  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 (infomax), 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 CNSs. 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 tothat existing in simple
cells of the primary visual cortex (V1). More importantly, we find that without
external input, this network exhibits cell assembly-like and synfire chain-like
spontaneous activity and a critical neuronal avalanche. RI provides a simple
framework to explain a wide range of phenomena observed in in vivo and in vitro
neuronal networks, and it should provide a novel understanding of experimental
results for multineuronal activity and plasticity from an information-theoretic
point of view.
