  Natural memories are associative, declarative and distributed. Symbolic
computing memories resemble natural memories in their declarative character,
and information can be stored and recovered explicitly; however, they lack the
associative and distributed properties of natural memories. Sub-symbolic
memories developed within the connectionist or artificial neural networks
paradigm are associative and distributed, but are unable to express symbolic
structure and information cannot be stored and retrieved explicitly; hence,
they lack the declarative property. To address this dilemma, we use
Relational-Indeterminate Computing to model associative memory registers that
hold distributed representations of individual objects. This mode of computing
has an intrinsic computing entropy which measures the indeterminacy of
representations. This parameter determines the operational characteristics of
the memory. Associative registers are embedded in an architecture that maps
concrete images expressed in modality-specific buffers into abstract
representations, and vice versa, and the memory system as a whole fulfills the
three properties of natural memories. The system has been used to model a
visual memory holding the representations of hand-written digits, and
recognition and recall experiments show that there is a range of entropy
values, not too low and not too high, in which associative memory registers
have a satisfactory performance. The similarity between the cue and the object
recovered in memory retrieve operations depends on the entropy of the memory
register holding the representation of the corresponding object. The
experiments were implemented in a simulation using a standard computer, but a
parallel architecture may be built where the memory operations would take a
very reduced number of computing steps.
