Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

By Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Buesing, Wulfram Gerstner. 2008

154 citations recorded by the source corpus.

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Evaluation & Benchmarks

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

In Evaluation & Benchmarks

Citations in the corpus

Overcoming catastrophic forgetting in neural networks (2016, background): “The weight for the early phase of plasticity [Clopath et al., 2008] could encode the current synaptic strength, whereas the weight associated with the late-phase of plasticity or the consolidated phase could encode the mean weight.”
Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019, methodology): “We suggest a normative theory of synaptic consolidation that functionally reproduces the phenomena of the multi-state models of tagging & capture (Barrett et al., 2009; Clopath et al., 2008; Ziegler et al., 2015).”
Continual Reinforcement Learning with Complex Synapses (2018, background): “One potential answer may arise from the experimental observations that synaptic plasticity occurs at a range of different timescales, including short-term plasticity (Zucker & Regehr, 2002), long-term plasticity (Bliss & Lømo, 1973) and synaptic consolidation (Clopath et al., 2008).”
Embracing Change: Continual Learning in Deep Neural Networks. (2020, background): “Not only does this go to the heart of the optimization problem, it also is well-motivated by studies of synaptic consolidation in biological brains [3].”
Continual Reinforcement Learning in 3D Non-stationary Environments (2019, background): “However, recent evidences in behavioral experiments on rats suggests, more generally, behavioral correlates of synaptic consolidation especially when the subject is exposed to novel or strong external stimuli (e.g. a foot shock) [8, 9].”
The dendritic branch is the preferred integrative unit for protein synthesis-dependent LTP (2011, methodology): “Lastly, our data showing individual branches as the functional unit of long-term memory storage can be used to refine current computational models of STC (Barrett et al., 2009; Clopath et al., 2008), which have incorporated neither the spatial nor competition component of the CPH.”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, unknown): “Several models have taken the observations of discrete plasticity states into account (Abarbanel et al., 2005; Graupner and Brunel, 2007; Clopath et al., 2008).”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, result): “Our model has similarities with LTP induction in the TagTriC mode”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, background): “If a neuromodulatory signal (green arrows, top) arrives, the synapses with raised flags undergo plasticity (top: synapses of medium strength before phasic neuromodulatory signal; bottom: synapses after change).”
Models of Metaplasticity: A Review of Concepts (2015, background): “Although this issue has been addressed theoretically for various models (Clopath et al., 2008; Billings and van Rossum, 2009; Gilson and Fukai, 2011; Tetzlaff et al., 2013; Zenke et al., 2013), it suggests that additional timescales are necessary to properly combine short-term and long-term…”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “Indeed, modern computational models of synaptic consolidation take into account the effect of neuromodulators (Clopath et al., 2008; Ziegler et al., 2015) in a framework reminiscent of the three-factor rule defined by Eqs.”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “When we estimated t w we explicitly ignored correlations and required that LTD and LTP cancel at a firing rate k .”