Synaptic learning rules for sequence learning

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.

Neuronal sequences during theta rely on behavior-dependent spatial maps

“…Phase-precession and theta sequences have also been observed in brain regions other than the hippocampus (Kim et al, 2012;Hafting et al, 2008;Jones and Wilson, 2005; der Meer and Redish, 2011; Tingley et al, 2018;Tang et al, 2021). These studies indicate that the theta phase code plays a supporting role in a wide array of cognitive functions, such as sequential learning (Lisman and Idiart, 1995;Skaggs et al, 1996;Reifenstein et al, 2021), prediction (Lisman and Redish, 2009;Kay et al, 2020) and planning (Johnson and Redish, 2007;Erdem and Hasselmo, 2012;Bolding et al, 2020;Bush et al, 2015).…”

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

“…In line with this prediction, consolidation failed when replay speed was reduced to that of physical motion ( Fig 3G ) because the time scale of rate changes in place and grid cell activity is then much longer than the delay between the two pathways and the time scale of STDP ( Fig 2C ). Accelerated replay during sleep [ 13 ] hence supports systems memory consolidation within the PPT by aligning the time scales of neural activity and synaptic plasticity [ 54 ], and this alignment is similar to the effect of phase precession during memory acquisition [ 55 ].…”

From Biological Synapses to “Intelligent” Robots

“…Sequence learning (Figure 6) in neural networks has been a model in terms of finely tuned temporal firing activities enabling the compression of slow behavioral sequences down to the millisecond timescale, which is that of synaptic plasticity. Mathematical analysis and computer simulations have produced the phenomenon of phase precession [95][96][97][98][99]. Within critically short synaptic learning windows, phase precession was found to improve temporal-order neural network learning [98,99].…”

“…Mathematical analysis and computer simulations have produced the phenomenon of phase precession [95][96][97][98][99]. Within critically short synaptic learning windows, phase precession was found to improve temporal-order neural network learning [98,99]. Putative mechanisms for linking the millisecond timescale of synaptic plasticity to the slow timescale of behavior relate induction times of synaptic plasticity to spike-timing-dependent plasticity, a specific form of synaptic plasticity, taking into account the temporal order of presynaptic and postsynaptic spiking, on the one hand, and the slower firing rates of place cells [97][98][99], a specific class of location coding neurons, on the other.…”

“…constitutes the temporal intervals at which presynaptic and postsynaptic activities induce synaptic plasticity during learning, and model accounts have simulated precisely timed neural activity generated by phase precession, i.e., the successive across-cycle shift of from-late-to-early spike phases by comparison with a background oscillation [98]. Phase precession allows for a temporal compression of a sequence of behavioral events from the timescale of seconds to that of milliseconds [99][100][101], matching the widths of generic spike-timing-dependent plasticity (STDP) learning windows [102][103][104]. The processes of synaptic plasticity are described activity-dependent alterations of synaptic transmission efficiency (functional plasticity) resulting from or accompanied by changes in the structure and number of synaptic connections (structural plasticity).…”