Global dynamics of a network of stochastic neurons maximizes local mutual information

We define a stochastic neuron as an element that increases its internal sta te with probability p until a threshold value is reached; after that its in ternal state is set back to the initial value. We study the local informati on of a stochastic neuron between the message arriving from the input neuro ns and the response of the neuron. We study the dependence of the local inf ormation on the firing probability ct of the synaptic inputs in a network o f such stochastic neurons. The values of cr obtained in the simulations are the same as those obtained theoretically by maximization of local mutual i nformation. We conclude that the global dynamics maximizes the local mutual information of single units, which means that the self-selected parameter value of the population dynamics is such that each neuron behaves as an opt imal encoder.