Abstract
Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing.
Highlights
Modifications of the strength of synaptic connections between neurons that occur in an activity-dependent manner are hypothesized to play an active role in generating the structure of neuronal networks [1,2,3,4,5,6,7]
Our results show that weight dependence and noise in the weight update are crucial features to obtain a realistic and functionally efficient spike-timing-dependent plasticity (STDP) model
In complement to previous studies on weight-dependent STDP [15,24,25,29], we have focused on the advantages for STDP to generate long-tail distributions that involve weights many times stronger than their mean
Summary
Modifications of the strength (or weight) of synaptic connections between neurons that occur in an activity-dependent manner are hypothesized to play an active role in generating the structure of neuronal networks [1,2,3,4,5,6,7]. Electrophysiological measurements in the barrel cortex of mice revealed rare large-amplitude responses in addition to more frequent medium- and smallamplitude responses [18] In addition to their long-tail character, the observed distributions exhibit a couple of outliers many times (e.g., 20) stronger than the mean. Similar long-tail distributions have been observed by two-photon imaging of dendritic spines in the hippocampal CA1 of young rats [19], where the spine size may be positively correlated with the strength of synapse [20] These findings led us to investigate the conditions under which STDP can generate such long-tail weight distributions in an activity-dependent manner. We focus on the conditions allowing STDP to produce long-tail weight distributions
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