title: A biologically plausible learning rule for the Infomax on recurrent neural networks.
abstract: A fundamental issue in neuroscience is to understand how neuronal circuits in the cerebral cortex play their functional roles through their characteristic firing activity. Several characteristics of spontaneous and sensory-evoked cortical activity have been reproduced by Infomax learning of neural networks in computational studies. There are, however, still few models of the underlying learning mechanisms that allow cortical circuits to maximize information and produce the characteristics of spontaneous and sensory-evoked cortical activity. In the present article, we derive a biologically plausible learning rule for the maximization of information retained through time in dynamics of simple recurrent neural networks. Applying the derived learning rule in a numerical simulation, we reproduce the characteristics of spontaneous and sensory-evoked cortical activity: cell-assembly-like repeats of precise firing sequences, neuronal avalanches, spontaneous replays of learned firing sequences and orientation selectivity observed in the primary visual cortex. We further discuss the similarity between the derived learning rule and the spike timing-dependent plasticity of cortical neurons.
authors.affiliation: Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University Kyoto, Japan ; CREST, Japan Science and Technology Agency Kawaguchi, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Hayakawa
authors.affiliation: Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University Kyoto, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kaneko
authors.affiliation: CREST, Japan Science and Technology Agency Kawaguchi, Japan ; Department of Applied Analysis and Complex Dynamics, Graduate School of Informatics, Kyoto University Kyoto, Japan.
authors.forename: Toshio
authors.initials: T
authors.lastname: Aoyagi
keywords: biologically plausible learning rule; information maximization; neuronal avalanche; orientation selectivity; precise firing sequence; recurrent neural network; spike-timing-dependent plasticity