Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections
By Robert Rosenbaum, Brent Doiron. 2013
111 citations recorded by the source corpus.
Networks of model neurons with balanced recurrent excitation and inhibition produce irregular and asynchronous spiking activity. We extend the analysis of balanced networks to include the known dependence of connection probability on the spatial separation between neurons. In the continuum limit we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. For finite size networks we investigate the pattern forming dynamics arising when balanced conditions are not satisfied. The spatiotemporal dynamics of balanced networks offer new challenges in the statistical mechanics of complex systems.
Subfields: Cognitive Neuroscience, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/882e2615229bfe2d8d96afe0992f29bba06291dd
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In Cognitive Neuroscience
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In Statistical and Nonlinear Physics
Citations in the corpus
- Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…the ratio of excitation to inhibition, i.e. breaking the balance condition that characterizes functional neural networks (Barral and Reyes, 2016; Rosenbaum and Doiron, 2014; van Vreeswijk and Sompolinsky, 1996)– induces a tendency to super-critical propagation of activity, including many large…”