A biologically plausible learning rule for the Infomax on recurrent neural networks.
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Authors retain ownership of the copyright for their Frontiers article, but it allows anyone to download, reuse, reprint, modify, distribute, and/or copy their article (or parts of it), so long as the original authors and original publication in this journal are cited, and subject to any third-party copyright notices. It is fine to republish the articles as long as you acknowledge the original publication with FrontiersA 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.
- Frontiers in computational neuroscience
Frontiers in computational neuroscience (8), 2014-11-25