A biologically plausible learning rule for the Infomax on recurrent neural networks

• Published in: Frontiers in computational neuroscience Vol. 8; p. 143
• Main Authors: Hayakawa, Takashi, Kaneko, Takeshi, Aoyagi, Toshio
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 25.11.2014; Frontiers Media S.A
• Subjects: Avalanches; Circuits; Firing pattern; information maximization; Learning; Nervous system; Neural networks; neuronal avalanches; Neurons; Neuroplasticity; Neuroscience; Orientation behavior; orientation selectivity; precise firing sequences; recurrent neural network; Science; spike-timing-dependent plasticity; Visual cortex; Visual pathways; Japan; information maximization; recurrent neural network; biologically plausible learning rule; precise firing sequence; spike-timing-dependent plasticity; neuronal avalanche; orientation selectivity
• DOI: 10.3389/fncom.2014.00143
• ISSN: 1662-5188

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.