Synaptic dynamics: Linear model and adaptation algorithm.
Linear model for synapse temporal dynamics and learning algorithm for synaptic adaptation in spiking neural networks are presented. The proposed linear model substantially simplifies analysis and training of spiking neural networks, meanwhile accurately models facilitation and depression dynamics in synapse. The learning rule is biologically plausible and is capable of simultaneously adjusting both of LTP and STP parameters of individual synapses in a network. To prove efficiency of the system, a small size spiking neural network is trained for generating different spike and bursting patterns of cortical neurons. The simulation results revealed that the linear model of synaptic dynamics along with the proposed STDP based learning algorithm can provide a practical tool for simulating and training very large scale spiking neural circuitry comprising of significant number of synapses and neurons.
Neural Dynamics Laboratory, University of Southern California, USA. ayousefi@usc.edu
Ali
A
Yousefi
Alireza A
AA
Dibazar
Theodore W
TW
Berger
D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D013569:Synapses / Q000502:physiology*