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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Irregularity of emergent network activity in the local circuit

Francesca Barbieri1*, Maria V Sanchez-Vives12 and Albert Compte1

Author Affiliations

1 Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona,, Barcelona, Spain

2 Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Barcelona, Spain

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BMC Neuroscience 2009, 10(Suppl 1):P333  doi:10.1186/1471-2202-10-S1-P333


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/10/S1/P333


Published:13 July 2009

© 2009 Barbieri et al; licensee BioMed Central Ltd.

Poster presentation

In recent years, there has been in easing interest in determining the statistics of firing in local networks that generate self-sustained activity, and its mechanistics bstrate. Two phenomena thought to be generated by reverberation in the local recurrent circuitry are persistent activity underlying working memory and slow oscillatory activity during slow-wave sleep or anaesthesia. Neurophysiological experiments on awake monkeys have reported highly irregular persistent activity during the performance of an oculomotor delayed response ask [1]. In these experiments, the coefficient of variation (CV) for interspike intervals of prefrontal neurons during the delay period is above 1, reflecting highly irregular firing. Recent modeling studies [2,3] have proposed different mechanisms that can reproduce this irregularity of the persistent state.

On the other hand, a biophysical network model of slow oscillations that reproduces both single neuron as well as collective network firing patterns observed in vitro has been proposed [4]. As persistent activity in the working memory model, in this network the up state is maintained by strong recurrent excitation balanced with inhibition, but here an intrinsic slow adaptation current produces spontaneous transitions between up and down states. In the framework of bistable networks, while it is now evident that working memory activity generated in the local circuit can be quite irregular [2,3], an experimental and theoretical analysis of the statistics of firing during the up state of slow oscillations is still pending.

In order to address this issue, we analyzed data from the following studies, (1) in vitro recordings that show slow oscillatory activity [5]. These recordings allow evaluation of the irregularity of reverberant activity in the local circuit in the absence of sources of noise from outside the microcircuit; (2) in vivo recordings from anaesthetized animals showing slow oscillations. The comparison with (1) allows the evaluation of the influence of external inputs on the irregularity of reverberant activity in the microcircuit. We compare and interpret the experimental results based on biophysical simulations of three different networks, (i) Ring model with selective excitatory neurons and inhibition, which displays highly regular persistent activity [6]; (ii) Ring model endowed with short-term depression mechanism and high reset potential, which displays irregular persistent activity [2]; (iii) Biophysical model of slow oscillations [4].

References

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  2. Barbieri F, Brunel N: Irregular persistent activity induced by excitatory feedback.

    Frontiers in Computational Neuroscience 2007, 1:5. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Roudi Y, Latham PE: A balanced memory network.

    PLOS Comp Biol 2007, 3:1679-700. Publisher Full Text OpenURL

  4. Compte A, Sanchez-Vives MV, McCormick D, Wang XJ: Cellular and network mechanism of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model.

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  5. Sanchez-Vives MV, McCormick D: Cellular and network mechanisms of rhythmic recurrent activity in cortex.

    Nat Neuroscience 2000, 3:1027-1034. Publisher Full Text OpenURL

  6. Compte A, Brunel N, Goldman-Rakic PS, Wang XJ: Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.

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