Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma

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 val...

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Vydáno v:	PloS one Ročník 6; číslo 10; s. e25339
Hlavní autoři:	Gilson, Matthieu, Fukai, Tomoki
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 07.10.2011 Public Library of Science (PLoS)
Témata:	Biology Brain research Competition Computer Science Configurations Correlation Cybernetics Data processing Dynamic stability Firing pattern Information processing Life Sciences Machine Learning Mental depression Models, Neurological Nerve Net - cytology Nerve Net - physiology Neuronal Plasticity - physiology Neurons Neurons - cytology Neurons and Cognition Neurosciences Parameterization Physiological aspects Rodents Science Specialization Synapses Synapses - physiology Synaptic plasticity Tails Take-all disease Time Factors Japan Distribution curves Action potentials Neuronal plasticity Neurons Neural networks Body weight Single neuron function Synapses
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: MG TF. Wrote the paper: MG TF.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0025339