Frontiers in Synaptic Neuroscience Synaptic Neuroscience

By V. Pawlak, Jeffery R. Wickens, Alfredo Kirkwood, J. Kerr

266 citations recorded by the source corpus.

Spike timing dependent plasticity (STDP) is a temporally specific extension of Hebbian associative plasticity that has tied together the timing of presynaptic inputs relative to the postsynaptic single spike. However, it is difficult to translate this mechanism to in vivo conditions where there is an abundance of presynaptic activity constantly impinging upon the dendritic tree as well as ongoing postsynaptic spiking activity that backpropagates along the dendrite. Theoretical studies have proposed that, in addition to this pre- and postsynaptic activity, a “third factor” would enable the association of specific inputs to specific outputs. Experimentally, the picture that is beginning to emerge, is that in addition to the precise timing of pre- and postsynaptic spikes, this third factor involves neuromodulators that have a distinctive influence on STDP rules. Specifically, neuromodulatory systems can influence STDP rules by acting via dopaminergic, noradrenergic, muscarinic, and nicotinic receptors. Neuromodulator actions can enable STDP induction or – by increasing or decreasing the threshold – can change the conditions for plasticity induction. Because some of the neuromodulators are also involved in reward, a link between STDP and reward-mediated learning is emerging. However, many outstanding questions concerning the relationship between neuromodulatory systems and STDP rules remain, that once solved, will help make the crucial link from timing-based synaptic plasticity rules to behaviorally based learning.

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

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “While a detailed account of the role of neuromodulators goes beyond the scope of this article (but see [93, 94] for reviews), here we note that several neuromodulators at least partially meet one of these core requirements for RCPs.”
Stable Learning in Stochastic Network States (2012, background): “…and invertebrates that STDP is modulated by neuromodulators (Seol et al., 2007; Pawlak and Kerr, 2008; Shen et al., 2008; Zhang et al., 2009; Pawlak et al., 2010; S. Cassenaer, and G. Laurent, personal communication), which are known to be key factors in memory retention (Kasamatsu and…”
Neuromodulation of brain states. (2012, unknown): “Furthermore, synaptic plasticity is strongly influenced by neuromodulators (Pawlak et al., 2010; Rasmusson, 2000).”
Plasticity of Cortical Excitatory-Inhibitory Balance (2015, background): “Neuromodulation is also known to play a major role in cortical plasticity (Froemke & Martins 2011, Gu 2002, Pawlak et al. 2010, Weinberger 2007).”
RM-SORN: a reward-modulated self-organizing recurrent neural network (2015, background): “These plasticity mechanisms are, in turn, shaped by neuro-modulators, which are often part of a reward system (Pawlak et al., 2010).”
A History of Spike-Timing-Dependent Plasticity (2011, result): “As shown by the teams of Jason Kerr, Alfredo Kirkwood, and Guo-qiang Bi, STDP is also under powerful neuromodulatory control (Seol et al., 2007; Pawlak and Kerr, 2008; Zhang et al., 2009; Pawlak et al., 2010).”
The spike timing dependence of plasticity (2012, unknown): “For detailed review, see Pawlak et al. (2010).”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, methodology): “In addition, Friedmann et al. (2017) investigated the integration of on-chip plasticity processors into the BrainScaleS system to modulate STDP based on the model of neuromodulators in biology (Pawlak et al., 2010) allowing for supervised training.”
Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks (2011, background): “Neuromodulation can also be essential either for inducing STDP or for altering the threshold for induction, see Pawlak et al. (2010) in this special issue for a that is independent of the synaptic weight or only very weakly dependent.”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, methodology): “We review STDP experiments, where neuromodulation is manipulated during or after the pairing protocol.”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, background): “…al., 2008; Barrett et al., 2009), the state of the slow synaptic trace is normally protected against overwriting and cannot change unless a “write-now” signal is given (Crow, 1968; Carpenter and Grossberg, 1987; Izhikevich, 2007; Fre´maux et al., 2010; Fre´gnac et al., 2010; Pawlak et al., 2010).”