Hopfield neural network

This post has been taken from http://www.learnartificialneuralnetworks.com/ . The content has been reproduced here for solely for the benefit of students. Kindly acknowledge the original site and not this blog.

One of the earliest recurrent neural networks reported in literature was the auto-associator independently described by Anderson (Anderson, 1977) and Kohonen (Kohonen, 1977) in 1977. It consists of a pool of neurons with connections between each unit i and j, i 6= j.

The auto-associator network. All neurons are both input and output neurons, i.e., a
pattern is clamped, the network iterates to a stable state, and the output of the network consists of
the new activation values of the neurons.

All connections are weighted. In 1982, Hopfield (Hopfield, 1982) brings together several earlier ideas concerning these networks and presents a complete mathematical analysis based on Ising spin models (Amit, Gutfreund, & Sompolinsky, 1986). It is therefore that this network, which we will describe in this chapter, is generally referred to as the Hopfield network.

The Hopfield network consists of a set of N interconnected neurons which update their activation values asynchronously and independently of other neurons. All neurons are both input and output neurons. The activation values are binary. Originally, Hop eld chose activation values of 1 and 0, but using values +1 and -1 presents some advantages discussed below. We will therefore adhere to the latter convention.

The state of the system is given by the activation values1 y = (yk). The net input sk(t+1) of a neuron k at cycle t+1 is a weighted sum

A simple threshold function is applied to the net input to obtain the new activation value yi(t + 1) at time t + 1:

Hopfield network as associative memory

A primary application of the Hopfield network is an associative memory. In this case, the weights of the connections between the neurons have to be thus set that the states of the system corresponding with the patterns which are to be stored in the network are stable. These states can be seen as 'dips' in energy space. When the network is cued with a noisy or incomplete test pattern, it will render the incorrect or missing data by iterating to a stable state which is in some sense 'near' to the cued pattern.

i.e., if xp j and xp k are equal, wjk is increased, otherwise decreased by one (note that, in the original Hebb rule, weights only increase). It appears, however, that the network gets saturated very quickly, and that about 0:15N memories can be stored before recall errors become severe. There are two problems associated with storing too many patterns:

1. the stored patterns become unstable;
2. spurious stable states appear (i.e., stable states which do not correspond with stored patterns).

The first of these two problems can be solved by an algorithm proposed by Bruce et al. (Bruce, Canning, Forrest, Gardner, & Wallace, 1986):

Algorithm 1 Given a starting weight matrix for each pattern xp to be stored and each element xp k in xp de ne a correction εk such that

Now modify wjk by Repeat this procedure until all patterns are stable.

It appears that, in practice, this algorithm usually converges. There exist cases, however, where the algorithm remains oscillatory (try to find one)!