework to specific projects in sensory processing, motor control, and cognitive function. These modeling efforts are carried out in collaboration with several experimental neuroscience groups at Washington University.

Specific features of our modeling approach:

• Provides a novel characterization of population-temporal coding that combines population vectors with linear decoding of neural spikes;
• Unifies linear control theory with biologically plausible models of neural function;
• Provides a general way to generate circuits that have analytically determined synaptic weights that provide the desired functionality;
• Promotes the formulation of specific hypotheses about circuit function and about key design constraints.

For more information, see:

• Introduction - A general overview of the assumptions, approach, and central results of our framework.
• Framework - A concise statement of the framework and the methodology that it results in.
• Examples - Some examples of applications of the framework.
• Publications - A selection of publications of related work.
• Book Info - Basic information on our book that details the framework and its application.
• Code Library - A set of Matlab® functions that can be used to build simulations based on our framework.

As a general overview, you may wish to look at the workshop materials we used recently at CNS*2003.

Computational neuroscience related courses at the University of Waterloo.

 

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