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.
Highlights
One of the major research areas of neurobiology is long-term learning of synapses in neural tissue (Koch, 1999; Lisman and Spruston, 2005; Pfister and Gerstner, 2006; Morrison et al, 2008)
We have taken this hypothesis one step further, creating a plasticity rule where the complete synaptic plasticity is dependent on the postsynaptic membrane potential
Is this supported by the mechanisms underlying the generation of synaptic plasticity? According to (Aihara et al, 2007; Sjöström et al, 2008), a slow inactivation of the Calcium channels following a medium Calcium elevation is necessary for long-term depression (LTD), whereas a fast Calcium spike produces long-term potentiation (LTP)
Summary
One of the major research areas of neurobiology is long-term learning (i.e., plasticity) of synapses in neural tissue (Koch, 1999; Lisman and Spruston, 2005; Pfister and Gerstner, 2006; Morrison et al, 2008). Various models have tried to incorporate the principal experimental findings, e.g., in implementations of the classical ratebased Bienenstock–Cooper–Munroe (BCM) rule (Bienenstock et al, 1982; Shouval et al, 2002; Kurashige and Sakai, 2006) or the newer spike timing-dependent plasticity (STDP) rule (Badoual et al, 2006; Morrison et al, 2008) Since both rules describe phenomena which have been shown to coexist at the same synapse, several models try to achieve a synthesis of both rules (Senn, 2002; Izhikevich and Desai, 2003; Pfister and Gerstner, 2006; Benuskova and Abraham, 2007)
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