Neural Comput - Analysis of the stabilized supralinear network.

Tópicos

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Resumo

We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear input-output function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong. For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs. We compare this to the dynamic stabilization in the balanced network, which yields only linear behavior. We more exhaustively analyze the two-dimensional case of one excitatory and one inhibitory population. We show that in this case, dynamic stabilization will occur whenever the determinant of the weight matrix is positive and the inhibitory time constant is sufficiently small, and analyze the conditions for supersaturation, or decrease of firing rates with increasing stimulus contrast (which represents increasing input firing rates). In work to be presented elsewhere, we have found that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.

Resumo Limpo

studi ratemodel neural network compos excitatori inhibitori neuron neuron inputoutput function power law power greater observ primari visual cortex supralinear inputoutput function lead supralinear summat network respons multipl input weak input show stronger input drive excitatori subnetwork instabl network will dynam stabil provid feedback inhibit suffici strong wide rang network stimulus paramet dynam stabil yield transit supralinear sublinear summat network respons multipl input compar dynam stabil balanc network yield linear behavior exhaust analyz twodimension case one excitatori one inhibitori popul show case dynam stabil will occur whenev determin weight matrix posit inhibitori time constant suffici small analyz condit supersatur decreas fire rate increas stimulus contrast repres increas input fire rate work present elsewher found transit supralinear sublinear summat can explain wide varieti nonlinear cerebr cortic process

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