Published December 16, 2019
| Version v1
Publication
Building Blocks for Spikes Signals Processing
Description
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.
Abstract
Junta de Andalucía P06-TIC-O1417Abstract
Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Abstract
Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/90975
- URN
- urn:oai:idus.us.es:11441/90975