Physical Models of Cognition

Abstract

This chapter presents and discusses physical models for simulating some aspects of neural intelligence, and in particular, the process of cognition. The main departure from the classical approach here is in the utilization of a terminal version of classical dynamics introduced in Chap. 7. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is irreversible and nondeterministic. We pay special attention to terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore, which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamical solutions are exploited. The first one is a stochastic attractor — a stable stationary stochastic process to which random solutions of closed system converge. As a model of cognition, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon (discussed in Chap. 7) is terminal chaos which occurs in open systems.