Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Area of Science:

• Computational neuroscience
• Neuroscience simulation

Background:

• Large-scale neuronal network simulations are crucial for understanding brain dynamics.
• Simulations including electrical synapses (gap junctions) alongside chemical synapses face scalability challenges due to communication overhead.

Purpose of the Study:

• To enhance the scalability of large-scale neuronal network simulations that include gap junctions.
• To enable efficient exploration of combined electrical and chemical synaptic interactions in complex brain models.

Main Methods:

• Integrated a framework for continuous interactions with a directed communication scheme for spikes.
• Leveraged the sparsity of gap junctions compared to chemical synapses.
• Implemented and tested the approach in the NEST simulator.

Main Results:

• Achieved excellent scalability for simulations with gap junctions.
• Accelerated large-scale simulations by over an order of magnitude.
• Demonstrated efficient simulation of networks with natural synapse densities across thousands of nodes.

Conclusions:

• The integrated framework significantly improves the performance of neuronal network simulations incorporating gap junctions.
• This advancement facilitates the large-scale computational study of neural dynamics involving both electrical and chemical signaling.
• Enables unprecedented exploration of brain network function at scale.