A Model for Complex Sequence Learning and Reproduction in Neural Populations

Temporal patterns of activity which repeat above chance level in the brains of vertebrates and in the mammalian neocortex have been reported experimentally. This temporal structure is thought to subserve functions such as movement, speech, and generation of rhythms. Several studies aim to explain how particular sequences of activity are learned, stored, and reproduced. The learning of sequences is usually conceived as the creation of an excitation pathway within a homogeneous neuronal population, but models embodying the autonomous function of such a learning mechanism are fraught with concerns about stability, robustness, and biological plausibility. We present two related computational models capable of learning and reproducing sequences which come from external stimuli. Both models assume that there exist populations of densely interconnected excitatory neurons, and that plasticity can occur at the population level. The first model uses temporally asymmetric Hebbian plasticity to create excitation pathways between populations in response to activation from an external source. The transition of the activity from one population to the next is permitted by the interplay of excitatory and inhibitory populations, which results in oscillatory behavior that seems to agree with experimental findings in the mammalian neocortex. The second model contains two layers, each one like the network used in the first model, with unidirectional excitatory connections from the first to the second layer experiencing Hebbian plasticity. Input sequences presented in the second layer become associated with the ongoing first layer activity, so that this activity can later elicit the the presented sequence in the absence of input. We explore the dynamics of these models, and discuss their potential implications, particularly to working memory, oscillations, and rhythm generation.