Relating STDP to BCM

By Eugene M. Izhikevich, Niraj S. Desai. Neural Computation, 2003

339 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b772a4a5a1b1657a4bdadf8016f9e68d21c4fd0d

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Emergent Dynamical Properties of the BCM Learning Rule (2017, background): “In addition to the present work contributing to the debate by demonstrating the relevance of fast homeostasis to synaptic stability, it also furthers the discussion as regards the link between STDP and the BCM rule: Zenke et al. [33] found that homeostasis needs to have a faster rate of change for s”
An efficient automated parameter tuning framework for spiking neural networks (2014, methodology): “All plastic connections used a standard nearest-neighbor STDP implementation (Izhikevich and Desai, 2003) but distinct STDP rules were used for STDP occurring between excitatory-to-excitatory (E → E) neurons and STDP occurring between excitatory-to-inhibitory (E → I) neurons.”
Phenomenological models of synaptic plasticity based on spike timing (2008, result): “Note that earlier efforts to show that STDP maps to the BCM model (Izhikevich and Desai 2003; Senn et al. 2000) demonstrated neither an exact mapping nor a sliding threshold.”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “In general, there might be stability issues with generic STDP when used in complex biological models (Izhikevich and Desai, 2003;”
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations (2011, background): “Several other models have addressed the issue of spikingbased implementations of the BCM learning framework (13, 14).”
Stable Learning in Stochastic Network States (2012, background): “…theoretical studies have succeeded in reproducing some aspects of the BCM rule by considering nonlinear interactions be-tween presynaptic and postsynaptic spike patterns in STDP (Senn et al., 2001; Izhikevich and Desai, 2003; Burkitt et al., 2004; Pfister and Gerstner, 2006; Clopath et al., 2010).”
Pyramidal Neuron Conductance State Gates Spike-Timing-Dependent Plasticity (2010, background): “…might assume that the minimum of the t-LTD curve is bigger than the maximum of the t-LTP curve (Song et al., 2000), or that the integral of the STDP curve is negative and that multispike interactions are such that the whole curve is sampled when firing rates are high (Izhikevich and Desai, 2003).”
Frontiers in Synaptic Neuroscience Synaptic Neuroscience (positive): “Given its success, there has been considerable interest in understanding the relationship between BCM and STDP (Izhikevich and Desai, 2003; Burkitt et al., 2004; Pfister and Gerstner, 2006; Benuskova and Abraham, 2007).”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “Considerable theoretical effort has been devoted to studying the characteristics of STDP learning of excitatory synapses [23–43].”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, neutral): “, 2006) which is in turn closely related to other nonlinear STDP models (Senn et al., 2001; Froemke and Dan, 2002; Izhikevich and Desai, 2003) as well as to the BCM model discussed above (Pfister et al.”
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule (2007, background): “Izhikevich and Desai (2003) have also recently presented some theoretical work related to the relationship between STDP and the BCM rule.”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, methodology): “Around 1t = 0 pair-based STDP models assume a sharp transition from maximal long-term depression (LTD) to maximal long-term potentiation (LTP) as the time lags increase from negative to positive values (Gerstner et al., 1996; Song et al., 2000; Izhikevich and Desai, 2003; Morrison et al., 2008), whe”
A History of Spike-Timing-Dependent Plasticity (2011, background): “…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 and Desai, 2003; Watt and Desai, 2010).”
A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo (2015, positive): “Izhikevich and Desai [11] have shown that whereas all-to-all spike pairing rule fails to make STDP compatible with classical BCM formulation of LTP/LTD, nearestneighbor pairing rule is able to link STDP to classical LTP/LTD (see e.”
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity (2006, neutral): “…this the All-to-All interaction) (Gerstner et al., 1996; Kempter et al., 1999; KistlerandvanHemmen,2000;Songetal.,2000)andthecasewhere onlyneighboringspikesaretakenintoaccount(Nearest-Spikeinter-action)(vanRossumetal.,2000;Bi,2002;IzhikevichandDesai, 2003;Burkittetal.,2004;PfisterandGerstner,2006).”
STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity (2007, methodology): “Among others, it cannot account for the simultaneous homosynaptic LTP and heterosynaptic LTD induced by HFS of the MPP Springer terminating upon granule cells in the hippocampal dentate gyrus as observed in Abraham et al. (2001).”
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal (2012, neutral): “A1 below (Song et al., 2000; Izhikevich and Desai, 2003).”