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Front Comput Neurosci. 2009 Oct 30;3:23. doi: 10.3389/neuro.10.023.2009. eCollection 2009.

SORN: a self-organizing recurrent neural network.

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Frankfurt Institute of Advanced Studies, Johann Wolfgang Goethe University Frankfurt am Main, Germany.


Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.


intrinsic plasticity; recurrent neural networks; reservoir computing; synaptic plasticity; time series prediction

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