Optimizing Generic Neural Microcircuits through Reward Modulated STDP

How can we characterize if a given neural circuit is optimal for the class of computational operations that it has to perform on a certain input distribution? We show that modifying the efficacies of recurrent synapses in a generic neural microcircuit via spike timing dependent plasticity (STDP) can optimize the circuit in an unsupervised fashion for a particular input distribution if STDP is modulated by a global reward signal. More precisely, optimizing microcircuits through reward modulated STDP leads to a lower eigen-value spread of the cross-correlation matrix, higher entropy; highly decorrelated neural activity, and tunes the circuit dynamics to a regime that requires a large number of principal components for representing the information contained in the liquid state as compared to randomly drawn microcircuits. Another set of results show that such optimization brings the mean firing rate into a realistic regime, while increasing the sparseness and the information content of the network. We also show that the performance of optimized circuits improves for several linear and non-linear tasks.