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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU

Francisco Naveros, Niceto R Luque, Jesús A Garrido

IEEE Transactions on Neural Networks and Learning Systems

|August 29, 2014

Summary

This summary is machine-generated.

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This study introduces a hybrid CPU-GPU spiking neural network simulator. It efficiently models large-scale neural networks by combining event-driven and time-driven methods for improved performance.

Area of Science:

Computational Neuroscience
Artificial Intelligence
High-Performance Computing

Background:

Traditional CPU-based time-driven simulations excel for small spiking neural networks but struggle with large-scale systems.
Event-driven CPU simulations and time-driven GPU simulations offer performance advantages in specific scenarios.
Efficient simulation of complex, large-scale spiking neural networks is crucial for advancing computational neuroscience and AI.

Purpose of the Study:

To develop an efficient, hybrid CPU-GPU spiking neural network simulator.
To enable heterogeneous distribution and simulation of diverse neural models across different network scales.
To compare the performance of event-driven and time-driven simulation methods on hybrid platforms.

Main Methods:

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Developed a novel event-and-time-driven spiking neural network simulator for hybrid CPU-GPU architectures.

Implemented a strategy to simulate low-activity network parts on CPU (event-driven) and high-activity parts on CPU/GPU (time-driven).

Utilized a cerebellar-inspired neural network model with dense and converging layers for benchmarking.

Main Results:

The hybrid simulator efficiently handles large-scale spiking neural networks with heterogeneous components.
Comparative analysis demonstrated the performance benefits of the hybrid approach over traditional methods.
The simulator effectively models complex neural structures, including dense diverging and converging layers.

Conclusions:

The developed hybrid simulator offers a powerful and efficient solution for large-scale spiking neural network simulations.
This approach facilitates the study of bio-inspired and artificial neural networks with complex architectures.
The findings pave the way for more sophisticated and scalable neural network modeling on modern hardware.