  In this paper a model of neural circuit in the brain has been proposed which
is composed of cyclic sub-circuits. A big loop has been defined to be
consisting of a feed forward path from the sensory neurons to the highest
processing area of the brain and feed back paths from that region back up to
close to the same sensory neurons. It has been mathematically shown how some
smaller cycles can amplify signal. A big loop processes information by contrast
and amplify principle. It has been assumed that the spike train coming out of a
firing neuron encodes all the information produced by it as output. This
information over a period of time can be extracted by a Fourier transform. The
Fourier coefficients arranged in a vector form will uniquely represent the
neural spike train over a period of time. The information emanating out of all
the neurons in a given neural circuit over a period of time will be represented
by a collection of points in a multidimensional vector space. This cluster of
points represents the functional or behavioral form of the neural circuit. It
has been proposed that a particular cluster of vectors as the representation of
a new behavior is chosen by the brain interactively with respect to the memory
stored in that circuit and the synaptic plasticity of the circuit. It has been
proposed that in this situation a Coulomb force like expression governs the
dynamics of functioning of the circuit and stability of the system is reached
at the minimum of all the minima of a potential function derived from the force
like expression. The calculations have been done with respect to a pseudometric
defined in a multidimensional vector space.
