Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

We demonstrate the ability of a randomly connected attractor network with dynamic synapses to discriminate between similar sequences containing multiple stimuli and suggest such networks provide a general basis for neural computations in the brain. The network is based on units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections within each unit. Such excitatory feedback to a unit can generate bistability, though in many networks only under conditions of net excitatory input from other units. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the robust general purpose computational engines needed for us to solve many cognitive tasks.