Volume 10 Supplement 1

Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access

Control of the temporal interplay between excitation and inhibition by the statistics of visual input

  • Jens Kremkow1, 2Email author,
  • Laurent Perrinet1,
  • Cyril Monier3,
  • Yves Frégnac3,
  • Guillaume S Masson1 and
  • Ad Aertsen2, 4
BMC Neuroscience200910(Suppl 1):O21

DOI: 10.1186/1471-2202-10-S1-O21

Published: 13 July 2009

Introduction

In the primary visual cortex (V1), single cell responses to simple visual stimuli (drifting gratings) are usually strong but with a high trial-by-trial variability. In contrast, when exposed to full field natural scenes with simulated eye movements, the firing patterns of these neurons are sparse but highly reproducible over trials [1]. So far the mechanisms behind these two distinct different response behaviours are not yet fully understood. Different mechanisms are candidates for controlling spike timing precision and models are needed to clarify their respective contribution, which may be of thalamic or intracortical origin. As a first step, we investigated which aspects of the neuronal dynamics can be explained by balanced feedforward excitation and inhibition and its dependency upon the spatio-temporal statistics of the different stimuli. We built a simple model of the early visual system (LGN, V1). The thalamocortical connectivity was similar to the gpush-pullh architecture used in [2], with additional depressing thalamocortical synapses [3]. The model was written in PyNN [4] using NEST [5] as simulator. Indeed, the model can reproduce the main response characteristics of first-order thalamo-cortical neurons in layer 4 of cat V1. During drifting gratings, excitatory and inhibitory conductances of cortical neurons were anti-correlated [6, 7], such that excitation can be freely integrated and induce multiple spikes. In contrast, during natural scenes the conductances were correlated, with inhibition lagging by few milliseconds [1, 8]. This small lag between excitation and inhibition induces a strong selectivity to synchronous inputs, with a consequence that the responses became sparse and precise. However, some key aspects of the in vivo recordings in cat area V1 cannot be explained, such as selective reduction of stimulus-locked subthreshold noise during natural scene viewing, precise firing during fixational eye-movements and center-surround non-linearities, opening the door for future investigation about the role of intra-cortical recurrent connectivity in further shaping the neuronal responses to natural images. In conclusion, our study points that correlated inhibition can explain, at least in part, sparse and reliable spiking activity as observed in response to natural scenes. This is consistent with its role reported from other sensory modalities and cortical areas [8]. Thus correlated excitation and inhibition could be a general mechanism to induce sparse and precise spiking in the nervous system.

Declarations

Acknowledgements

This work was supported by the CNRS, the Agence Nationale de la Recherche (ANR – Natstats), the 6th RFP of the EU (grant no. 15879-FACETS) and by the German Federal Ministry of Education and Research (BMBF grant 01GQ0420 to BCCN, Freiburg).

Authors’ Affiliations

(1)
INCM, CNRS-Aix-Marseille University
(2)
Neurobiology and Biophysics, University of Freiburg
(3)
UNIC, CNRS
(4)
Bernstein Center for Computational Neuroscience

References

  1. Fregnac Y, Baudot P, Levy M, Marre O: An intracellular view of time coding and sparseness in V1 during virtual oculomotor exploration of natural scenes. Cosyne. 2005, 17.Google Scholar
  2. Troyer T, Krukowski A, Proebe N, Miller K: Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J Neurosci. 1998, 18: 5908-5927.PubMedGoogle Scholar
  3. Banitt Y, Martin KAC, Segev I: A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression. J Neurosci. 2007, 27: 10230-10239. 10.1523/JNEUROSCI.1640-07.2007.PubMedView ArticleGoogle Scholar
  4. Davison A, Brüderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P: PyNN: a common interface for neuronal network simulators. Front Neuroinform. 2008, 2: 11-10.3389/neuro.11.011.2008.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Gewaltig MO, Diesmann M: NEST (NEural Simulation Tool). Scholarpedia. 2007, 2: 1430.View ArticleGoogle Scholar
  6. Anderson JS, Carandini M, Ferster D: Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. J Neurophysiol. 2000, 84: 909-926.PubMedGoogle Scholar
  7. Monier C, Fournier J, Frégnac Y: In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices. J Neurosci Methods. 2008, 169: 323-365. 10.1016/j.jneumeth.2007.11.008.PubMedView ArticleGoogle Scholar
  8. Okun M, Lampi I: Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci. 2008, 11: 535-537. 10.1038/nn.2105.PubMedView ArticleGoogle Scholar

Copyright

© Kremkow et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

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