Summary
When designing a discrete time simulation tool for neuronal networks, conceptual difficulties are often encountered in defining the interaction between the continuous dynamics of the neurons and the point events (spikes) they exchange. These problems increase significantly when the tool is designed to be distributed over many computers. In this chapter, we bring together the methods that have been developed over the last years to handle these difficulties. We describe a framework in which the temporal order of events within a simulation remains consistent. It is applicable to networks of neurons with arbitrary subthreshold dynamics, both with and without delays, exchanging point events either constrained to a discrete time grid or in continuous time, and is compatible with distributed computing.
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Morrison, A., Diesmann, M. (2007). Maintaining Causality in Discrete Time Neuronal Network Simulations. In: Graben, P.b., Zhou, C., Thiel, M., Kurths, J. (eds) Lectures in Supercomputational Neurosciences. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73159-7_10
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DOI: https://doi.org/10.1007/978-3-540-73159-7_10
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