Synaptic learning rules for sequence learning

• Published in: eLife 2021-04, Vol.10
• Main Authors: Reifenstein, Eric Torsten, Bin Khalid, Ikhwan, Kempter, Richard
• Format: Article
• Language: English
• Subjects: Asymmetry; Brain - cytology; Brain - physiology; Compression; Computer Simulation; Humans; Learning; Mathematical models; Memory; Models, Neurological; Neural Pathways - physiology; Neuronal Plasticity; Neurons - physiology; Neuroscience; Numerical Analysis, Computer-Assisted; phase precession; sequence learning; Symmetry; Synaptic plasticity; Synaptic Transmission; temporal compression; Temporal variations; Time Factors
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.67171

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.