  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 has an analogy in
classical electrostatics, by which the notion of force and potential energy has
been introduced. Since the model takes input from each neuron in a network and
produces a behavior as the output, it would be extremely difficult or may even
be impossible to implement. But with the help of the model a possible
explanation for an hitherto unexplained neurological observation in human brain
has been offered. The model is compatible with a recent model of sequential
behavioral dynamics. The model is based on electrophysiology, but its relevance
to hemodynamics has been outlined.
