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A compressed sensing integrate-and-fire neuron concept for massively parallel recordings
Publikationstyp
Conference Paper
Date Issued
2024
Sprache
English
Author(s)
Citation
IEEE International Symposium on Circuits and Systems (ISCAS 2024)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
ISBN
9798350330991
A compressed sensing integrate-and-fire neuron concept for massively parallel recordings is presented which expands the fundamental idea of superimposing timely sparse signals for data compression to any kind of continuous-time signals. Merging compressed sensing and amplitude-to-spike conversion, the proposed approach increases the information density and reduces the channel load. Combining multiple data-compressive neurons as a sensing array, further compression can be achieved when the spikes from different recording sites are superimposed on a single transmission channel. Signal reconstruction quality and transmission channel load are investigated to provide a strategy for selecting the design parameters of the proposed system. A proof-of-concept is presented, where a load per recording channel of 1 % under a relative reconstruction error of 0.32 % (SNR = 25 dB) is achieved.
Subjects
analog compression
channel reduction
compressed sensing
integrate-and-fire neuron
neural interface
sensor array
DDC Class
621.3: Electrical Engineering, Electronic Engineering