We propose and analyze analog VLSI implementations of neural networks in which both the neural cells and the synapses are realized using Operational Transconductance Amplifiers (OTAs). These circuits have inherent advantages of immunity to noise, very high input/output impedances, differential architecture with automatic inversion, and density. An efficient on-chip technique for weight adaptati...

This chapter is devoted to the analysis of the complex dynamics exhibited by twodimensional discrete-time delayed Hopfield-type neural networks. Since the pioneering work of (Hopfield, 1982; Tank & Hopfield, 1986), the dynamics of continuous-time Hopfield neural networks have been thoroughly analyzed. In implementing the continuous-time neural networks for practical problems such as image proce...

A hybrid Neural-Genetic algorithm (NG) is presented for FPGA Segmented Channel Routing Problems (FSCRP). The NG algorithm consists in a Hopfield Neural Network (HNN) which manages the problem constraints, hybrided with a Genetic Algorithm (GA) for improving the solutions obtained. Six hard FSCRP instances have been generated in order to test the performance of the NG algorithm. Key-Words: FPGAs...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

We rigorously establish a close relationship between message passing algorithms and models of neurodynamics by showing that the equations of a continuous Hopfield network can be derived from the equations of belief propagation on a binary Markov random field. As Hopfield networks are equipped with a Lyapunov function, convergence is guaranteed. As a consequence, in the limit of many weak connec...

In this paper, some sufficient conditions for the local and global exponential stability of the discrete-time Hopfield neural networks with general activation functions are derived, which generalize those existing results. By means of M-matrix theory and some inequality analysis techniques, the exponential convergence rate of the neural networks to the equilibrium is estimated, and for the loca...