IMPROVING RECALL FROM AN ASSOCIATIVE MEMORY

The associative net as a model of biological associative memory is investigated. Calculating the output pattern retrieved from a partially connected associative net presented with noisy input cues involves several computations. This is complicated by variations in the dendritic sums of the output units due to errors in the cue and differences in input activity and unit usage. The possible implementation of these computations by biological neural machinery is unclear. We demonstrate that a relatively simple transformation can reduce variation in the dendritic sums. This leads to a winners-take-all type of strategy that produces increased recall performance which is equivalent to the more complicated optimal strategy proposed by others. We describe in detail the possible biological implications of our strategies, the novel feature of which ascribes a role to the NMDA and non-NMDA channels found in hippocampal pyramidal cells.