Event Abstract

Spontaneous activity in a self-organizing recurrent network reflects prior learning

  • 1 Frankfurt Institute for Advanced Studies, Germany
  • 2 Max Planck Institute for Brain Research, Germany

In the neocortex, spontaneous activity in the absence of sensory input exhibits nonrandom spatiotemporal patterns [Tsodyks et al., 1999, Fiser et al., 2004]. Following the repetitive presentation of a given visual stimulus, the spontaneous activity patterns show similarities with the sensory-evoked responses [Han et al., 2008]. It has been hypothesized that spontaneous activity reflects prior information, learned via plasticity based on past experience, which when integrated with sensory-evoked activity enables Bayesian inference [Berkes et al., 2009]. We explore the characteristics of spontaneous activity following unsupervised learning of spatio-temporal stimuli in a self-organizing recurrent network (SORN) shaped by synaptic and neuronal plasticity. The SORN model [Lazar et al., 2009] consists of a population of excitatory cells and a smaller population of inhibitory cells. The connectivity among excitatory units is sparse and subject to a simple spike-timing-dependent plasticity rule. Additionally, synaptic normalization keeps the sum of an excitatory neuron's afferent weights constant, while intrinsic plasticity regulates a neuron’s firing threshold to maintain a low average activity level. The network receives input sequences composed of different letters and learns the structure embedded in these sequences in an unsupervised manner. Following a learning interval, we omit the input and analyse the characteristics of spontaneous activity. We find that the network revisits states similar to those embedded during input stimulation and that it follows similar trajectories through its high-dimensional state space. Furthermore we show that the spontaneous activity reflects the statistical properties of the data: during spontaneous activity the network preferentially visits states that are similar to evoked activity patterns for inputs with a higher prior probability. Our results establish a novel link between STDP-based unsupervised learning in recurrent networks and concepts of statistical inference. Acknowledgments: This work was supported by the Hertie Foundation, grant PLICON (EC MEXT-CT-2006-042484), and GABA Project (EU-04330). We thank Sophie Deneve for literature suggestions.

Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.

Presentation Type: Poster Presentation

Topic: Poster session I

Citation: Lazar A, Pipa G and Triesch J (2010). Spontaneous activity in a self-organizing recurrent network reflects prior learning. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00336

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 19 Feb 2010; Published Online: 19 Feb 2010.

* Correspondence: Andreea Lazar, Frankfurt Institute for Advanced Studies, Franffurt am Main, Germany, andreea.lazar@brain.mpg.de