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Natural memories are associative, declarative and distributed. Symboliccomputing memories resemble natural memories in their declarative character,and information can be stored and recovered explicitly; however, they lack theassociative and distributed properties of natural memories. Sub-symbolicmemories developed within the connectionist or artificial neural networksparadigm are associative and distributed, but are unable to express symbolicstructure and information cannot be stored and retrieved explicitly; hence,they lack the declarative property. To address this dilemma, we useRelational-Indeterminate Computing to model associative memory registers thathold distributed representations of individual objects. This mode of computinghas an intrinsic computing entropy which measures the indeterminacy ofrepresentations. This parameter determines the operational characteristics ofthe memory. Associative registers are embedded in an architecture that mapsconcrete images expressed in modality-specific buffers into abstractrepresentations, and vice versa, and the memory system as a whole fulfills thethree properties of natural memories. The system has been used to model avisual memory holding the representations of hand-written digits, andrecognition and recall experiments show that there is a range of entropyvalues, not too low and not too high, in which associative memory registershave a satisfactory performance. The similarity between the cue and the objectrecovered in memory retrieve operations depends on the entropy of the memoryregister holding the representation of the corresponding object. Theexperiments were implemented in a simulation using a standard computer, but aparallel architecture may be built where the memory operations would take avery reduced number of computing steps.

An Entropic Associative Memory