Investigating the Role of Refractory Periods in Neuronal Network Dynamics Using SNNAP and MATLAB
Session Number
IND STUDY 04 CMPS
Advisor(s)
Dr. Ashwin Mohan, Illinois Mathematics and Science Academy
Discipline
Independent Study
Start Date
17-4-2025 10:30 AM
End Date
17-4-2025 10:45 AM
Abstract
The refractory period plays a crucial role in shaping neuronal excitability and network dynamics. This study examines how absolute and relative refractory periods influence spike timing in a three-neuron network simulated in SNNAP (Simulator for Neural Networks and Action Potentials) and more complex neural networks. The network consists of a Hodgkin-Huxley (HH) neuron exciting gi_6 and gi_7 integrate-and-fire neurons through weighted synapses. Unexpectedly, post-synaptic neurons fired during the hyperpolarization phase of the presynaptic neuron rather than at the peak of the action potential. By adjusting synaptic conductance (g_syn) and threshold reset parameters, we analyzed how refractory dynamics affect spike timing and oscillatory behavior. Further analysis extends to MATLAB simulations, where we model single neurons and more detailed networks using Hodgkin-Huxley and integrate-andfire equations, as well as making more accurate models. By systematically varying membrane capacitance (Cm), synaptic delay, and threshold adaptation, we examine how refractory periods influence neural synchronization. These results provide insights into how refractory periods regulate network excitability and oscillatory activity. Future directions include studying adaptive refractory mechanisms and synaptic plasticity to explore how neural circuits dynamically adjust their firing properties over time.
Investigating the Role of Refractory Periods in Neuronal Network Dynamics Using SNNAP and MATLAB
The refractory period plays a crucial role in shaping neuronal excitability and network dynamics. This study examines how absolute and relative refractory periods influence spike timing in a three-neuron network simulated in SNNAP (Simulator for Neural Networks and Action Potentials) and more complex neural networks. The network consists of a Hodgkin-Huxley (HH) neuron exciting gi_6 and gi_7 integrate-and-fire neurons through weighted synapses. Unexpectedly, post-synaptic neurons fired during the hyperpolarization phase of the presynaptic neuron rather than at the peak of the action potential. By adjusting synaptic conductance (g_syn) and threshold reset parameters, we analyzed how refractory dynamics affect spike timing and oscillatory behavior. Further analysis extends to MATLAB simulations, where we model single neurons and more detailed networks using Hodgkin-Huxley and integrate-andfire equations, as well as making more accurate models. By systematically varying membrane capacitance (Cm), synaptic delay, and threshold adaptation, we examine how refractory periods influence neural synchronization. These results provide insights into how refractory periods regulate network excitability and oscillatory activity. Future directions include studying adaptive refractory mechanisms and synaptic plasticity to explore how neural circuits dynamically adjust their firing properties over time.