Array Processing of Neural Signals Recorded from the Peripheral Nervous System for the Classification of Action Potentials

BackgroundRecording from the peripheral nervous system is key in the development of implantable neural interfaces. Despite a long history of using implantable electrodes for neuro-stimulation, it is difficult to make recordings from the nerves as signal amplitudes are often too small to be detected. Methods exist that are suitable for recording evoked potentials, but these require artificial stimulation of the nerve and thus have limited use in implanted neural interfaces.New methodIn order to address these issues new methods are developed to analyse spontaneously occurring action potentials by extending an approach called velocity selective recording, which uses longitudinally spaced electrodes to record action potentials as they propagate. The new methods using image processing techniques to automatically identify and classify action potentials without any prior knowledge of their morphology.ResultsSimulations are developed to test the methods, and a detailed experimental validation is performed using in-vivo recordings from the L5 dorsal rootlet of rat. Results show that this new approach can discriminate action potentials from both simulated and real recordings and the experimental validation demonstrates an ability to detect dermal stimulation by changes in the firing patterns of different axons.Comparison to existing methodsThis framework, unlike existing methods, is intrinsically suitable for recordings of spontaneous neural activity. Further it improves upon both the computational complexity and the overall performance of existing methods.ConclusionIt is possible to perform on-line discrimination and identification of action potentials without any prior knowledge of their morphology using new image processing inspired methods

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 vessel’s 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.<br/

A Reconfigurable Architecture for Implementing Locally Connected Neural Arrays

Moore’s law is rapidly approaching a long-predicted decline, and with it the performance gains of conventional processors are becoming ever more marginal. Cognitive computing systems based on neural networks have the potential to provide a solution to the decline of Moore’s law. Identifying common traits in neural systems can lead to the design of more efficient, robust and adaptable processors. Despite the potentials, large-scale neural systems remain difficult to implement due to constraints on scalability. Here we introduce a new hardware architecture for implementing locally connected neural networks that can model biological systems with a high level of scalability. We validate our architecture using a full model of the locomotion system of the Caenorhabditis elegans. Further, we show that our proposed architecture archives a nine-fold increase in clock speed over existing hardware models. Importantly the clock speed for our architecture is found to be independent of system size, providing an unparalleled level of scalability. Our approach can be applied to the modelling of large neural networks, with greater performance, easier configuration and a high level of scalability

Array Processing of Neural Signals Recorded from the Peripheral Nervous System for the Classification of Action Potentials

BackgroundRecording from the peripheral nervous system is key in the development of implantable neural interfaces. Despite a long history of using implantable electrodes for neuro-stimulation, it is difficult to make recordings from the nerves as signal amplitudes are often too small to be detected. Methods exist that are suitable for recording evoked potentials, but these require artificial stimulation of the nerve and thus have limited use in implanted neural interfaces.New methodIn order to address these issues new methods are developed to analyse spontaneously occurring action potentials by extending an approach called velocity selective recording, which uses longitudinally spaced electrodes to record action potentials as they propagate. The new methods using image processing techniques to automatically identify and classify action potentials without any prior knowledge of their morphology.ResultsSimulations are developed to test the methods, and a detailed experimental validation is performed using in-vivo recordings from the L5 dorsal rootlet of rat. Results show that this new approach can discriminate action potentials from both simulated and real recordings and the experimental validation demonstrates an ability to detect dermal stimulation by changes in the firing patterns of different axons.Comparison to existing methodsThis framework, unlike existing methods, is intrinsically suitable for recordings of spontaneous neural activity. Further it improves upon both the computational complexity and the overall performance of existing methods.ConclusionIt is possible to perform on-line discrimination and identification of action potentials without any prior knowledge of their morphology using new image processing inspired methods

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&times; 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

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 vessel’s 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.<br/