Temporal modulation of spike-timing-dependent plasticity (original) (raw)
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Spike-timing-dependent plasticity: common themes and divergent vistas
2002
Abstract. Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre-and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks.
Mechanisms and significance of spike-timing dependent plasticity
Biological Cybernetics, 2002
Hebb's original postulate left two important issues unaddressed: (i) what is the effective time window between pre-and postsynaptic activity that will result in potentiation? and (ii) what is the learning rule that underlies decreases in synaptic strength? While research over the past 2 decades has addressed these questions, several studies within the past 5 years have shown that synapses undergo long-term depression (LTD) or longterm potentiation (LTP) depending on the order of activity in the pre-and postsynaptic cells. This process has been referred to as spike-timing dependent plasticity (STDP). Here we discuss the experimental data on STDP, and develop models of the mechanisms that may underlie it. Specifically, we examine whether the standard model of LTP and LTD in which high and low levels of Ca 2þ produce LTP and LTD, respectively, can also account for STDP. We conclude that the standard model can account for a type of STDP in which, counterintuitively, LTD will be observed at some intervals in which the presynaptic cell fires before the postsynaptic cell. This form of STDP will also be sensitive to parameters such as the presence of an afterdepolarization following an action potential. Indeed, the sensitivity of this type of STDP to experimental parameters suggests that it may not play an important physiological role in vivo. We suggest that more robust forms of STDP, which do not exhibit LTD at pre-before-post intervals, are not accounted for by the standard model, and are likely to rely on a second coincidence detector in addition to the NMDA receptor.
Human synapses show a wide temporal window for spike-timing-dependent plasticity
Frontiers in Synaptic Neuroscience, 2010
Throughout our lifetime, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. Synapses can bi-directionally alter strength and the magnitude and sign depend on the millisecond timing of presynaptic and postsynaptic action potential firing. Recent findings on laboratory animals have shown that neurons can show a variety of temporal windows for spike-timing-dependent plasticity (STDP). It is unknown what synaptic learning rules exist in human synapses and whether similar temporal windows for STDP at synapses hold true for the human brain. Here, we directly tested in human slices cut from hippocampal tissue removed for surgical treatment of deeper brain structures in drug-resistant epilepsy patients, whether adult human synapses can change strength in response to millisecond timing of pre-and postsynaptic firing. We find that adult human hippocampal synapses can alter synapse strength in response to timed preand postsynaptic activity. In contrast to rodent hippocampal synapses, the sign of plasticity does not sharply switch around 0-ms timing. Instead, both positive timing intervals, in which presynaptic firing preceded the postsynaptic action potential, and negative timing intervals, in which postsynaptic firing preceded presynaptic activity down to −80 ms, increase synapse strength (tLTP). Negative timing intervals between −80 to −130 ms induce a lasting reduction of synapse strength (tLTD). Thus, similar to rodent synapses, adult human synapses can show spike-timing-dependent changes in strength. The timing rules of STDP in human hippocampus, however, seem to differ from rodent hippocampus, and suggest a less strict interpretation of Hebb's predictions.
Spike-timing-dependent synaptic plasticity: from single spikes to spike trains
Neurocomputing, 2004
We present a neurobiologically motivated model of a neuron with active dendrites and dynamic synapses, and a training algorithm which builds upon single spike-timing-dependent synaptic plasticity derived from neurophysiological evidence. We show that in the presence of a moderate level of noise, the plasticity rule can be extended from single to multiple presynaptic spikes and applied to effectively train a neuron in detecting temporal sequences of spike trains. The trained neuron responds reliably under different regimes and types of noise.
Entropy
Synaptic plasticity is characterized by remodeling of existing synapses caused by strengthening and/or weakening of connections. This is represented by long-term potentiation (LTP) and long-term depression (LTD). The occurrence of a presynaptic spike (or action potential) followed by a temporally nearby postsynaptic spike induces LTP; conversely, if the postsynaptic spike precedes the presynaptic spike, it induces LTD. This form of synaptic plasticity induction depends on the order and timing of the pre- and postsynaptic action potential, and has been termed spike time-dependent plasticity (STDP). After an epileptic seizure, LTD plays an important role as a depressor of synapses, which may lead to their complete disappearance together with that of their neighboring connections until days after the event. Added to the fact that after an epileptic seizure the network seeks to regulate the excess activity through two key mechanisms: depressed connections and neuronal death (eliminating...
Two-Trace Model for Spike-Timing-Dependent Synaptic Plasticity
We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the Ca 2+ concentration in the den-dritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, thus constituting a practical tool for the study of the interplay of neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre-and postsynaptic spikes, the standard pairwise STDP rule is reproduced, with appropriate parameters determining the respective weights and timescales for the causal and the anticausal contributions. The model contains otherwise only three free parameters, which can be adjusted to reproduce triplet nonlinearities in hippocampal culture and cortical slices. We also investigate the transition from time-dependent to rate-dependent plasticity occurring for both correlated and uncorrelated spike patterns.
Frontiers in computational neuroscience, 2018
In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb's postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected ...
The Journal of Neuroscience, 2016
Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity.
International Journal of Neural Systems, 2006
Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre-and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytical investigation of the impact of STDP at the network level.
Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains
PLoS ONE, 2008
Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn 'early spike patterns', that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense 'distractor' spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.