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This article is part of the supplement: Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access Oral presentation

Inhibitory STDP generates inverse models through detailed balance

Maren Westkott, Christian Albers and Klaus Pawelzik*

Author affiliations

Institute for Theoretical Physics, University of Bremen, 28359 Bremen, Germany

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Citation and License

BMC Neuroscience 2013, 14(Suppl 1):O3  doi:10.1186/1471-2202-14-S1-O3

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/14/S1/O3


Published:8 July 2013

© 2013 Westkott et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Oral presentation

In the song bird ('backward') mappings from sensory representations to motor areas recently were proposed that would 'postdict' the motor activations during singing. Such a sensor-motor mapping represents an inverse model of the motor-sensor-loop passing through the world and thereby can explain the impressive imitation capabilities of song birds [1].

The neurobiological mechanisms that might generate, fine tune and continuously adapt such inverse models, however, are not known. Here we show that spike timing dependent plasticity (STDP) of the inhibitory synapses is sufficient for the self-organisation of the inverse model in a simple closed loop motor-sensor-motor system [2].

Similar to the case of forward models, where predictable, self-generated inputs become suppressed [3], the proposed mechanism generates sparse motor activities by cancelling predictable fluctuations of the neurons' excitabilities. Our results show that inverse mappings can be learned with an elementary and biologically plausible learning rule and thus could underly imitation learning. In our presentation we will discuss also the potential relevance of this mechanism for the operation state of recurrent networks as e.g. cortex [4].

References

  1. Hahnloser R, Ganguli S: Vocal learning with inverse models. Edited by S. Panzeri, P. Quiroga. Principles of Neural Coding. CRC Taylor and Francis; 2013.

  2. Haas J, Nowotny T, Abarbanel HD: Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex.

    J Neurophysiol 2006, 96:3305-3313. PubMed Abstract | Publisher Full Text OpenURL

  3. Bell CC, Han VZ, Sugawara Y, Grant K: Synaptic plasticity in a cerebellum-like structure depends on temporal order.

    Nature 1997, 387:278-281. PubMed Abstract | Publisher Full Text OpenURL

  4. Okun M, Lampl I: Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities.

    Nature Neurosci 2008, 11(5):535-537. PubMed Abstract | Publisher Full Text OpenURL