Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

2015 ◽  
Vol 25 (11) ◽  
pp. 113108 ◽  
Author(s):  
Hui Liu ◽  
Yongduan Song ◽  
Fangzheng Xue ◽  
Xiumin Li
Keyword(s):  
Neural Network ◽  
Learning Rule ◽  
Spike Timing ◽  
Dynamic Features ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

scholarly journals Spike-timing-dependent plasticity leads to gamma band responses in a neural network

2009 ◽  
Vol 101 (3) ◽  
pp. 227-240 ◽  
Author(s):  
Ingo Fründ ◽  
Frank W. Ohl ◽  
Christoph S. Herrmann
Keyword(s):  
Neural Network ◽  
Spike Timing ◽  
Gamma Band ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions

2013 ◽  
Vol 25 (12) ◽  
pp. 3113-3130 ◽  
Author(s):  
Jan-Moritz P. Franosch ◽  
Sebastian Urban ◽  
J. Leo van Hemmen
Keyword(s):  
Function Approximation ◽  
Sensory Input ◽  
Learning Rule ◽  
Animal Learn ◽  
Spike Timing ◽  
Time Dependent ◽  
Stable Configuration ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Tactile System

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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