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

Department of Electronic and Electrical Engineering, University of Bath, Bath, UK.
Medical & Biological Engineering (Impact Factor: 1.5). 02/2012; 50(3):309-18. DOI: 10.1007/s11517-012-0874-z
Source: PubMed

ABSTRACT This paper describes the improvements to the theory of velocity selective recording and some simulation results. In this method, activity in 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 Q(v) 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 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes improvements to the theory of velocity selective recording (VSR) of neural signals. Action potentials are classified and differentiated based on their conduction velocities which can be calculated from concurrent neural recordings taking at different locations on a nerve. Existing work has focussed primarily on electrically evoked compound action potentials (CAPs) where only a single evoked response per velocity is recorded. This paper extends the theory of VSR to naturally occurring neural signals recorded from rat and attempts to identify the level of activity (firing rates) within particular velocity ranges.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods ('delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.
    IEEE Transactions on Biomedical Circuits and Systems 09/2013; 8(3). DOI:10.1109/TBCAS.2013.2277561 · 3.15 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents results from a pilot experiment in which the technique of velocity selective recording (VSR) was used to identify naturally occurring electroneurogram (ENG) signals within the intact nerve of a rat. Signals were acquired using a set of electrodes placed along the length of the nerve, formed from simple wire hooks. This basic form of recording has already been applied in-vivo to the analysis of electrically excited compound action potentials (CAPs) in both pig and frog, however, this method has never before been used to identify naturally occurring neural signals. Results in this paper highlight challenges which must be overcome in order for the transition to be made from electrically evoked potentials to naturally occurring signals.

Full-text (2 Sources)

Available from
Oct 20, 2014