Abstract
Neural information processing is extremely complicated. A core challenge in theoretical neuroscience is to develop properly simplified models, which, on one hand, capture the fundamental features of the complex systems, and on the other hand, allow us to pursue analytic treatments. In the present study, we aim to develop simple models for synaptic information integration. We use simple current-based models to approximate the dynamics of conductance-based multi-compartment ones. The nonlinear shunting inhibition is expressed as a product between the contributions of excitatory and inhibitory currents, and its strength depends on the spatial configuration of excitatory and inhibitory inputs, agreeing with the experimental data. We expect that the current study will serve as a building brick for analyzing the dynamics of large-size networks.
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Zhang, D., Cui, Y., Li, Y., Wu, S. (2011). Simple Models for Synaptic Information Integration. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_23
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DOI: https://doi.org/10.1007/978-3-642-24965-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
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