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Related Experiment Video

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

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Published on: November 12, 2019

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Francisco Naveros¹, Jesus A Garrido¹, Richard R Carrillo¹

¹Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada Granada, Spain.

Frontiers in Neuroinformatics

|February 23, 2017

Summary

This summary is machine-generated.

This study introduces novel simulation techniques for neural models, enhancing accuracy and performance for complex systems like Leaky Integrate-and-Fire (LIF), Adaptive Exponential Integrate-and-Fire (AdEx), and Hodgkin-Huxley (HH) neural models.

Keywords:

CPU GPU bi-fixed-step integration methods event- and time-driven techniques look-up table spiking neural models

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Area of Science:

Computational neuroscience
Neural system modeling
Biophysics

Background:

Simulating neural structures is key to understanding brain computation.
Higher biological plausibility in models increases mathematical complexity and simulation challenges.
Existing event-driven and time-driven simulation techniques face accuracy and performance issues with complex neural models.

Purpose of the Study:

To address simulation challenges (accuracy and performance) in complex neural models.
To propose and evaluate novel simulation techniques for Leaky Integrate-and-Fire (LIF), Adaptive Exponential Integrate-and-Fire (AdEx), and Hodgkin-Huxley (HH) neural models.
To improve computational efficiency and precision in neural system simulations.

Main Methods:

Developed modifications for event-driven techniques: look-up table recombination and improved synchronous input handling.
Introduced the bi-fixed-step integration method for time-driven simulations, with automatic step-size adjustment for CPU and hybrid CPU-GPU platforms.
Compared proposed techniques against traditional event- and time-driven methods across varying neural complexity levels.

Main Results:

Proposed event-driven modifications enhance handling of incremental complexity and synchronous activity.
The bi-fixed-step integration method improves simulation accuracy and performance by adapting to neural model dynamics stiffness.
Modified techniques systematically outperform traditional methods as neural model complexity increases.

Conclusions:

Novel simulation techniques offer significant improvements in accuracy and performance for complex neural models.
The proposed methods provide efficient and precise tools for advancing computational neuroscience research.
These advancements facilitate more sophisticated modeling of neural computation and brain function.