Benjamin Metcalfe

A new method for spike extraction using velocity selective recording demonstrated with physiological ENG in Rat

BACKGROUND This paper describes a series of experiments designed to verify a new method of electroneurogram (ENG) recording that enables the rate of neural firing within prescribed bands of propagation velocity to be determined in real time. Velocity selective recording (VSR) has been proposed as a solution to the problem of increasing the information available from an implantable neural interface (typically with electrodes in circumferential nerve cuffs) and has been successful in transforming compound action potentials into the velocity domain. NEW METHOD The new method extends VSR to naturally-evoked (physiological) ENG in which the rate of neural firing at particular velocities is required in addition to a knowledge of the velocities present in the recording. RESULTS The experiments, carried out in rats required individual spikes to be distinct and non-overlapping, which could be achieved by a microchannel or small-bore cuff. In these experiments, strands of rat nerve were laid on ten hook electrodes in oil to demonstrate the principle. COMPARISON WITH EXISTING METHOD The new method generates a detailed overview of the firing rates of neurons based on their conduction velocity and direction of propagation. In addition it allows real time working in contrast to existing spike sorting methods using statistical pattern processing techniques. CONCLUSIONS Results show that by isolating neural activity based purely on conduction velocity it was possible to determine the onset of direct cutaneous stimulation of the L5 dermatome.

An enhancement to velocity selective discrimination of neural recordings: Extraction of neuronal firing rates

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.

Fibre-selective discrimination of physiological ENG using velocity selective recording: report on pilot rat experiments.

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.

An Analytical Comparison of Locally-Connected Reconfigurable Neural Network Architectures Using a C. elegans Locomotive Model

The scale of modern neural networks is growing rapidly, with direct hardware implementations providing significant speed and energy improvements over their software counterparts. However, these hardware implementations frequently assume global connectivity between neurons and thus suffer from communication bottlenecks. Such issues are not found in biological neural networks. It should therefore be possible to develop new architectures to reduce the dependence on global communications by considering the connectivity of biological networks. This paper introduces two reconfigurable locally-connected architectures for implementing biologically inspired neural networks in real time. Both proposed architectures are validated using the segmented locomotive model of the C. elegans, performing a demonstration of forwards, backwards serpentine motion and coiling behaviours. Local connectivity is discovered to offer up to a 17.5× speed improvement over hybrid systems that use combinations of local and global infrastructure. Furthermore, the concept of locality of connections is considered in more detail, highlighting the importance of dimensionality when designing neuromorphic architectures. Convolutional Neural Networks are shown to map poorly to locally connected architectures despite their apparent local structure, and both the locality and dimensionality of new neural processing systems is demonstrated as a critical component for matching the function and efficiency seen in biological networks.

Sonar imaging in extremely shallow inshore environments from an unmanned surface vehicle

Sonar imaging systems and autonomous platforms are becoming smaller and more affordable. Consequently, their applications are expanding further into the civilian domain. A good example and the focus of this work is an autonomous survey system for aiding police dive teams during underwater crime scene investigations. In this paper, we describe our ultra portable and low-cost unmanned surface vehicle (USV) currently under development for the London Metropolitan Police Service. In particular, we concentrate on the challenges associated with operating the USVs sonar payloads in the extremely shallow waters associated with relevant operational scenarios; these include, canals, lakes, and reservoirs with water depths commonly in the range of only 0.5 m to 3 m. In these cases, a key challenge is the corrupting influence of multipath reflections between the floor and water surfaces. We have taken a pragmatic approach to this multipath problem that uses in-situ assessment of the sonar performance for optimal data...

A New Method for Neural Spike Alignment: The Centroid Filter

Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared with the conventional methods. We present a mathematical analysis of the centroid method, characterizing its fundamental operation and performance. Furthermore, we show that the centroid method lends itself particularly well to hardware realization, and we present results from a low-power implementation that operates on an FPGA, consuming ten times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.

Modelling dynamic photovoltaic arrays for marine applications

This paper presents a new simulator platform with findings from experiments aiming to identify the electrical characteristics of a marine vessel covered in photovoltaic modules, operating in various sea conditions. More specifically, we show that by giving a solar array the ability to reconfigure the arrangement of its modules in real time, that significant improvements (up to 50%) in power yield can be achieved compared to typical static arrays. A bespoke MATLAB simulator has been developed in order to model the complex interplay between the electrical arrangement of photovoltaic modules, the position of the photovoltaic modules on the vessel, the vessels tilting motion on the surface of the sea and the resultant irradiance based on the position of the Sun in the sky. Our approach allows the user to define these factors using a simple and intuitive graphical user interface so that a range of scenarios can be quickly simulated. We have used a basic test strategy that allows us to measure the effectiveness of different arrays and quantify performance in terms of mean output power and power stability over a range of sea conditions. A key factor in the effectiveness of the use of marine survey vessels is their ability to remain at sea for extended periods, preferably avoiding the use of high-carbon fuel sources such as diesel generators. This is of particular importance when observing marine life as the platform needs to operate as quietly as possible. The ASV Global C-Enduro autonomous, self-righting platform is the initial application for this new energy harvesting system, with the aim to extend mission endurance. A second case study has also been performed in parallel with this, using a much more divergent orientation of onboard photovoltaic modules in order to asses the ability for a dynamic photovoltaic array to increase and stabilise power output.

Multi-channel Recordings from L2 Dorsal Root of Rat

Five channel recordings made using a set of hook electrodes from the intact dorsal rootlet of an adult female rat.

A Summary of Current and New Methods in Velocity Selective Recording (VSR) of Electroneurogram (ENG)

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 band pass 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.