Central pattern generating and recognizing in olfactory bulb: A correlation learning rule

Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons

“…In this framework, a pattern is learned when the complex (or chaotic) dynamics of the network settles onto a periodic oscillatory regime (a limit cycle) that is specific of the input pattern. This behavior emulates putative mechanisms of odor learning in rabbits that have been put forward by physiologists such as W. Freeman [18,19]. An important functional aspect is that removal of the learned pattern after learning should lead to a significative change in the network dynamics.…”

Nonlinear data analysis of experimental (EEG) data and comparison with theoretical (ANN) data

“…Also the dimensionality (low or high) of EEG data depends on the channel selected or the exact experimental conditions. This is well supported from that fact that the EEG signals can be a mixture of nonlinear deterministic oscillation and linear stochasticity [12] or possibility of low-dimensional phenomena in EEG of olfactory bulb [19].…”

“…The view of the EEG as a largely stochastic system was challenged during the 1980's with the publication of both data analysis and models of the EEG [18,19] that were inspired by "chaos theory. "…”

Time delays and stimulus-dependent pattern formation in periodic environments in isolated neurons

“…Limit cycles, strange attractors and other dynamical phenomena have been used by many authors to represent encoded temporal patterns as associative memories [7], [22], [4], [17], [14]. Most of the existing literature on theoretical studies of artificial neural networks is predominantly concerned with autonomous systems containing temporally uniform network parameters and external input stimuli.…”