Bifurcation analysis in an associative memory model

Convergence results in an associative memory model

-This paper presents rigorous mathematical proofs for some observed convergence phenomena in an associative memory model introduced by Hopfield (based on Hebbian rules)for storing a number o f random n-bit patterns. The capability o f the model to correct a linear number o f random errors in a bit pattern has been established earlier, but the existence of a large domain o f attraction (correcti...

Implicant network: an associative memory model

The Implicant Network is a neural network model capable of storing an arbitrary boolean function . The difference from previous one-shot learning models is that the training algorithm compresses the positive set online with linear time and space requirements. The algorithm works by building a Sum Of Products (SOP) representation of the positive set as it is presented to the network. Since the m...

An associative analysis of object memory

Different aspects of recognition memory in rodents are commonly assessed using variants of the spontaneous object recognition procedure in which animals explore objects that differ in terms of their novelty, recency, or where they have previously been presented. The present article describes three standard variants of this procedure, and outlines a theory of associative learning, SOP which can ...

Stability and bifurcation in an autoassociative memory model

Effect of Connectivity in an Associative Memory Model

We investigate how geometric properties translate into functional properties in sparse networks of computing elements. Specifically, we determine how the eigenvalues of the interconnection graph (which in turn reflect connectivity properties) relate to the quantities, number of items stored, amount of error-correction, radius of attraction, and rate of convergence, in an associative memory mode...