In this paper, a generalized model of Cohen-Grossberg neural networks (CGNNs) with time-varying delays and reaction-diffusion term is investigated. By constructing suitable Lyapunov functional, inequality technique and M -matrix theory, some sufficient conditions for global exponential stability of generalized CGNNs with time-varying delays and reaction-diffusion term are obtained. An examples ...

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at le...

Abstract In this article, we investigate exponential lag synchronization results for the Cohen–Grossberg neural networks with discrete and distributed delays on an arbitrary time domain by applying feedback control. We formulate problem using scales theory so that can be applied to any uniform or non-uniform domains. Also, provide a comparison of shows obtained are unified generalize existing r...

We study the probabilistic generative models parameterized by feedforward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unit to those of its Markov blanket. We ...