The theory of velocity selective neural recording: a study based on simulation

John Taylor, Martin Schuettler, Christopher T Clarke, Nick Donaldson

Research output: Contribution to journalArticle

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Abstract

This paper describes improvements to the theory of velocity selective recording and some simulation results. In this method, activity is different groups of axons is discriminated by their propagation velocity. A multi-electrode cuff and an array of amplifiers produce multiple neural signals; if artificial delays are inserted and the signals are added, the activity in axons of the matched velocity are emphasized. We call this intrinsic velocity selective recording. However, simulation shows that interpreting the time signals is then not straight-forward and the selectivity Qv is low. New theory shows that bandpass filters improve the selectivity and explains why this is true in the time domain. A simulation study investigates the limits on the available velocity selectivity both with and without additive noise and with reasonable sampling rates and analogue-to-digital conversion (ADC) parameters. Bandpass filters can improve the selectivity by factors up to 7 but this depends on the speed of the action potential and the signal-to-noise ratio.
LanguageEnglish
Pages309-318
Number of pages10
JournalMedical and Biological Engineering and Computing
Volume50
Issue number3
Early online date24 Feb 2012
DOIs
StatusPublished - 1 Mar 2012

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Bandpass filters
Additive noise
Analog to digital conversion
Signal to noise ratio
Sampling
Electrodes
Axons

Cite this

The theory of velocity selective neural recording: a study based on simulation. / Taylor, John; Schuettler, Martin; Clarke, Christopher T; Donaldson, Nick.

In: Medical and Biological Engineering and Computing, Vol. 50, No. 3, 01.03.2012, p. 309-318.

Research output: Contribution to journalArticle

Taylor, John ; Schuettler, Martin ; Clarke, Christopher T ; Donaldson, Nick. / The theory of velocity selective neural recording: a study based on simulation. In: Medical and Biological Engineering and Computing. 2012 ; Vol. 50, No. 3. pp. 309-318.
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