English
Español
Log In
Email address
Password
Log in
Have you forgotten your password?
Communities & Collections
Research Outputs
Projects
Researchers
Statistics
Investigación Indoamérica
English
Español
Log In
Email address
Password
Log in
Have you forgotten your password?
Home
CRIS
Publications
Real-Time Display of Spiking Neural Activity of SIMD Hardware Using an HDMI Interface
Export
Statistics
Options
Real-Time Display of Spiking Neural Activity of SIMD Hardware Using an HDMI Interface
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Date Issued
2022
Author(s)
Vallejo-Mancero, B.
Nader, C.
Madrenas, J.
Zapata, M.
Centro de investigación en Mecatrónica y Sistemas Interactivos
Type
Conference Paper
DOI
10.1007/978-3-031-15934-3_60
URL
https://cris.indoamerica.edu.ec/handle/123456789/8585
Abstract
Spiking neural networks (SNN) are considered the third generation of artificial networks and are powerful computational models inspired by the function and structure of biological neural networks, to solve different types of problems such as pattern recognition, classification, signal processing, among others. SNN have also aroused the interest of neuroscientists intending to obtain new knowledge about the functions of the neuronal system through the analysis of the patterns observed in spike trains. Therefore, in addition to the development of hardware solutions that allow the execution of the different neural models, it is important, to provide tools for the visualization and analysis of the spike trains and the evolution of the neural parameters of the affected neurons in real-time. This work describes a new solution that takes the hardware emulator of evolved neural spiking system (HEENS) as the starting point, which is a bio-inspired architecture that emulates SNN using reconfigurable hardware implemented in field-programmable gate arrays (FPGAs). Reported development includes new dedicated hardware modules to interface HEENS with the high definition multimedia interface (HDMI) port, ensuring execution cycles within a time window of at least 1 ms, a period considered real-time in many neural applications. Tests of the synthesized architecture including the new tool have been carried out, executing different types of applications. The result is a friendly and flexible tool that has successfully allowed the visualization of pulse trains and neural parameters and constitutes an alternative for the monitoring and supervision of the SNN in real-time. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subjects
Non-immersive reality...
Scopus© citations
1
Acquisition Date
Jun 6, 2024
View Details
Views
2
Acquisition Date
Apr 3, 2025
View Details
google-scholar
View Details
Downloads
View Details