Robust exponential binary pattern storage in Little-Hopfield networks
نویسندگان
چکیده
The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-points of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a longstanding open problem whether robust exponential storage of binary patterns was possible in such a network memory model. In this note, we design elementary families of Little-Hopfield networks that solve this problem affirmatively.
منابع مشابه
Robust exponential Little-Hopfield network storage
The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-states of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a long-standing open problem whether robust exp...
متن کاملEfficient and optimal Little-Hopfield auto-associative memory storage using minimum probability flow
We present an algorithm to store binary memories in a Little-Hopfield neural network using minimum probability flow, a recent technique to fit parameters in energy-based probabilistic models. In the case of memories without noise, our algorithm provably achieves optimal pattern storage (which we show is at least one pattern per neuron) and outperforms classical methods both in speed and memory ...
متن کاملRobust Exponential Memory in Hopfield Networks
The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministi...
متن کاملEfficient and optimal binary Hopfield associative memory storage using minimum probability flow
We present an algorithm to store binary memories in a Hopfield neural network using minimum probability flow, a recent technique to fit parameters in energybased probabilistic models. In the case of memories without noise, our algorithm provably achieves optimal pattern storage (which we show is at least one pattern per neuron) and outperforms classical methods both in speed and memory recovery...
متن کاملLMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays
This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.
متن کامل