We propose a genetic algorithm for mutually connected neural networks to obtain a higher capacity of associative memory. In Hop eld network as an associative memory system, the memory capacity is at most 15% of the number of neurons. Here we applied our method to the Hop eld network, and obtained the capacity of 33%. We conjectured that this is due to both asymmetry and sparseness of the connec...

In the paper we describe a device based upon logic array principles but which is capable of providing many large functions. A device providing 2 of 2 variables is readily achievable with an evaluation time of the order of tens of milliseconds. By suitable programming the device is capable of emulating the function of binary weighted neural networks. Hence we are currently implementing the ADAM ...

This paper presents a neural model for reliable and fault tolerant transmission in Wireless Sensor Networks based on Bi-directional Associative Memory. The proposed model is an attempt to enhance the performances of both the cooperative and non cooperative Automatic Repeat Request (ARQ) schemes in terms of reliability and fault tolerance. We have also demonstrated the performances of both the s...

This thesis presents a dynamical system approach to learning forward and inverse models in associative recurrent neural networks. Ambiguous inverse models are represented by multi-stable dynamics. Random projection networks, i.e. reservoirs, together with a rigorous regularization methodology enable robust and efficient training of multi-stable dynamics with application to flexible movement con...

We propose learning rules for recurrent neural networks with high-order interactions between some or all neurons. The designed networks exhibit the desired associative memory function: perfect storage and retrieval of pieces of information and/or sequences of information of any complexity.

Many theorists emphasize the role of an “internal model of the world” in directing intelligent behavior. Internal models predict the evolution of the environment by imitating its causal flow. They compute prediction signals and use these prediction signals to form novel associative chains. Formation of such predictive chains may contribute to reasoning and planning. Animals seem to learn and us...

J. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proc. Nat. Acad. Sci., USA, vol. 79, pp. 2254-2258, Apr. 1982. R. J. McEliece, et al., “The Capacity of the Hopfield Associative Memory,” IEEE Transactions on Information Theory, vol. T-33, pp. 461-482, 1987. B. L. Montgomery et al., “Evaluation of the use of Hopfield Neural Network Model as...

It is well known that stability of Hopfield type neural networks plays a very important role in both theoretical research and applications. So, it has been kept on studying in two decades. Stochastic effectiveness to this kind of neural networks has also received a lot of attention (ref. [Liao et al, 1996 A], [Liao et al, 1996 B], [Blythe,S. et al, 2001A] and [Blythe,S. et al, 2001B]). In this ...

J. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proc. Nat. Acad. Sci., USA, vol. 79, pp. 2254-2258, Apr. 1982. R. J. McEliece, et al., “The Capacity of the Hopfield Associative Memory,” IEEE Transactions on Information Theory, vol. T-33, pp. 461-482, 1987. B. L. Montgomery et al., “Evaluation of the use of Hopfield Neural Network Model as...

J. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proc. Nat. Acad. Sci., USA, vol. 79, pp. 2254-2258, Apr. 1982. R. J. McEliece, et al., “The Capacity of the Hopfield Associative Memory,” IEEE Transactions on Information Theory, vol. T-33, pp. 461-482, 1987. B. L. Montgomery et al., “Evaluation of the use of Hopfield Neural Network Model as...