Sparse Distributed Memory Using Rank-Order Neural Codes

A variant of a sparse distributed memory (SDM) is shown to have the capability of storing and recalling patterns containing rank-order information. These are patterns where information is encoded not only in the subset of neuron outputs that fire, but also in the order in which that subset fires. This is an interesting companion to several recent works in the neuroscience literature, showing that human memories may be stored in terms of neural spike timings. In our model, the ordering is stored in static synaptic weights using a Hebbian single-shot learning algorithm, and can be reliably recovered whenever the associated input is supplied. It is shown that the memory can operate using only unipolar binary connections throughout. The behavior of the memory under noisy input conditions is also investigated. It is shown that the memory is capable of improving the quality of the data that passes through it. That is, under appropriate conditions the output retrieved from the memory is less noisy than the input used to retrieve it. Thus, this memory architecture could be used as a component in a complex system with stable noise properties and, we argue, it can be implemented using spiking neurons