AUTHOR=Lazar Andreea , Pipa Gordon , Triesch Jochen TITLE=SORN: a self-organizing recurrent neural network JOURNAL=Frontiers in Computational Neuroscience VOLUME=3 YEAR=2009 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/neuro.10.023.2009 DOI=10.3389/neuro.10.023.2009 ISSN=1662-5188 ABSTRACT=
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.