Network capacity for latent attractor computation

We (1999) have proposed a paradigm called "latent attractors" where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus-a brain region of fundamental significance for memory and spatial learning. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. Following methods developed for associative memory networks, we present analytical and computational results on the capacity of latent attractor networks.