Recurrent infomax generates cell assemblies, avalanches, and simple cell-like selectivity

Through evolution, animals have acquired central nervous systems (CNSs), which are extremely efficient information processing devices that improve an animal’s adaptability to various environments. It has been proposed that the process of information maximization (infomax1), which maximizes the information transmission from the input to the output of a feedforward network, may provide an explanation of the stimulus selectivity of neurons in CNSs2–7. However, CNSs contain not only feedforward but also recurrent synaptic connections, and little is known about information retention over time in such recurrent networks. Here, we propose a learning algorithm based on infomax in a recurrent network, which we call “recurrent infomax” (RI). RI maximizes information retention and thereby minimizes information loss in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells