The influence of strong connections on dynamics of neuron population with long-tailed synaptic weight distribution

Chizhkova, Ekaterina A.; Chizhov, Anton V.

doi:10.1088/1742-6596/661/1/012017

Cited by 1 publication

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References 10 publications

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“…In our experience, as an example, this can be important for networks with a fraction of strong connections, e.g. those with long-tail weight distributions, and only for spontaneous activity, not the one driven by stimulus [ 56 ]. In that work, we compared the effects of different connectivity profiles on the population dynamics of discrete neuron networks.…”

Section: Discussionmentioning

confidence: 99%

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

Chizhov

Graham

2021

PLoS Comput Biol

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A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.

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Section: Discussionmentioning

confidence: 99%