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 (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. 
Takuma Tanaka, Takeshi Kaneko, Toshio Aoyagi