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Introduction We have developed a network structure, inspired by experimental data on the neural correlates of navigation in the hippocampus [1], that is able to learn and recognize multiple overlapping sequences of symbols. The network relies on an analog of phase precession, the phenomenon in which place cells fire at progressively earlier phases with respect to a local field potential oscillation as the animal moves further into the place field. Following [1], we suppose phase precession serves to compress temporal sequences, ensuring both the appropriate timescale for synaptic plasticity and robustness to variation in symbol presentation rate. One model proposes that phase precession results from the interaction of rhythmic inhibition with excitatory input [2]. We investigate how different assumptions about the nature of the excitatory input and variation in other model parameters affect the robustness of phase precession and sequence learning.

Neural network model for sequence learning based on phase precession