Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

Synchronization hubs may arise from strong rhythmic inhibition during gamma oscillations in primary visual cortex

Stefanos E Folias1*, Danko Nikolić23 and Jonathan E Rubin1

Author Affiliations

1 Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA

2 Department of Neurophysiology, Max Planck Institute for Brain Research, Wolfgang Goethe University, 60528 Frankfurt/Main, Germany

3 Frankfurt Institute for Advanced Studies, Wolfgang Goethe University, 60438 Frankfurt/Main, Germany

For all author emails, please log on.

BMC Neuroscience 2011, 12(Suppl 1):P277  doi:10.1186/1471-2202-12-S1-P277

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P277


Published:18 July 2011

© 2011 Folias 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.

Poster presentation

Parallel multiunit recordings from V1 in anesthetized cat were collected during the presentation of random sequences of drifting sinusoidal gratings at 12 fixed orientations while gamma oscillations were present. In agreement with the seminal work [1], most units were orientation selective to varying degrees and synchronization was evident in spike train crosscorrelograms computed between units with similar preferred orientations, particularly during the presentation of optimal stimuli. Interestingly, a subset of units, which we refer to as synchronization hubs, were additionally found to synchronize with units having differing preferred orientations which was consistent with a previous study [2]. Moreover, oscillatory patterning in spike train autocorrelograms was also found to be strongest in units denoted as synchronization hubs, and synchronization hubs also tended to have narrower tuning curves relative to other units.

We used simplified computational models of small networks of V1 neurons to demonstrate that neurons subject to a sufficiently strong level of inhibitory input can function as synchronization hubs. Neurons were endowed either with integrate-and-fire or conductance-based dynamics and each neuron received a combination of excitatory (AMPA) synaptic inputs that were Poisson-distributed and inhibitory (GABA) inputs that were coherent at a gamma-frequency range. If the strength of rhythmic inhibition was increased for a subset of neurons in the network, and excitation was increased simultaneously to maintain a fixed firing rate, then these neurons produced stronger oscillatory patterning in their discharge probabilities. The oscillations in turn synchronized these neurons with other neurons in the network. Importantly, the strength of synchronization increased with neurons of differing orientation preferences even though no direct synaptic coupling existed between the hubs and the other neurons.

Enhanced levels of inhibition account for the emergence of synchronization hubs in the following way: Inhibitory inputs exhibiting a gamma rhythm determine a time window within which a cell is likely to discharge. Increased levels of inhibition narrow down this window further simultaneously leading to (i) even stronger oscillatory patterning of the neuron's activity and (ii) enhanced synchronization with other neurons. This enables synchronization even between cells with differing orientation preferences. Additionally, the same increased levels of inhibition may be responsible for the narrow tuning curves of hub neurons. In conclusion, synchronization hubs may be the cells that interact most strongly with the network of inhibitory interneurons during gamma oscillations in primary visual cortex.

References

  1. Gray C, Singer W: Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex.

    Proc. Nati. Acad. Sci 1989, 86:1698-1702. Publisher Full Text OpenURL

  2. Yu S, Huang D, Singer W, Nikolić D: A small world of neuronal synchrony. Cerebral Cortex.

    2008, 18:2891-2901. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL