POSTER PRESENTATION Open Access Probabilistic computation underlying sequence learning in a spiking attractor memory network

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli but it remains an open issue how neuronal circuits could reli-ably encode such sequences of information. We consider the task of generating and learning spatiotemporal spike patterns in the context of an attractor memory network, in which each memory is stored in a distributed fashion represented by increased firing in pools of excitatory neurons. Excitatory activity is locally modulated by inhi-bitory neurons representing lateral inhibition that gener-ates a type of winner-take-all dynamics. Networks of this type have previously been shown to exhibit switch-ing between a non-coding ground state and low-rate memory state activations displaying gamma oscillations [1]; however, stable sequential associations between dif