Phenomenological models of synaptic plasticity based on spike timing

By Abigail Morrison, Markus Diesmann, Wulfram Gerstner. Biological cybernetics, 2008

633 citations recorded by the source corpus.

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.

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

View Paper: https://www.semanticscholar.org/paper/fb73b7aadf39ccb2c64e3dc152c18cd97507f4ee

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Citations in the corpus

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, methodology): “In order to investigate its functional effects, we use a phenomenological model that abstracts some key features observed in physiology (Morrison et al. 2008).”
A review of learning in biologically plausible spiking neural networks (2019, neutral): “The exact relationship between microscopic synaptic properties and macroscopic functional consequences remains highly controversial (Morrison, Diesmann, & Gerstner, 2008).”
Computational models of long term plasticity and memory (2017, result): “Phenomenological models can describe accurately many other aspects of the experimental observations [Senn et al., 2001, Pfister and Gerstner, 2006, Morrison et al., 2008, Clopath et al., 2010].”
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (2023, methodology): “3E, the STDP learner in SpikingJelly uses the “ all-to-all ” STDP with the trace method ( 157 ), in which all the pre-and postsynaptic spike pairs are considered.”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, methodology): “Our conclusions are supported by analyses and simulations of various different STDP models (Morrison et al., 2008; Clopath et al., 2010).”
Frontiers in Synaptic Neuroscience Synaptic Neuroscience (positive): “Simple pair-based STDP rules, even if complemented with additional constraints, are not able to capture much of the phenomenology of STDP (Morrison et al., 2008).”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, methodology): “The Convallis rule reproduces a STDP curve similar to bi-exponential form found in many computational models [51].”
The spike timing dependence of plasticity (2012, background): “While most early studies suggested a correlation requirement of about ±100 ms for plasticity (Baranyi and Fehér, 1981; Gustafsson et al., 1987), a few studies noted an effect of spike order, with LTP occurring when presynaptic inputs led or were synchronous with postsynaptic spikes (evoked by a…”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “, [41,43], and in particular the distribution of resultant synaptic weights.”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, result): “Similar to previous plasticity models (Rochester et al., 1956; Bienenstock et al., 1982; Gerstner et al., 1996; Song et al., 2000; Fusi, 2002; Pfister and Gerstner, 2006; Morrison et al., 2008), coactivation of presynaptic and postsynaptic neurons induces synaptic memory traces.”
Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex (2012, background): “For example the synaptic weights in phenomenological models of spike timing dependent plasticity, for which causal implementation are known [31] follow dynamics similar to (1).”
Frontiers in Computational Neuroscience Computational Neuroscience Structural Plasticity Controlled by Calcium Based Correlation Detection (2008, neutral): “These studies typically measure the timing Δt = t post − t pre between the spiking in the presynaptic neuron's soma t pre and the spiking in the postsynaptic neuron's soma t post (see Morrison et al., 2008, for the defi nition of delays in models of synaptic plasticity).”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “Other phenomenological [56,57] or mechanistic models of STDP [58] also show some of these characteristics, but come without such theoretical properties.”
A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards (2013, background): “The following assumptions are also made: Morrison, Diesmann, & Gerstner, 2008).”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways (2009, background): “Such spike-timing-dependent plasticity (STDP) has been the subject of both theoretical (Gerstner et al. 1996; Kempter et al. 1999; Gutig et al. 2003; Burkitt et al. 2004; Meffin et al. 2006) and experimental (Markram et al. 1997; Bi and Poo 2001) research; for a review, see Morrison et al. (2008) .”
Pyramidal Neuron Conductance State Gates Spike-Timing-Dependent Plasticity (2010, background): “Previous studies of static STDP learning rules indicate that whether or not this balance is reached (and the nature of the resulting synaptic dynamics) depends critically on how STDP is implemented (Song et al., 2000; van Rossum et al., 2000; Gütig et al., 2003; Burkitt et al., 2004; Zou and Destexh”
Unsupervised Natural Visual Experience Rapidly Reshapes Size Invariant Object Representation in Inferior Temporal Cortex (2010, positive): “…Wiskott and Sejnowski, 2002; Wyss et al., 2006), and such models can be implemented using variants of Hebbian-like learning rules that are dependent on the timing of spikes (Gerstner et al., 1996; Sprekeler et al., 2007; Wallis and Rolls, 1997; Morrison et al., 2008; Sprekeler and Gerstner, 2009).”