Neural avalanches exemplify intermittent behavior in brain dynamics through large-scale regional interactions and are crucial elements of brain dynamical behaviors. Originally introduced in the Self-Organized Criticality framework, these intermittent complex behaviors can also be examined through Temporal Complexity (TC) theory. Computational neural network models have become central in the neuroscience field. Izhikevich’s neuron model [1] provides a powerful yet simple framework for simulating networks with over 20 brain-like dynamic patterns, enabling studies of normal and pathological conditions. Our work analyzes the temporal complexity of neural avalanches and coincidence events in an Izhikevich Spiking Neural Network, comparing systems with and without Spike-Time Dependent Plasticity (STDP) [2] processes.
Role of Synaptic Plasticity in the Emergence of Temporal Complexity in a Izhikevich Spiking Neural Network
Marco Cafiso;
2026-01-01
Abstract
Neural avalanches exemplify intermittent behavior in brain dynamics through large-scale regional interactions and are crucial elements of brain dynamical behaviors. Originally introduced in the Self-Organized Criticality framework, these intermittent complex behaviors can also be examined through Temporal Complexity (TC) theory. Computational neural network models have become central in the neuroscience field. Izhikevich’s neuron model [1] provides a powerful yet simple framework for simulating networks with over 20 brain-like dynamic patterns, enabling studies of normal and pathological conditions. Our work analyzes the temporal complexity of neural avalanches and coincidence events in an Izhikevich Spiking Neural Network, comparing systems with and without Spike-Time Dependent Plasticity (STDP) [2] processes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
