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

PLoS ONE ◽  
2008 ◽  
Vol 3 (1) ◽  
pp. e1377 ◽  
Author(s):  
Timothée Masquelier ◽  
Rudy Guyonneau ◽  
Simon J. Thorpe
Keyword(s):  
Spike Trains ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Repeating Patterns

Spike-Timing Dependent Plasticity for Evolved Robots

2002 ◽  
Vol 10 (3-4) ◽  
pp. 243-263 ◽  
Author(s):  
Ezequiel Di Paolo
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Spike Trains ◽  
Spike Timing ◽  
Behavioral Strategies ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Noise ◽  
Functional Value ◽  
Millisecond Range

Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is demonstrated by randomizing the spike trains. In the low neural noise scenario, weight values undergo rhythmic changes at the mesoscale due to bursting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization results in loss of performance in this case. In contrast, in the high neural noise scenario, robots are robust to loss of information in the timing of the spike trains, demonstrating the counterintuitive results that plasticity, which is dependent on precise spike timing, can work even in its absence, provided the behavioral strategies make use of robust longer-term invariants of sensorimotor interaction. A comparison with a rate-based model of synaptic plasticity shows that under similarly noisy conditions, asymmetric spike-timing dependent plasticity achieves better performance by means of efficient reduction in weight variance over time. Performance also presents negative sensitivity to reduced levels of noise, showing that random firing has a functional value.

scholarly journals Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity

2012 ◽  
Vol 8 (7) ◽  
pp. e1002584 ◽  
Author(s):  
Matthieu Gilson ◽  
Tomoki Fukai ◽  
Anthony N. Burkitt
Keyword(s):  
Spectral Analysis ◽  
Spike Trains ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Input Spike

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

Equation-free analysis of spike-timing-dependent plasticity

2015 ◽  
Vol 109 (6) ◽  
pp. 701-714 ◽  
Author(s):  
Carlo R. Laing ◽  
Ioannis G. Kevrekidis
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity
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