title: SORN: a self-organizing recurrent neural network.
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.
authors.affiliation: Frankfurt Institute of Advanced Studies, Johann Wolfgang Goethe University Frankfurt am Main, Germany. lazar@fias.uni-frankfurt.de
authors.forename: Andreea
authors.initials: A
authors.lastname: Lazar
authors.forename: Gordon
authors.initials: G
authors.lastname: Pipa
authors.forename: Jochen
authors.initials: J
authors.lastname: Triesch