Randomly connected sigma-pi neurons can form associative memories

Randomly Connected Sigma-Pi Neurons Can Form . . .

Adaptive critic for sigma-pi networks

-This article presents an investigation which studied how training o f sigma-pi networks with the associative reward-penalty ( A R-p ) regime may be enhanced by using two networks in parallel. The technique uses what has been termed an unsupervised "'adaptive critic element" (ACE) to give critical advice to the supervised sigma-pi network. We utilise the conventions that the sigma-pi neuron mod...

Star Cellular Neural Networks for Associative and Dynamic Memories

In this paper, we propose a Star cellular neural network (Star CNN) for associative and dynamic memories. A Star CNN consists of local oscillators and a central system. All oscillators are connected to a central system in the shape of a Star, and communicate with each other through a central system. A Star CNN can store and retrieve given patterns in the form of synchronized chaotic states with...

Connected Cortical Recurrent Networks

A model of an associative memory composed of many mod ules working as attractor neural networks with features of biological re alism is proposed and analyzed using standard statistical physics tech niques The memories of the system are stored in the synapses between neurons in the same module and the synapses between neurons in di er ent modules provide the associations between these memories A...

Lp approximation of Sigma-Pi neural networks

A feedforward Sigma-Pi neural network with a single hidden layer of m neurons is given by mSigma(j=1) cjg (nPi(k=1) xk-thetak(j)/lambdak(j)) where cj, thetak(j), lambdak are elements of R. In this paper, we investigate the approximation of arbitrary functions f: Rn-->R by a Sigma-Pi neural network in the Lp norm. An Lp locally integrable function g(t) can approximate any given function, if and ...