Definition
Population density models are reduced descriptions of neuronal ensembles, which consist in characterizing them by the instantaneous distribution of some neuronal variables over the population. The simplest and most useful form only uses the neuron membrane potentials. The distribution of membrane potentials and its dynamical evolution can be exactly computed in different cases, for noninteracting neurons submitted to time-varying stochastic inputs. This provides the basis for the most interesting application of population density models, namely, the quantitative description of the dynamics of networks of coupled spiking neurons. The approach has in particular been used to analyze neural rhythms.
Introduction
Cognitive phenomena depend on the concerted activity of large populations of neurons. Understanding the dynamics of such systems has been one of the major goals of theoretical neuroscience. Various methods have been developed to tackle this problem. These methods range...
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Notes
- 1.
The important case of excitation-inhibition balance in a multi-population network, which allows for stronger synapses and fluctuations in the noise variance, is discussed in section “Strong Coupling Regime.”
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Brunel, N., Hakim, V. (2013). Population Density Models. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_74-1
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