STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity

By Lubica Benuskova, Wickliffe C. Abraham. 2007

51 citations recorded by the source corpus.

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, neutral): “[113] Benuskova and Abraham [63] Zenke et al.”
Stable Learning in Stochastic Network States (2012, result): “Similarly, previous models of higher-order STDP interactions (Senn et al., 2001; Shouval et al., 2002; Badoual et al., 2006; Pfister and Ger-stner, 2006; Benuskova and Abraham, 2007; Clopath et al., 2010) did not include metaplasticity as an emergent property of the model.”
Frontiers in Synaptic Neuroscience Synaptic Neuroscience (unknown): “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).”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, background): “Although heterosynaptic plasticity was not implemented in this model, some of the unpaired inputs still changed, apparently because they were occasionally active within STDP windows of postsynaptic spikes (Benuskova and Abraham, 2007).”
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, unknown): “model this as yet unexplained heterosynaptic plasticity phenomenon in the dentate gyrus of freely moving rats [12].”
"Heterosynaptic" LTD in the dentate gyrus of anesthetized rat requires homosynaptic activity. (2007, methodology): “For these simulations, the model used was the same as that described in Benuskova and Abraham (2007) with the scaling factor equal to 5000 and no hard limits on maximal or minimal values of the synaptic weights.”