Context or stimulus dependent transient activity of large groups of
neurons that are able to organize reproducible order in thoughts and
actions is one of the most challenging problems to disclose the dynamical
mechanisms of animal cognition and behavior. Traditional
approaches that operate with simple attractors (fixed points and limit cycles)
are not valid for the solution of this problem. Based on
experiments on olfactory information encoding (locust and bee), and on the the
hunting behavior of the marine mollusk Clione, we discuss the
principles of dynamical modeling that help to understand the origin of transient
activity in complex neural ensembles (fast odor recognition,
in particular). In this context, we formulate the Winnerless Competition (WLC)
principle. The main point of this principle is the transformation
of an incoming identity code or spatial code into ensemble (spatio)-temporal
output based on the intrinsic transient dynamics of
the neural network (Antennal Lobe - AL, for example). In the presence of stimuli,
the AL transient activity, whose geometrical image in the
phase space of an AL dynamical model is a heteroclinic sequence, uniquely
depends on incoming information. Together with the results of computer
modeling of networks with different levels of complexity, we present rigorous
results about the robustness and reproducibility of the WLC
dynamics and discuss the advantages of coding and processing neural information
with transients, i.e., heteroclinic sequences.
Mikhail I. Rabinovich
Mikhail I. Rabinovich del Institute for Nonlinear Science, Universidad
de California, San Diego, es miembro de la Academia de Ciencias de
Rusia. Ha escrito más de 250 artículos científicos y 14 libros, y es reconocido
internacionalemente como uno de los máximos expertos en
sistemas dinámicos. Durante los últimos 15 años su investigación se ha
desarrollado en el contexto de la Neurociencia Computacional