Sequence learning, prediction, and replay in networks of spiking neurons

Our work on extending the temporal memory (TM) component of HTM is now available at [2111.03456] Sequence learning, prediction, and replay in networks of spiking neurons. In this work, we demonstrate that the principle mechanisms underlying sequence learning, prediction, and replay in the TM model can be implemented using continuous-time dynamics with known biological ingredients such as spiking neurons, dendritic action potentials, spike-timing-dependent plasticity, and homeostasis.
By strengthening the link to biology, our implementation permits a more direct evaluation of the TM-model predictions based on experimentally accessible quantities and behavioral data.
All code is available at Sequence learning, prediction, and replay in networks of spiking neurons | Zenodo

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