Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics

By Jen-Yung Chen, Peter Lonjers, Christopher M. Lee, Marina Chistiakova, Maxim Volgushev, Maxim Bazhenov. 2013

91 citations recorded by the source corpus.

Spike timing-dependent plasticity (STDP) and other conventional Hebbian-type plasticity rules are prone to produce runaway dynamics of synaptic weights. Once potentiated, a synapse would have higher probability to lead to spikes and thus to be further potentiated, but once depressed, a synapse would tend to be further depressed. The runaway synaptic dynamics can be prevented by precisely balancing STDP rules for potentiation and depression; however, experimental evidence shows a great variety of potentiation and depression windows and magnitudes. Here we show that modifications of synapses to layer 2/3 pyramidal neurons from rat visual and auditory cortices in slices can be induced by intracellular tetanization: bursts of postsynaptic spikes without presynaptic stimulation. Induction of these heterosynaptic changes depended on the rise of intracellular calcium, and their direction and magnitude correlated with initial state of release mechanisms. We suggest that this type of plasticity serves as a mechanism that stabilizes the distribution of synaptic weights and prevents their runaway dynamics. To test this hypothesis, we develop a cortical neuron model implementing both homosynaptic (STDP) and heterosynaptic plasticity with properties matching the experimental data. We find that heterosynaptic plasticity effectively prevented runaway dynamics for the tested range of STDP and input parameters. Synaptic weights, although shifted from the original, remained normally distributed and nonsaturated. Our study presents a biophysically constrained model of how the interaction of different forms of plasticity—Hebbian and heterosynaptic—may prevent runaway synaptic dynamics and keep synaptic weights unsaturated and thus capable of further plastic changes and formation of new memories.

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, methodology): “Nevertheless, this form of heterosynaptic plasticity has been shown to prevent runaway of LTP in models of feedforward circuits [30] and has been demonstrated to co-occur with Hebbian plasticity in experiments [109].”
A tutorial on the free-energy framework for modelling perception and learning (2017, background): “Moreover, the dependence of synaptic plasticity on initial weights has been seen experimentally (Chen et al., 2013), so we feel it is plausible for the dependence predicted by the model to be present in real synapses.”
Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations (2016, background): “Such synaptic dynamics can be potentially prevented by including heterosynaptic plasticity (Chen et al., 2013).”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, methodology): “Details of slice preparation and recording were similar to those used in previous studies (Volgushev et al., 2000; Lee et al., 2012; Chen et al., 2013).”