Frontiers in Synaptic Neuroscience Synaptic Neuroscience

100 citations recorded by the source corpus.

Spike-timing-dependent plasticity (STDP) offers a powerful means of forming and modifying neural circuits. Experimental and theoretical studies have demonstrated its potential usefulness for functions as varied as cortical map development, sharpening of sensory receptive fields, working memory, and associative learning. Even so, it is unlikely that STDP works alone. Unless changes in synaptic strength are coordinated across multiple synapses and with other neuronal properties, it is difficult to maintain the stability and functionality of neural circuits. Moreover, there are certain features of early postnatal development (e.g., rapid changes in sensory input) that threaten neural circuit stability in ways that STDP may not be well placed to counter. These considerations have led researchers to investigate additional types of plasticity, complementary to STDP, that may serve to constrain synaptic weights and/or neuronal firing. These are collectively known as “homeostatic plasticity” and include schemes that control the total synaptic strength of a neuron, that modulate its intrinsic excitability as a function of average activity, or that make the ability of synapses to undergo Hebbian modification depend upon their history of use. In this article, we will review the experimental evidence for homeostatic forms of plasticity and consider how they might interact with STDP during development, and learning and memory.

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

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Homeostatic plasticity, on the other hand, acts on much longer timescales of hours to days [14, 16, 19, 29, 55, 56, 80].”
An efficient automated parameter tuning framework for spiking neural networks (2014, background): “…is occurring when simulating synaptic plasticity (Abbott and Nelson, 2000), as new types of plasticity models such as homeostatic synaptic scaling (Watt and Desai, 2010; Carlson et al., 2013), short-term plasticity (Mongillo et al., 2008), and spike-timing dependent plasticity (STDP) (Song et…”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, background): “While plasticity can cause substantial synaptic changes in less than one minute [24–26], homeostatic responses typically differ on the order of several magnitudes (hours or days) [29,37].”
A History of Spike-Timing-Dependent Plasticity (2011, background): “Finding links between STDP and the BCM learning rules seems key to future progress in our understanding of the relationship between intrinsically unstable synaptic plasticity and stability-promoting mechanisms such as synaptic scaling (Turrigiano et al., 1998; Turrigiano and Nelson, 2000; Izhikevich”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “Homeostatic thresholds are known to promote neuronal stability in correlated learning rules, such as STDP, which favors long-term potentiation at high frequencies regardless of spike timing [183], [184].”