Spike Timing Dependent Plasticity Enhances Integrated Information at the EEG Level: A Large-scale Brain Simulation Experiment

Author(s):  
Keiko Fujii ◽  
Hoshinori Kanazawa ◽  
Yasuo Kuniyoshi
Laser Physics ◽  
2021 ◽  
Vol 32 (1) ◽  
pp. 016201
Author(s):  
Tao Tian ◽  
Zhengmao Wu ◽  
Xiaodong Lin ◽  
Xi Tang ◽  
Ziye Gao ◽  
...  

Abstract Based on the well-known Fabry–Pérot approach, after taking into account the variation of bias current of the vertical-cavity semiconductor optical amplifier (VCSOA) according to the present synapse weight, we implement the optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a VCSOA with double optical spike injections, and numerically investigate the corresponding weight-dependent STDP characteristics. The simulation results show that, the bias current of VCSOA has significant effect on the optical STDP curve. After introducing an adaptive variation of the bias current according to the present synapse weight, the optical weight-dependent STDP based on VCSOA can be realized. Moreover, the weight training based on the optical weight-dependent STDP can be effectively controlled by adjusting some typical external or intrinsic parameters and the excessive adjusting of synaptic weight is avoided, which can be used to balance the stability and competition among synapses and pave a way for the future large-scale energy efficient optical spiking neural networks based on the weight-dependent STDP learning mechanism.


2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott

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.


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

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