Synaptic learning rules for sequence learning.

Eric Torsten Reifenstein,Ikhwan Bin Khalid,Richard Kempter

doi:10.7554/elife.67171

Abstract

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.

Highlights

It is a pivotal quality for animals to be able to store and recall the order of events (“temporal-order learning”, 1–3) but there is only little work on the neural mechanisms generating asymmetric memory associations across behavioral time intervals [4]
The faster time scale is given by the temporal properties of the induction of synaptic plasticity [6, 7] — and spike-timing dependent plasticity (STDP) is a common form of synaptic plasticity that depends on the millisecond timing and temporal order of presynaptic and postsynaptic spiking
We show that phase precession facilitates the learning of the temporal order of behavioral sequences for asymmetric learning windows that are shorter than a theta cycle

Summary

Introduction

It is a pivotal quality for animals to be able to store and recall the order of events (“temporal-order learning”, 1–3) but there is only little work on the neural mechanisms generating asymmetric memory associations across behavioral time intervals [4]. Phase precession allows for a temporal compression of a sequence of behavioral events from the time scale of seconds down to milliseconds (Fig. 1;12–14), which matches the widths of generic STDP learning windows [15,16,17,18]. This putative advantage of phase precession for temporal-order learning, has not yet been quantified. We provide a mechanistic description of associative chaining models [19] and extend these models to explain how to store serial order

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