A perspective on the control of FES-supported standing

This special section is about the control of electrical stimulators to restore standing functions to paraplegics. It addresses several important topics regarding the interactions of the intact central nervous systems (CNS) with the artificial control system. The topics are as follows: how paraplegics use their arms to help themselves stand up with functional electrical stimulation (FES); the user-driven artificial control of FESsupported standing up; a controller which is promising for the control of sitting down; the application of reinforcement machine learning for the controllers of standing up; arms-free\ud standing with voluntary upper body balancing and artificially controlled ankle stiffness; and cognitive feedback in balancing. This Commentary introduces the papers in this section and relates them to earlier research

The Design of a Low Noise, Multi-Channel Recording System for Use in Implanted Peripheral Nerve Interfaces

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems

Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider

SAF-A promotes origin licensing and replication fork progression to ensure robust DNA replication

Funding CC was supported by a BBSRC EASTBIO Doctoral Training programme PhD studentship. SH was supported by Daiwa Anglo-Japanese Foundation 812 (12928/13746). Work in the Hiraga-Donaldson lab supported by Cancer Research UK awards C1445/A19059 and DRCPGM\100013. NG is supported by Medical Research Council (MC_UU_00007/13) Acknowledgements Information for SAF-A expression was obtained at The Cancer Genome Atlas TCGA) Research Network (https://www.cancer.gov/tcga). We thank Dr Ryu-suke Nozawa for help in the early stage of the project, and Professor Julian Blow for advice on the 3D licensing assay. Thanks to the staff of the Iain Fraser Cytometry Centre, and Microscopy and Histology facility at the University of Aberdeen.Peer reviewedPostprin

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 to conventional methods. We present a mathematical analysis of the centroid method, characterising its fundamental operation and performance. Further, we show that the centroid method lends itself particularly well to hardware realisation and we present results from a low-power implementation that operates on an FPGA, consuming 10 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

A summary of the theory of velocity selective neural recording

This paper describes improvements to the technique of velocity selective recording (VSR) in which multiple neural signals are matched and summed to identify excited axon populations in terms of velocity. This form of recording has been termed intrinsic velocity selective recording (IVSR). The signals are acquired using a multi-electrode cuff (MEC) which is now available as a component for use in implantable neuroprostheses. The improvements outlined in the paper involve the use of bandpass filters at the output of the system which allows a higher level of selectivity to be obtained than is possible using IVSR. © 2011 IEEE

Microchannel neural interface manufacture by stacking silicone and metal foil laminae

Objective: Microchannel neural interfaces (MNIs) overcome problems with recording from peripheral nerves by amplifying signals independent of Node of Ranvier position. Selective recording and stimulation using an MNI requires good insulation between microchannels and a high electrode density. We propose that stacking microchannel laminae will improve selectivity over single layer MNI designs due to the increase in electrode number and an improvement in microchannel sealing. Approach: This paper describes a manufacturing method for creating MNIs which overcomes limitations on electrode connectivity and microchannel sealing. Laser cut silicone – metal foil laminae were stacked using plasma bonding to create an array of microchannels containing tripolar electrodes. Electrodes were DC etched and electrode impedance and cyclic voltammetry were tested. Main results: MNIs with 100 µm and 200 µm diameter microchannels were manufactured. High electrode density MNIs are achievable with electrodes present in every microchannel. Electrode impedances of 27.2 ± 19.8 kΩ at 1kHz were achieved. Following 2 months of implantation in Lewis rat sciatic nerve, micro-fascicles were observed regenerating through the MNI microchannels. Significance: Selective microchannel neural interfaces with the peripheral nervous system may allow upper limb amputees to control prostheses intuitively

An Implantable ENG Detector with In-System Velocity Selective Recording (VSR) Capability

Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented