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

作者：Hayakawa Takashi^*; Kaneko Takeshi; Aoyagi Toshio

来源：Frontiers in Computational Neuroscience, 2014, 8: 143.

DOI：10.3389/fncom.2014.00143

• information maximization
• biologically plausible learning rule
• recurrent neural network
• neuronal avalanche
• precise firing sequence
• orientation selectivity
• spike-timing-dependent plasticity

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.

• 出版日期2014-11-25