Behavioral response to strong aversive stimuli: A neurodynamical model
Kaushik Majumdar

TL;DR
This paper introduces a neurodynamical model of neural circuit behavior during responses to strong aversive stimuli, using Fourier analysis and electrostatic analogies to explain complex neuronal interactions.
Contribution
It presents a novel theoretical model combining Fourier-based neural behavior analysis with electrostatic analogies to explain neural responses to aversive stimuli.
Findings
Model explains previously unexplained neurological observations.
Compatibility with existing behavioral dynamics models.
Provides a framework linking electrophysiology and hemodynamics.
Abstract
In this paper a theoretical model of functioning of a neural circuit during a behavioral response has been proposed. A neural circuit can be thought of as a directed multigraph whose each vertex is a neuron and each edge is a synapse. It has been assumed in this paper that the behavior of such circuits is manifested through the collective behavior of neurons belonging to that circuit. Behavioral information of each neuron is contained in the coefficients of the fast Fourier transform (FFT) over the output spike train. Those coefficients form a vector in a multidimensional vector space. Behavioral dynamics of a neuronal network in response to strong aversive stimuli has been studied in a vector space in which a suitable pseudometric has been defined. The neurodynamical model of network behavior has been formulated in terms of existing memory, synaptic plasticity and feelings. The model…
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Taxonomy
TopicsNeural dynamics and brain function · EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies
