Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity

Abstract

Classical experiments on spike timing-dependent plasticity (STDP) use a protocol based on pairs of presynaptic and postsynaptic spikes repeated at a given frequency to induce synaptic potentiation or depression. Therefore, standard STDP models have expressed the weight change as a function of pairs of presynaptic and postsynaptic spike. Unfortunately, those paired-based STDP models cannot account for the dependence on the repetition frequency of the pairs of spike. Moreover, those STDP models cannot reproduce recent triplet and quadruplet experiments. Here, we examine a triplet rule (i.e., a rule which considers sets of three spikes, i.e., two pre and one post or one pre and two post) and compare it to classical pair-based STDP learning rules. With such a triplet rule, it is possible to fit experimental data from visual cortical slices as well as from hippocampal cultures. Moreover, when assuming stochastic spike trains, the triplet learning rule can be mapped to a Bienenstock-Cooper-Munro learning rule.

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

“…In contrast to standard STDP rules (reviewed in 16 ), pairing frequency dependence 18 and burst--timing dependence 32 are qualitatively described. In addition the rule is expected to reproduce the triplet and quadruplet experiments in hippocampal slices 40 (data not shown), because for all STDP protocols the plasticity rule in this paper is similar to an earlier nonlinear STDP rule 29 . Deriving STDP rules from voltage dependence has been attempted before 18,41--42 .…”

“…The quantity could for example represent the amount of glutamate bound to postsynaptic receptors 29 or the number of NMDA receptors in an activated state 28 . Potentiation is given by Here, is a free amplitude parameter fitted to the data and (t) is another low--pass filtered version of u(t) similar to (t) but with a shorter time constant τ + around 10ms.…”

Two dimensional model of learning in spiking neural networks with homeostasis and reward

“…Для хебівської форми використано правило триплетної спайк-часовозалежної пластичності [15], яке може бути зведено до розширеного правила BCM [16].…”

A neuromorphic VLSI design for spike timing and rate based synaptic plasticity

“…It is widely believed that processes responsible for synaptic plasticity provide key mechanisms underlying learning and memory in the brain Song et al (2000); Pfister and Gerstner (2006); Sjöström et al (2001); Wang et al (2005). Researchers from various fields, including biology, neurophysiology, and engineering over the last fifty years have attempted to explore, describe and understand the processes of synaptic plasticity leading to learning and memory.…”

“…Many theoretical and experimental studies have focused on studying changes to synaptic strength caused by STDP Gerstner et al (1996); Bi and Poo (1998); Song et al (2000); Froemke and Dan (2002); Wang et al (2005); Pfister and Gerstner (2006); Iannella and Tanaka (2006); Iannella et al (2010). At the same time, there have been attempts at translating such rules to VLSI circuit implementations Bofill-I- Petit and Murray (2004) ;Indiveri et al (2006); Tanaka et al (2009) ;Rachmuth et al (2011); Azghadi et al (2011Azghadi et al ( , 2012a.…”