Replay of Learned Neural Firing Sequences during Rest in Human Motor Cortex


The offline “replay” of neural firing patterns underlying waking experience, previously observed in non-human animals, is thought to be a mechanism for memory consolidation. Here, we test for replay in the human brain by recording spiking activity from the motor cortex of two participants who had intracortical microelectrode arrays placed chronically as part of a brain-computer interface pilot clinical trial. Participants took a nap before and after playing a neurally controlled sequence-copying game that consists of many repetitions of one “repeated” sequence sparsely interleaved with varying “control” sequences. Both participants performed repeated sequences more accurately than control sequences, consistent with learning. We compare the firing rate patterns that caused the cursor movements when performing each sequence to firing rate patterns throughout both rest periods. Correlations with repeated sequences increase more from pre- to post-task rest than do correlations with control sequences, providing direct evidence of learning-related replay in the human brain.


They are now many evidences of offline replays of neural firing sequences.

The big question is how those replays are triggered & coordinated to consolidate long term memories. The hippocampus seems to play a key role in this (via sharp-wave ripples patterns SWR).

There are many papers about such potential interactions between the hippocampus and related cortical areas, but subcortical structures are also likely to be involved (such replays have been observed in the ventral striatum for instance)


Replays can be:

  • Retrospective (engaging sequences already experienced) or prospective (engaging sequences ahead in time)
  • Forward (same order as experienced) or reverse (opposite to experienced)
  • Local or remote (according to the subject location in the event space)
  • Time-compressed (x0.1) or dilated (x2) or everything in between (from the paper mentioned above)

Potential factors influencing replay across CA1 during sharp-wave ripples, Liset M. de la Prida, April 2020:

Sharp-wave ripples are complex neurophysiological events recorded along the trisynaptic hippocampal circuit (i.e. from CA3 to CA1 and the subiculum) during slow-wave sleep and awake states. They arise locally but scale brainwide to the hippocampal target regions at cortical and subcortical structures. During these events, neuronal firing sequences are replayed retrospectively or prospectively and in the forward or reverse order as defined by experience. They could reflect either pre-configured firing sequences, learned sequences or an option space to inform subsequent decisions. How can different sequences arise during sharp-wave ripples? Emerging data suggest the hippocampal circuit is organized in different loops across the proximal (close to dentate gyrus) and distal (close to entorhinal cortex) axis. These data also disclose a so-far neglected laminar organization of the hippocampal output during sharp-wave events. Here, I discuss whether by incorporating celltype-specific mechanisms converging on deep and superficial CA1 sublayers along the proximodistal axis, some novel factors influencing the organization of hippocampal sequences could be unveiled.


For the link with subcortical structures, see this paper for example:

in addition to being accompanied by hippocampal replay of spatial trajectories, SWR events might also be accompanied by activation of subcortical motor representations (Wirtshafter & Wilson 2019). Hence, when mental representations of a particular location become active within hippocampal place cell populations—either during an SWR event or during a “theta sequence” driven by phase precession—a corresponding representation of the motor action necessary to reach that location may become concurrently activated within subcortical regions, including septum and striatum. Concurrent activation of hippocampal state representations and subcortical action representations might support neural computations that are essential for reinforcement learning and value-based decision making.