Fibre-selective recording from the peripheral nerves of frogs using a multi-electrode cuff

Martin Schuettler, Nick Donaldson, Vipin Seetohul, John Taylor

Research output: Contribution to journalArticlepeer-review

39 Citations (SciVal)


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.
Original languageEnglish
Article number036016
JournalJournal of Neural Engineering
Issue number3
Publication statusPublished - 1 Jun 2013


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