ArticleFrontiers in synaptic neuroscience2010
Rate and pulse based plasticity governed by local synaptic state variables.
Article in Frontiers in synaptic neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.Neuroinformatics · 2026Article
- A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics.eLife · 2023Article
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- Plasticity and Adaptation in Neuromorphic Biohybrid Systems.iScience · 2020Review
- A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.Scientific reports · 2018Article
- Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuli.Scientific reports · 2018Article
- A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent "Heterosynaptic" LTD in the Dentate Gyrus In Vivo.PLoS computational biology · 2015Article
- Network-driven design principles for neuromorphic systems.Frontiers in neuroscience · 2015Article
- Implementation of a spike-based perceptron learning rule using TiO2-x memristors.Frontiers in neuroscience · 2015Article
- Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs.Frontiers in neuroscience · 2015Article
- Plasticity in memristive devices for spiking neural networks.Frontiers in neuroscience · 2015Review
- Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS.Frontiers in neuroscience · 2015Article
- Calcium-dependent calcium decay explains STDP in a dynamic model of hippocampal synapses.PloS one · 2014Article
- Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.PloS one · 2014Article
- Configurable analog-digital conversion using the neural engineering framework.Frontiers in neuroscience · 2014Article
- Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks.Journal of computational neuroscience · 2012Article
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2 authors.
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Abstract
Classically, action-potential-based learning paradigms such as the Bienenstock-Cooper-Munroe (BCM) rule for pulse rates or spike timing-dependent plasticity for pulse pairings have been experimentally demonstrated to evoke long-lasting synaptic weight changes (i.e., plasticity). However, several recent experiments have shown that plasticity also depends on the local dynamics at the synapse, such as membrane voltage, Calcium time course and level, or dendritic spikes. In this paper, we introduce a formulation of the BCM rule which is based on the instantaneous postsynaptic membrane potential as well as the transmission profile of the presynaptic spike. While this rule incorporates only simple local voltage- and current dynamics and is thus neither directly rate nor timing based, it can replicate a range of experiments, such as various rate and spike pairing protocols, combinations of the two, as well as voltage-dependent plasticity. A detailed comparison of current plasticity models with respect to this range of experiments also demonstrates the efficacy of the new plasticity rule. All experiments can be replicated with a limited set of parameters, avoiding the overfitting problem of more involved plasticity rules.
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