We study pulse-coupled neural networks that satisfy only two assumptions: each isolated neuron fires periodically, and the neurons are weakly connected. Each such network can be transformed by a piece-wise continuous change of variables into a phase model, whose synchronization behavior and oscillatory associative properties are easier to analyze and understand. Using the phase model, we can pr...

In this paper a new design procedure for Hopfield associative memories storing grey-scale images is presented. The proposed architecture, with both intra-layer and inter-layer connections, is an evolution of a previous work based on the decomposition of the image with 2L gray levels into L binary patterns, stored in L uncoupled neural networks: that allows to store images with the commonly used...

| We study pulse-coupled neural networks that satisfy only two assumptions: each isolated neuron res periodically, and the neurons are weakly connected. Each such network can be transformed by a piece-wise continuous change of variables into a phase model, whose synchronization behavior and oscillatory associative properties are easier to analyze and understand. Using the phase model, we can pr...

Several models of associative learning predict that stimulus processing changes during association formation. How associative learning reconfigures neural circuits in primary sensory cortex to "learn" associative attributes of a stimulus remains unknown. Using 2-photon in vivo calcium imaging to measure responses of networks of neurons in primary somatosensory cortex, we discovered that associa...

Global asymptotic stability of BAM neural networks with time-varying delays Bao-Zhu ZHAO, Juan HE , Xiu-Hong GUO 1 Foundation department, Sichuan TOP Vocational Institute of Information Technology, Chengdu, Sichuan, China 611743 E-mail: [email protected] Received: 3-03-2012; Accepted: 5-10-2012 *Corresponding author Abstract This paper presents a sufficient condition for the existence, uniqueness...

ABSTRACT In the present paper, an effort has been made to compare and analyze the performance for pattern recalling with conventional hebbian learning rule and with evolutionary algorithm in Hopfield Model of feedback Neural Networks. A set of ten objects has been considered as the pattern set. In the Hopfield type of neural networks of associative memory, the weighted code of input patterns pr...

Fuzzy associative memories (FAMs) belong to the class of fuzzy neural networks (FNNs). A FNN is an artificial neural network (ANN) whose input patterns, output patterns, and/or connection weights are fuzzy-valued [19, 11]. Research on FAM models originated in the early 1990’s with the advent of Kosko’s FAM [35, 37]. Like many other associative memory models, Kosko’s FAM consists of a single-lay...

This paper introduces the concept of parallel distributed computation (PDC) in neural networks, whereby a neural network distributes a number of computations over a network such that the separate computations are not localized in any part of the network. This form of computation is analogous to distributed storage of information as found in distributed associative memories. The paper describes ...

Pattern recognition (recognizing a pattern from inputs) and recall (describing or predicting the inputs associated with a recognizable pattern) are essential for neural-symbolic processing and cognitive capacities. Without them the brain cannot interact with the world e.g.: form internal representations and recall memory upon which it can perform logic and reason. Neural networks are efficient,...

We establish robustness stability results for a specific type of artificial neural networks for associative memories under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system that are the asymptotically stable equilibria of the original unperturbed neural network. The proposed stability analysis tool is th...