Analysis of the stabilized supralinear network

Details Analysis of the stabilized supralinear network Analysis of the stabilized supralinear network

2012-02-29

arxiv.org/abs/1202.6670v1

Biology

Quantitative Biology

Neurons and Cognition

29 pages, 2 figures

Scientific paper

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. 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 2-dimensional case of 1 excitatory and 1 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 show that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.

Affiliated with

Also associated with

No associations

Rating

Analysis of the stabilized supralinear network does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Analysis of the stabilized supralinear network, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Analysis of the stabilized supralinear network will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-345246