Modeling of spiking-bursting neural behavior using two-dimensional map.

By Nikolai F. Rulkov. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

450 citations recorded by the source corpus.

A simple model that replicates the dynamics of spiking and spiking-bursting activity of real biological neurons is proposed. The model is a two-dimensional map that contains one fast and one slow variable. The mechanisms behind generation of spikes, bursts of spikes, and restructuring of the map behavior are explained using phase portrait analysis. The dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. Synchronization regimes for spiking and bursting activities of these maps are studied as a function of coupling strength. It is demonstrated that the results of this model are in agreement with the synchronization of chaotic spiking-bursting behavior experimentally found in real biological neurons.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7dd2f8a100b3b7673d2c2c61e060ba2e8fe42af9

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In Cognitive Neuroscience

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation (2022, methodology): “…neurons we used spiking model identical to the model used in in [14,15] that can be described by the following set of difference equations [68,75,76]: where Vn is the membrane potential, In is a slow dynamical variable describing the effects of slow conductances, and n is a discrete time-step…”
Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit (2015, methodology): “βn and σn describe external influences on the system, such as synaptic inputs (In ); α, σ, and μ are parameters that affect spiking patterns of the model [34, 35].”