Firing-rate Models for Neural Populations
نویسنده
چکیده
I discuss the construction of models that describe the ring rates of excitatory and inhibitory neurons in biological neural networks. A model is presented that incorporates both slow linear and fast nonlinear inhibition. With the appropriate excitatory-to-excitatory couplings this model can act as an associative memory in which pattern recognition is signalled by resonant ring behavior. Stored memories are represented by xed points of the excitatory and fast inhibitory dynamics. After memory recovery, slow inhibition returns the system to the silent, resting state.
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