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This article is part of the supplement: Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

Open Access Poster presentation

Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit

Ruggero Scorcioni*, David J Hamilton and Giorgio A Ascoli

Author Affiliations

Center for Neural Informatics, Structure, and Plasticity (CN3); Krasnow Institute for Advanced Study, George Mason University, VA, 22030, USA

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BMC Neuroscience 2008, 9(Suppl 1):P62  doi:10.1186/1471-2202-9-S1-P62


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/9/S1/P62


Published:11 July 2008

© 2008 Scorcioni et al; licensee BioMed Central Ltd.

Introduction

The mammalian hippocampus is involved in spatial representation and memory storage and retrieval, and much research is ongoing to elucidate the cellular and system-level mechanisms underlying these cognitive tasks. Modeling may be useful to link network-level activity patterns to the relevant features of hippocampal anatomy and electrophysiology. Investigating the effects of circuit connectivity requires simulations of a number of neurons close to real scale.

Methods

Here, we construct a model of the hippocampus with 16 distinct neuronal classes [1,2] (including both local and projection cells) and 200,000 individual McCulloch-Pitts neurons[3]. The number of neurons in each class and their interconnectivity are drawn from rat anatomy [1].

Results and Discussion

When the model is run with a simple variation of the McCulloch-Pitts formalism, self-sustaining non-repetitive activity patterns consistently emerge (see Fig 1). Specific firing threshold values are narrowly constrained for each cell class upon multiple runs with different stochastic wiring and initial conditions, yet these values do not directly affect network stability. Analysis of the model at different network sizes demonstrates that a scale reduction of one order of magnitude drastically alters network dynamics, including the variability of the output range, the distribution of firing frequencies, and the duration of self-sustained activity. Moreover, comparing the model to a control condition with an equivalent number of (excitatory/inhibitory balanced) synapses, but removing all class-specific information (i.e. collapsing the network to homogeneous random connectivity) has surprisingly similar effects to downsizing the total number of neurons. The reduced-scale model is also compared directly with integrate-and-fire simulations, which capture considerably more physiological detail at the single-cell level, but still fail to reproduce the full behavioral complexity of the large-scale model. Thus network size, cell class diversity, and connectivity details may all be critical to generate self-sustained non-repetitive activity patterns.

thumbnailFigure 1. Sample total activity in % of total neurons for 200 k neuron network with realistic connectivity. Network receives input only at t = 0 when 70% of its neurons are randomly activated.

Acknowledgements

This work was supported by NIH grants NS39600, AG025633 and NSF grant SGER 0747864.

References

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