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: 1.73). 02/2012; 50(3):309-18. DOI: 10.1007/s11517-012-0874-z
Source: PubMed


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.

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Available from: John Taylor, Oct 20, 2014
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    • "So, for example, if B = 15 kHz, fL = 1.875 kHz (approximated to 2 kHz for simulation) and hence substituting into eqn (4) with d = 3 mm and N = 10, the lower bound on Qv at v0 = 30 m/s is 2.2. (b) Upper bound: In [4] we noted that the upper bound on velocity selectivity is set by noise considerations because the spectrum of the signal (see eqn (3)) decreases monotonically with frequency eventually merging with the noise floor of the system. Clearly the signal has no energy left beyond this frequency to drive a BPF and so it seems reasonable to choose this 'noise corner frequency' as the upper frequency limit that also determines the maximum available velocity selectivity. "
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    ABSTRACT: This paper describes the theory of velocity selective recording (VSR) of neural signals including some new developments. In particular new limits on available selectivity using bandpass filters are introduced and discussed. 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 describes a practical method to estimate the level of activity (firing rates) within particular velocity ranges.
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    ABSTRACT: Objective: We investigate the ability of the method of velocity selective recording (VSR) to determine the fibre types that contribute to a compound action potential (CAP) propagating along a peripheral nerve. Real-time identification of the active fibre types by determining the direction of action potential propagation (afferent or efferent) and velocity might allow future neural prostheses to make better use of biological sensor signals and provide a new and simple tool for use in fundamental neuroscience. Approach: Fibre activity was recorded from explanted Xenopus Laevis frog sciatic nerve using a single multi-electrode cuff that records whole nerve activity with 11 equidistant ring-shaped electrodes. The recorded signals were amplified, delayed against each other with variable delay times, added and band-pass filtered. Finally, the resulting amplitudes were measured. Main result: Our experiments showed that electrically evoked frog CAP was dominated by two fibre populations, propagating at around 20 and 40 m/s, respectively. The velocity selectivity, i.e. the ability of the system to discriminate between individual populations was increased by applying band-pass filtering. The method extracted an entire velocity spectrum from a 10 ms CAP recording sample in real time. Significance: Unlike the techniques introduced in the 1970s and subsequently, VSR requires only a single nerve cuff and does not require averaging to provide velocity spectral information. This makes it potentially suitable for the generation of highly-selective real-time control-signals for future neural prostheses. In our study, electrically evoked CAPs were analysed and it remains to be proven whether the method can reliably classify physiological nerve traffic.
    No preview · Article · May 2013 · Journal of Neural Engineering
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    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.
    No preview · Article · Sep 2013 · IEEE Transactions on Biomedical Circuits and Systems
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