Storage capacity of a Potts-perceptron

The storage capacity of Potts models for semantic memory retrieval

We introduce and analyse a minimal network model of semantic memory in the human brain. The model is a global associative memory structured as a collection of N local modules, each coding a feature, which can take S possible values, with a global sparseness a (the average fraction of features describing a concept). We show that, under optimal conditions, the number cM of modules connected on av...

Storage capacity of the reversed wedge perceptron with binary connections

Storage of correlated patterns in a perceptron

We calculate the storage capacity of a perceptron for correlated Gaussian patterns. We find that the storage capacity αc can be less than 2 if similar patterns are mapped onto different outputs and vice versa. As long as the patterns are in a general position we obtain, in contrast to previous works, that αc ≥ 1 in agreement with Cover’s theorem. Numerical simulations confirm the results. The c...

J ul 1 99 6 Training a perceptron by a bit sequence : Storage capacity

A perceptron is trained by a random bit sequence. In comparison to the corresponding classification problem, the storage capacity decreases to α c = 1.70±0.02 due to correlations between input and output bits. The numerical results are supported by a signal to noise analysis of Hebbian weights.

Reducing a cortical network to a Potts model yields storage capacity estimates

An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct shortand long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local...