Paper Info
Title
Building Blocks For Spikes Signals Processing
Abstract
Neuromorphic engineers study models and implementations of systems that mimic neurons behavior in the brain. Neuro-inspired systems commonly use spikes to represent information. This representation has several advantages: its robustness to noise thanks to repetition, its continuous and analog information representation using digital pulses, its capacity of pre-processing during transmission time, ..., Furthermore, spikes is an efficient way, found by nature, to codify, transmit and process information. In this paper we propose, design, and analyze neuro-inspired building blocks that can perform spike-based analog filters used in signal processing. We present a VHDL implementation for FPGA. Presented building blocks take advantages of the spike rate coded representation to perform a massively parallel processing without complex hardware units, like floating point arithmetic units, or a large memory. Those low requirements of hardware allow the integration of a high number of blocks inside a FPGA, allowing to process fully in parallel several spikes coded signals.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596845
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010
Keywords
Field
DocType
hardware description languages,massively parallel processing,neurophysiology,vhdl,floating point arithmetic,parallel processing,fpga,signal processing,field programmable gate arrays
Signal processing,Massively parallel,Computer science,Floating point,Neuromorphic engineering,Field-programmable gate array,Robustness (computer science),Artificial intelligence,VHDL,Machine learning,Hardware description language
Conference
ISSN
Citations 
PageRank 
2161-4393
8
0.59
References 
Authors
13
5
Name
Order
Citations
PageRank
Angel Jiménez-fernandez19620.53
Alejandro Linares-barranco247353.18
Rafael Paz-vicente320013.22
Gabriel Jiménez48710.18
Antón Civit513519.32