A History of Spike-Timing-Dependent Plasticity

By Henry Markram, Wulfram Gerstner, Per Jesper Sjöström. 2011

415 citations recorded by the source corpus.

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Pain-relief learning in flies, rats, and man: basic research and applied perspectives (2014, background): “Typically, in spike-timing dependent plasticity (STDP) (for reviews, see Bi and Poo 2001; Caporale and Dan 2008; Markram et al. 2011) a synaptic connection is strengthened when action www.learnmem.org 237 Learning & Memory potentials in the presynaptic cell precede those in the postsynaptic cell,…”
Deep Learning in Spiking Neural Networks (2018, methodology): “1) Unsupervised Learning via STDP: As stated above, unsupervised learning in SNNs often involves STDP as part of the learning mechanism [74], [75].”
Spike-Timing-Dependent Plasticity: A Comprehensive Overview (2012, background): “Finally, Froemke et al. (2010a) discuss the consequences of temporally non-linear spike-pair interactions in STDP.”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, background): “Traditional approaches to synaptic plasticity, influenced byHebb’s postulate (Hebb, 1949), have focused on the joint activation of pre- and postsynaptic neurons as a driver for synaptic changes (Bliss and Gardner-Medwin, 1973; Artola and Singer, 1993; Malenka and Nicoll, 1999).”
The spike timing dependence of plasticity (2012, background): “Many excellent reviews have been published on STDP (e.g., Abbott and Nelson, 2000; Dan and Poo, 2006; Letzkus et al., 2007; Caporale and Dan, 2008; Sjöström et al., 2008; Froemke et al., 2010a), including a comprehensive history (Markram et al., 2011).”
Network-timing-dependent plasticity (2015, background): “The relative timing of single spikes generated in connected pairs of neurons can also induce LTP and LTD (Markram et al., 1997; Bi and Poo, 1998), which has led to the well-known SpikeTiming-Dependent Plasticity (STDP) model of synaptic plasticity (Markram et al., 2011).”