Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

@article{Masquelier2008SpikeTD,
  title={Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains},
  author={Timoth{\'e}e Masquelier and Rudy Guyonneau and Simon J. Thorpe},
  journal={PLoS ONE},
  year={2008},
  volume={3}
}
Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early… Expand
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References

SHOWING 1-10 OF 55 REFERENCES
Neurons Tune to the Earliest Spikes Through STDP
TLDR
It is shown in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. Expand
Spike Timing-Dependent Synaptic Depression in the In Vivo Barrel Cortex of the Rat
TLDR
It is demonstrated that spike timing-dependent synaptic depression occurs in S1 in vivo, and is therefore a plausible plasticity mechanism in the sensory cortex. Expand
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
TLDR
The results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses. Expand
Discovering Spike Patterns in Neuronal Responses
TLDR
It is shown that the same repeated stimulus can produce more than one reliable temporal pattern of spikes, and it is concluded that the prestimulus history of a neuron may influence the precise timing of the spikes in response to a stimulus over a wide range of time scales. Expand
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
TLDR
A triplet rule is examined, a rule which considers sets of three spikes and is possible to fit experimental data from visual cortical slices as well as from hippocampal cultures and can be mapped to a Bienenstock–Cooper–Munro learning rule. Expand
Refractoriness and Neural Precision
TLDR
The underlying free firing rate derived by allowing for the refractory period often exceeded the observed firing rate by an order of magnitude and was found to convey information about the stimulus over a much wider dynamic range. Expand
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
TLDR
In modeling studies, it is found that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. Expand
Stable Hebbian Learning from Spike Timing-Dependent Plasticity
TLDR
The results indicate that stable correlation-based plasticity can be achieved without introducing competition, suggesting that plasticity and competition need not coexist in all circuits or at all developmental stages. Expand
The tempotron: a neuron that learns spike timing–based decisions
TLDR
This work proposes a new, biologically plausible supervised synaptic learning rule that enables neurons to efficiently learn a broad range of decision rules, even when information is embedded in the spatiotemporal structure of spike patterns rather than in mean firing rates. Expand
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
  • G. Bi, M. Poo
  • Biology, Medicine
  • The Journal of Neuroscience
  • 1998
TLDR
The results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification. Expand
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