Mohammad Dehghani-Habibabadi

This paper presents a spiking neural network model that learns to recognize spatiotemporal spike patterns sequentially using biological synaptic mechanisms, addressing the stability-plasticity dilemma in neural learning.

The paper examines how varying ratios of excitatory to inhibitory neurons affect scale-free dynamics in a leaky integrate-and-fire model, showing critical behavior and extended dynamic range at specific ratios.

The paper shows that a combination of synaptic plasticity mechanisms can self-organize neurons to detect repeating spatio-temporal spike patterns, supporting the idea of precise temporal coding in the brain.