Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.
Code and data are now available at https://gitlab.com/e.reifenstein/synaptic-learning-rules-for-sequence-learning
- Richard Kempter
- Richard Kempter
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Martin Vinck, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany
- Received: February 3, 2021
- Accepted: March 31, 2021
- Accepted Manuscript published: April 16, 2021 (version 1)
© 2021, Reifenstein et al.
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