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Through evolution, animals have acquired central nervous systems (CNSs),which are extremely efficient information processing devices that improve ananimal's adaptability to various environments. It has been proposed that theprocess of information maximization (infomax), which maximizes the informationtransmission from the input to the output of a feedforward network, may providean explanation of the stimulus selectivity of neurons in CNSs. However, CNSscontain not only feedforward but also recurrent synaptic connections, andlittle is known about information retention over time in such recurrentnetworks. Here, we propose a learning algorithm based on infomax in a recurrentnetwork, which we call "recurrent infomax" (RI). RI maximizes informationretention and thereby minimizes information loss in a network. We find thatfeeding in external inputs consisting of information obtained from photographsof natural scenes into an RI-based model of a recurrent network results in theappearance of Gabor-like selectivity quite similar tothat existing in simplecells of the primary visual cortex (V1). More importantly, we find that withoutexternal input, this network exhibits cell assembly-like and synfire chain-likespontaneous activity and a critical neuronal avalanche. RI provides a simpleframework to explain a wide range of phenomena observed in in vivo and in vitroneuronal networks, and it should provide a novel understanding of experimentalresults for multineuronal activity and plasticity from an information-theoreticpoint of view.

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