An STDP training algorithm for a spiking neural network with dynamic threshold neurons.
This paper proposes a supervised training algorithm for Spiking Neural Networks (SNNs) which modifies the Spike Timing Dependent Plasticity (STDP)learning rule to support both local and network level training with multiple synaptic connections and axonal delays. The training algorithm applies the rule to two and three layer SNNs, and is benchmarked using the Iris and Wisconsin Breast Cancer (WBC) data sets. The effectiveness of hidden layer dynamic threshold neurons is also investigated and results are presented.
Intelligent Systems Research Centre, University of Ulster, Magee Campus, School of Computing and Intelligent Systems, Derry, Northern Ireland, BT48 7JL, UK.
T J
TJ
Strain
L J
LJ
McDaid
T M
TM
McGinnity
L P
LP
Maguire
H M
HM
Sayers
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics