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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Sparse Spiking Neural-Like Membrane Systems on Graphics Processing Units.

Javier Hernández-Tello¹, Miguel Á Martínez-Del-Amor¹, David Orellana-Martín¹

¹Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, I3US, SCORE Lab, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain.

International Journal of Neural Systems

|May 16, 2024

Summary

This summary is machine-generated.

This study implements and parallelizes matrix compression methods for Spiking Neural P systems on GPUs. These optimized methods significantly improve simulation efficiency compared to existing GPU solutions.

Keywords:

GPU computing Membrane computing parallel simulation sparse matrices spiking neural P systems

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Parallel Computing

Background:

Spiking Neural P systems are simulated using matrix representations, often leading to inefficiencies with sparse matrices.
Existing parallel simulation methods rely on matrix-vector multiplication, which is resource-intensive for non-fully connected neural graphs.
Previous compression techniques for sparse matrices in this context were proposed but lacked implementation and parallelization.

Purpose of the Study:

To implement and parallelize two matrix compression methods for Spiking Neural P systems on GPUs.
To develop a new simulator for Spiking Neural P systems with delays incorporating these compression techniques.
To evaluate the performance of the implemented methods against state-of-the-art GPU libraries.

Main Methods:

Implementation of matrix compression algorithms for sparse adjacency matrices.
Parallelization of the compression methods and simulation on Graphics Processing Units (GPUs).
Development of a novel Spiking Neural P system simulator with delays and parallelized compression.

Main Results:

The implemented and parallelized compression methods demonstrate superior performance in Spiking Neural P system simulations.
Significant improvements in computational resource utilization (time and memory) were observed.
The new simulator with compression outperformed existing solutions based on standard GPU libraries.

Conclusions:

Matrix compression is crucial for efficient parallel simulation of sparse Spiking Neural P systems.
The developed GPU-based simulator with parallelized compression offers a significant advancement in performance.
These findings pave the way for more complex and efficient simulations of Spiking Neural P systems.