Volume 12, Number 3

A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons


Mario Antoine Aoun, Montreal, Quebec, Canada


We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.


Competitive Hebbian Learning, Chaotic Spiking Neural Network, Adaptive Exponential Integrate and Fire (AdEx) Neuron, Spike Timing Dependent Plasticity, STDP, Chaos Theory, Synchronization, Coupling, Chaos Control, Time Delay, Regular Spiking Neuron, Nearest neighbour, Recurrent Neural Network.