The Experts below are selected from a list of 38241 Experts worldwide ranked by ideXlab platform
Chris Eliasmith - One of the best experts on this subject based on the ideXlab platform.
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Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks
Neural computation, 2020Co-Authors: Andreas Stöckel, Chris EliasmithAbstract:Nonlinear interactions in the dendritic tree play a key role in Neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking Neural networks, such as the Neural Engineering Framework, tend to assume a linear superposition of postsynaptic currents. In this letter, we present a series of extensions to the Neural Engineering Framework that facilitate the construction of networks incorporating Dale's principle and nonlinear conductance-based synapses. We apply these extensions to a two-compartment LIF neuron that can be seen as a simple model of passive dendritic computation. We show that it is possible to incorporate neuron models with input-dependent nonlinearities into the Neural Engineering Framework without compromising high-level function and that nonlinear postsynaptic currents can be systematically exploited to compute a wide variety of multivariate, band-limited functions, including the Euclidean norm, controlled shunting, and nonnegative multiplication. By avoiding an additional source of spike noise, the function approximation accuracy of a single layer of two-compartment LIF neurons is on a par with or even surpasses that of two-layer spiking Neural networks up to a certain target function bandwidth.
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Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework.
arXiv: Neurons and Cognition, 2017Co-Authors: Andreas Stöckel, Aaron R. Voelker, Chris EliasmithAbstract:The mathematical model underlying the Neural Engineering Framework (NEF) expresses neuronal input as a linear combination of synaptic currents. However, in biology, synapses are not perfect current sources and are thus nonlinear. Detailed synapse models are based on channel conductances instead of currents, which require independent handling of excitatory and inhibitory synapses. This, in particular, significantly affects the influence of inhibitory signals on the neuronal dynamics. In this technical report we first summarize the relevant portions of the NEF and conductance-based synapse models. We then discuss a na\"ive translation between populations of LIF neurons with current- and conductance-based synapses based on an estimation of an average membrane potential. Experiments show that this simple approach works relatively well for feed-forward communication channels, yet performance degrades for NEF networks describing more complex dynamics, such as integration.
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Methods for applying the Neural Engineering Framework to neuromorphic hardware
arXiv: Neurons and Cognition, 2017Co-Authors: Aaron R. Voelker, Chris EliasmithAbstract:We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to implement linear and nonlinear dynamical systems that exploit axonal transmission time-delays, and to fully account for nonideal mixed-analog-digital synapses that exhibit higher-order dynamics with heterogeneous time-constants. This summarizes earlier versions of these methods that have been discussed in a more biological context (Voelker & Eliasmith, 2017) or regarding a specific neuromorphic architecture (Voelker et al., 2017).
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extending the Neural Engineering framework for nonideal silicon synapses
International Symposium on Circuits and Systems, 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
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ISCAS - Extending the Neural Engineering framework for nonideal silicon synapses
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
I-chi Lee - One of the best experts on this subject based on the ideXlab platform.
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Assembly of polyelectrolyte multilayer films on supported lipid bilayers to induce Neural stem/progenitor cell differentiation into functional neurons.
ACS applied materials & interfaces, 2014Co-Authors: I-chi LeeAbstract:The key factors affecting the success of Neural Engineering using Neural stem/progenitor cells (NSPCs) are the neuron quantity, the guidance of neurite outgrowth, and the induction of neurons to form functional synapses at synaptic junctions. Herein, a biomimetic material comprising a supported lipid bilayer (SLB) with adsorbed sequential polyelectrolyte multilayer (PEM) films was fabricated to induce NSPCs to form functional neurons without the need for serum and growth factors in a short-term culture. SLBs are suitable artificial substrates for Neural Engineering due to their structural similarity to synaptic membranes. In addition, PEM film adsorption provides protection for the SLB as well as the ability to vary the surface properties to evaluate the effects of physical and mechanical signals on NSPC differentiation. Our results revealed that NSPCs were inducible on SLB–PEM films consisting of up to eight alternating layers. In addition, the process outgrowth length, the percentage of differentiated n...
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Facilitating Neural stem/progenitor cell niche calibration for Neural lineage differentiation by polyelectrolyte multilayer films
Colloids and surfaces. B Biointerfaces, 2014Co-Authors: I-chi LeeAbstract:Neural stem/progenitor cells (NSPCs) are a possible candidate for advancing development and lineage control in Neural Engineering. Differentiated protocols have been developed in this field to generate Neural progeny and to establish Neural networks. However, continued refinement is required to enhance differentiation specificity and prevent the generation of unwanted cell types. In this study, we fabricated a niche-modulated system to investigate surface effects on NSPC differentiation by the formation of polyelectrolyte multilayer (PEM) films governed by electrostatic interactions of poly-l-glutamine acid as a polyanion and poly-l-lysine as a polycation. The serum- and chemical agent-free system provided a clean and clear platform to observe in isolation the interaction between surface niche and stem cell differentiation. We found that NSPCs were inducible on PEM films of up to eight alternating layers. In addition, neurite outgrowth, neuron percentage, and synaptic function were regulated by layer number and the surface charge of the terminal layer. The average process outgrowth length was over 500μm on PLL/PLGA(n=7.5) only after 3 days of culture. Moreover, the quantity and quality of the differentiated neurons were enhanced as the number of layers increased, especially when the terminal layer was poly-l-lysine. Our results achieve important targets of Neural Engineering, including long processes, large Neural network size, and large amounts of functional neurons. Our methodology for nanoscale control of material deposition can be successfully applied for surface modification, Neural niche modulation, and Neural Engineering applications.
Terrence C Stewart - One of the best experts on this subject based on the ideXlab platform.
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BioCAS - Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018Co-Authors: Kate D. Fischl, Terrence C Stewart, Andreas G. Andreou, Kaitlin FairAbstract:The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation. This work describes an implementation of the NEF on IBM's TrueNorth Neurosynaptic system. Using one TrueNorth chip, a NEF Neural population of 629 neurons representing five dimensions is demonstrated on hardware. However, the crossbar array architecture itself, utilized in the TrueNorth hardware, can be used to compute the basic NEF calculations for any sized Neural population, representing any dimensionality. The computation time is a function of the maximum values used in the computations.
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extending the Neural Engineering framework for nonideal silicon synapses
International Symposium on Circuits and Systems, 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
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ISCAS - Extending the Neural Engineering framework for nonideal silicon synapses
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
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an efficient spinnaker implementation of the Neural Engineering framework
International Joint Conference on Neural Network, 2015Co-Authors: Andrew Mundy, Terrence C Stewart, James C Knight, Steve FurberAbstract:By building and simulating Neural systems we hope to understand how the brain may work and use this knowledge to build Neural and cognitive systems to tackle Engineering problems. The Neural Engineering Framework (NEF) is a hypothesis about how such systems may be constructed and has recently been used to build the world's first functional brain model, Spaun. However, while the NEF simplifies the design of Neural networks, simulating them using standard computer hardware is still computationally expensive - often running far slower than biological real-time and scaling very poorly: problems the SpiNNaker neuromorphic simulator was designed to solve. In this paper we (1) argue that employing the same model of computation used for simulating general purpose spiking Neural networks on SpiNNaker for NEF models results in suboptimal use of the architecture, and (2) provide and evaluate an alternative simulation scheme which overcomes the memory and compute challenges posed by the NEF. This proposed method uses factored weight matrices to reduce memory usage by around 90% and, in some cases, simulate 2000 neurons on a processing core - double the SpiNNaker architectural target.
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IJCNN - An efficient SpiNNaker implementation of the Neural Engineering Framework
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: Andrew Mundy, Terrence C Stewart, James C Knight, Steve FurberAbstract:By building and simulating Neural systems we hope to understand how the brain may work and use this knowledge to build Neural and cognitive systems to tackle Engineering problems. The Neural Engineering Framework (NEF) is a hypothesis about how such systems may be constructed and has recently been used to build the world's first functional brain model, Spaun. However, while the NEF simplifies the design of Neural networks, simulating them using standard computer hardware is still computationally expensive - often running far slower than biological real-time and scaling very poorly: problems the SpiNNaker neuromorphic simulator was designed to solve. In this paper we (1) argue that employing the same model of computation used for simulating general purpose spiking Neural networks on SpiNNaker for NEF models results in suboptimal use of the architecture, and (2) provide and evaluate an alternative simulation scheme which overcomes the memory and compute challenges posed by the NEF. This proposed method uses factored weight matrices to reduce memory usage by around 90% and, in some cases, simulate 2000 neurons on a processing core - double the SpiNNaker architectural target.
Aaron R. Voelker - One of the best experts on this subject based on the ideXlab platform.
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Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework.
arXiv: Neurons and Cognition, 2017Co-Authors: Andreas Stöckel, Aaron R. Voelker, Chris EliasmithAbstract:The mathematical model underlying the Neural Engineering Framework (NEF) expresses neuronal input as a linear combination of synaptic currents. However, in biology, synapses are not perfect current sources and are thus nonlinear. Detailed synapse models are based on channel conductances instead of currents, which require independent handling of excitatory and inhibitory synapses. This, in particular, significantly affects the influence of inhibitory signals on the neuronal dynamics. In this technical report we first summarize the relevant portions of the NEF and conductance-based synapse models. We then discuss a na\"ive translation between populations of LIF neurons with current- and conductance-based synapses based on an estimation of an average membrane potential. Experiments show that this simple approach works relatively well for feed-forward communication channels, yet performance degrades for NEF networks describing more complex dynamics, such as integration.
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Methods for applying the Neural Engineering Framework to neuromorphic hardware
arXiv: Neurons and Cognition, 2017Co-Authors: Aaron R. Voelker, Chris EliasmithAbstract:We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to implement linear and nonlinear dynamical systems that exploit axonal transmission time-delays, and to fully account for nonideal mixed-analog-digital synapses that exhibit higher-order dynamics with heterogeneous time-constants. This summarizes earlier versions of these methods that have been discussed in a more biological context (Voelker & Eliasmith, 2017) or regarding a specific neuromorphic architecture (Voelker et al., 2017).
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extending the Neural Engineering framework for nonideal silicon synapses
International Symposium on Circuits and Systems, 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
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ISCAS - Extending the Neural Engineering framework for nonideal silicon synapses
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017Co-Authors: Aaron R. Voelker, Ben V Benjamin, Terrence C Stewart, Kwabena Boahen, Chris EliasmithAbstract:The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here, we analytically extend the NEF to directly harness the dynamics provided by heterogeneous mixed-analog-digital synapses. This theory is successfully validated by simulating two fundamental dynamical systems in Nengo using circuit models validated in SPICE. Thus, our work reveals the potential to engineer robust neuromorphic systems with well-defined high-level behaviour that harness the low-level heterogeneous properties of their physical primitives with millisecond resolution.
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IJCNN - Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: James C Knight, Chris Eliasmith, Aaron R. Voelker, Andrew Mundy, Steve FurberAbstract:The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear. The Neural Engineering Framework (NEF) is a hypothesis about how large-scale Neural systems represent values using populations of spiking neurons, and transform them using functions implemented by the synaptic weights between populations. By exploiting the fact that these connection weight matrices are factorable, we have recently shown that static NEF models can be simulated very efficiently using the SpiNNaker neuromorphic architecture. In this paper, we demonstrate how this approach can be extended to efficiently support both supervised and unsupervised learning rules designed to operate on these factored matrices. We then present a heteroassociative memory architecture built using these learning rules and prove that it is capable of learning a human-scale semantic network. Finally we demonstrate a 100 000 neuron version of this architecture running on the SpiNNaker simulator with a speed-up exceeding 150x when compared to the Nengo reference simulator.
Dominique M Durand - One of the best experts on this subject based on the ideXlab platform.
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Neural Engineering - A New Discipline for Analyzing and Interacting with the Nervous System
Methods of information in medicine, 2007Co-Authors: Dominique M DurandAbstract:Objectives: The field of Neural Engineering focuses on an area of research at the interface between neuroscience and Engineering. The area of Neural Engineering was first associated with the brain machine interface but is much broader and encompasses experimental, computational, and theoretical aspects of Neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, Neural prostheses, artificial and biological Neural circuits, Neural control, Neural tissue regeneration, Neural signal processing, Neural modelling and neuro-computation. One of the goals of Neural Engineering is to develop a selective interface for the peripheral nervous system. Methods: Nerve cuffs electrodes have been developed to either reshape or maintain the nerve into an elongated shape in order to increase the circumference to cross sectional ratio. It is then possible to place many electrodes around the nerve to achieve selectivity. This new cuff (flat interface nerve electrode: FINE) was applied to the hypoglossal nerve and the sciatic nerve in dogs and cats to estimate the selectivity of the interface. Results: By placing many contacts close to the axons, three different types of selectivity were achieved: 1) The FINE could generate a high degree of stimulation selectivity as estimated by the individual fascicle recording. 2) Similarly, recording selectivity was also demonstrated and blind source algorithms were applied to recover the signals. 3) Finally, by placing arrays of electrodes along the nerve, small fiber diameters could be excited before large fibers thereby reversing the recruitment order. Conclusion: Taking advantage of the fact that nerves are not round but oblong or flat allows a novel design for selective nerve interface with the peripheral nervous system. This new design has found applications in many disorders of the nervous system such as bladder incontinence, obstructive sleep apnea and stroke.
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Neural interfacing: not just BCI or BMI!
Journal of Neural Engineering, 2006Co-Authors: Dominique M DurandAbstract:There is a very strong interest in the feasibility of a brain machine interface as indicated by the number of submissions, citations and downloads from the Journal of Neural Engineering in this area (see the previous editorial, Durand 2006 J. Neural Eng.3 (3) . This interest is clearly understandable. The ability to decode and interpret brain signals could be of critical importance for not only understanding how the brain works but also to control prosthetic devices by pure thought. However, there is a lot more to Neural interfacing than the brain machine interface. The Neural Interfaces Workshop was held at the Bethesda North Marriott Hotel and Conference Center on 21–23 August, 2006. This meeting included functional neuromuscular/electrical stimulation, auditory prosthesis, cortical prosthesis, microelectrode array technology as well as brain computer/machine interfaces. The meeting was attended by a diverse group of scientists, engineers, and clinicians, representing the basic and applied science aspects of Neural interfaces. Support for this meeting came from the following institutes within the NIH: the National Institute of Neurological Disorders and Stroke (NINDS), National Institute on Aging, National Institute of Biomedical Imaging and BioEngineering, National Institute of Mental Health, and National Institute on Deafness and other Communication Disorders. This issue of the Journal of Neural Engineering has a dedicated section covering special aspects of Neural interfacing presented at the conference. A report on the presentations and the discussions at the conference written by NIH program officers can be found on page S137 Chen et al. Several papers invited by the Journal of Neural Engineering were chosen to reflect the breadth of Neural interfacing within the field of Neural Engineering. The papers cover a wide range of Neural systems from molecular interfacing to clinical restoration of standing/walking in paralyzed patients. The first paper from the Biomedical Engineering Department at Stanford University (Aravanis et al, S143; see also the front cover of the issue) is the first manuscript published in the Journal of Neural Engineering on optogenetic interfacing technology. Neurons in the central nervous system can be genetically modified to respond to light activation in order to control their activity. The second paper from the Case Western Reserve University (Tesfayesus and Durand, page S157 reports on the developments of new methods to recover nerve fascicular signals within the peripheral nervous system. The third paper by Weber et al on page S168 (University of Pittsburg and University of Alberta) describes a set of experiments involving a Neural interface with dorsal root ganglia. As noted by the reviewer: `the manuscript is of high interest because it involves DRG recordings in awake behaving animals and attempts to use a combination of recordings to predict the locomotor state'. The final paper (Mushahwar et al, page S181) reviews four different interfacing technologies for restoring standing/walking in patients with spinal cord injuries: surface functional electrical stimulation, implanted muscle-based electrodes, peripheral nerve intraNeural arrays and intraspinal microstimulation (ISMS) of the lumbosacral spinal cord. The Neural Interfaces Workshop has now grown too big and will be replaced by the Neural Interfaces Conference. This change from a NIH-led workshop to a NIH-sponsored conference reflects the strong leadership from the scientific community in this multi-disciplinary area. Announcements for the Neural Interfaces Conference to be held in Spring 2008 will be forthcoming.
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The present and future
Journal of Neural Engineering, 2006Co-Authors: Dominique M DurandAbstract:Neural Engineering has grown substantially in the last few years and it is time to review the progress of the first journal in this field. Journal of Neural Engineering (JNE) is a quarterly publication that started in 2004. The journal is now in its third volume and eleven issues, consisting of 114 articles in total, have been published since its launch. The editorial processing times have been kept to a minimum, the receipt to first decision time is 41 days, on average, and the time from receipt to publication has been maintained below three months. It is also worth noting that it is free to publish in Journal of Neural Engineering—there are no author fees—and once published the articles are free online for the first month. The journal has been listed in Pubmed® since 2005 and has been accepted by ISI® in 2006. Who is reading Journal of Neural Engineering? The number of readers of JNE has increased significantly from 8050 full-text downloads in 2004 to 14 900 in 2005 and the first seven months of 2006 have already seen 12 800 downloads. The top users in 2005 were the Microsoft Corporation, Stanford University and the University of Michigan. The list of top ten users also includes non-US institutions: University of Toronto, University of Tokyo, Hong Kong Polytechnic, National Library of China and University College London, reflecting the international flavor of the journal. What are the hot topics in Neural Engineering? Based on the number of downloads and citations for 2004–2005, the top three topics are: (1) Brain–computer interfaces (2) Visual prostheses (3) Neural modelling Several other topics such as microelectrode arrays, Neural signal processing, Neural dynamics and Neural circuit Engineering are also in the top ten. Where are Journal of Neural Engineering articles cited? JNE articles have reached a wide audience and have been cited in of some of the best journals in physiology and neuroscience such as Nature Neuroscience, Journal of Neuroscience, Trends in Neuroscience, Journal of Physiology, Proceedings of the National Academy of Science as well as in Engineering and physics journals such as Annals of Biomedical Engineering, Physical Review Letters and IEEE Transactions on Biomedical Engineering. However, the number of citations in clinical journals is limited. What is special about Journal of Neural Engineering? JNE has published two special issues: (1) The Eye and the Chip (visual prostheses) (vol. 2, (1), 2005) and (2) Sensory Integration: Role of Internal Models (vol. 2, (3), 2005). These special issues have attracted a lot of attention based on the number of article downloads. JNE also publishes tutorials intended to provide background information on specific topics such as classification, sensory substitution and cortical Neural prosthetics. A series of tutorials from the 3rd Neuro-IT and NeuroEngineering Summer School has been published with the first appearing in vol. 2 (4), 2005. What is in the future for Journal of Neural Engineering? The goal of any journal should be to provide a particular field with the best venue for scientists and engineers to make their work available and noticeable to the rest of the community. In particular, attracting a strong readership base and high quality manuscripts should be the first priority. Providing accurate, reliable and speedy reviews should be the next. With an international board of experts in the field of Neural Engineering, a solid base of reviewers, readers and contributors, JNE is in a strong position to continue to serve the Neural Engineering community. However, this is still a small community and growth is essential for continued success in this area. There are two areas of expansion of great interest for the field of Neural Engineering currently poised between basic science on one hand and clinical implementation on the other: translational neuroscience and therapeutic Neural Engineering. We should strive to bridge the gap between basic neuroscience, clinical science and Engineering by attracting contributions from neuroscientists and clinicians with an interest in Neural Engineering. I urge members of the Neural Engineering community to encourage their colleagues in these areas to consider JNE for publication of those manuscripts at the interface with neuroscience and Engineering. I would like to take this opportunity to acknowledge the work of the board members, the reviewers of the articles and the staff at the Institute of Physics Publishing for their contribution to the Journal of Neural Engineering.
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What is Neural Engineering?
Journal of Neural Engineering, 2006Co-Authors: Dominique M DurandAbstract:It is only recently that the term Neural Engineering or NeuroEngineering first appeared. The emergence of this new field can be attributed to the recognition that engineers, neuroscientists and clinicians should be working together to address the problems associated with the complexity of the nervous system. Neural Engineering has generated a lot of excitement not only for the development of interfaces between the brain and computers but for its mostly untapped potential to develop treatment for patients with neurological disorders such as strokes or epilepsy. Now the field has matured significantly as evidenced by its strong and regular presence at various conferences around the world and the growth in the number of published papers in the area. As a result, the scope of the field has evolved and a clear definition of Neural Engineering is needed. The editorial board of the Journal of Neural Engineering defines the field as follows: `Neural Engineering is an emerging interdisciplinary research area that brings to bear neuroscience and Engineering methods to analyze neurological function as well as to design solutions to problems associated with neurological limitations and dysfunction'. The main goal of the field is to solve neuroscience-related problems and to provide rehabilitative solutions for nervous system conditions. The emphasis on Engineering and quantitative methodology applied to the nervous system distinguishes Neural Engineering from traditional areas in neuroscience such as neurophysiology. The integration between neuroscience and Engineering separates Neural Engineering from other Engineering disciplines such as artificial Neural networks. Neural Engineering is situated between and draws heavily from basic neuroscience on one hand and clinical neuroscience (neurology) on the other. The field of Neural Engineering encompasses experimental, computational, theoretical, clinical and applied aspects of research areas at the molecular, cellular and systems levels. Although overlap between various topics exists (i.e. neuromodulation and neuroprostheses), all these areas are well established and have recognizable identities. Neural Engineering Scope • brain-machine (computer) interface • Neural interfacing • neurotechnology • neuroelectronics • neuromodulation • Neural prostheses • Neural control • neuro-rehabilitation • neuro-diagnostics • neuro-therapeutics • neuromechanical systems • neurorobotics • neuroinformatics • neuroimaging • Neural circuits: artificial and biological • neuromorphic Engineering • Neural tissue regeneration • Neural signal processing • theoretical and computational neuroscience • systems neuroscience • translational neuroscience However, the definition and scope of Neural Engineering are best determined by the scientists and engineers that practice it and this is only an overview of the field as it is understood today. The future of this exciting new field will be determined not by what we believe Neural Engineering should be but by its success in improving human health and quality of life through restoration and enhancement of the function of the nervous system.
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Why we need a new journal in Neural Engineering
Journal of Neural Engineering, 2004Co-Authors: Dominique M DurandAbstract:The field of Neural Engineering crystallizes for many engineers and scientists an area of research at the interface between neuroscience and Engineering. For the last 15 years or so, the discipline of Neural Engineering (neuroEngineering) has slowly appeared at conferences as a theme or track. The first conference devoted entirely to this area was the 1st International IEEE EMBS Conference on Neural Engineering which took place in Capri, Italy in 2003. Understanding how the brain works is considered the ultimate frontier and challenge in science. The complexity of the brain is so great that understanding even the most basic functions will require that we fully exploit all the tools currently at our disposal in science and Engineering and simultaneously develop new methods of analysis. While neuroscientists and engineers from varied fields such as brain anatomy, Neural development and electrophysiology have made great strides in the analysis of this complex organ, there remains a great deal yet to be uncovered. The potential for applications and remedies deriving from scientific discoveries and breakthroughs is extremely high. As a result of the growing availability of micromachining technology, research into neurotechnology has grown relatively rapidly in recent years and appears to be approaching a critical mass. For example, by understanding how neuronal circuits process and store information, we could design computers with capabilities beyond current limits. By understanding how neurons develop and grow, we could develop new technologies for spinal cord repair or central nervous system repair following neurological disorders. Moreover, discoveries related to higher-level cognitive function and consciousness could have a profound influence on how humans make sense of their surroundings and interact with each other. The ability to successfully interface the brain with external electronics would have enormous implications for our society and facilitate a revolutionary change in the quality of life of persons with sensory and/or motor deficits. Microelectrode technology represents the initial step towards this goal and has already improved the quality of life of many patients, as is evident from the success of auditory prostheses. The cost to society of neurological disorders such as stroke, Parkinson's disease, Alzheimer's disease and epilepsy is staggering. Stroke, which is the third leading cause of death in North America, runs up costs of $40 billion to society per year for its treatment. Costs associated with brain disorders are estimated at $285 billion. Breakthroughs in this field will have a significant impact on the market for enabling technologies. The market for neurological medical devices totaled $2 billion in 1999 and is projected to grow at a rate of 20 to 30% in the next ten years, far outpacing the market for cardiac devices. Although we have all recognized the importance of interdisciplinary research (see the NIH Road map at http://nihroadmap.nih.gov/), the fields of neuroscience and Engineering have remained compartmentalized. Collaboration is still difficult since the language of these disciplines is different. Moreover, the scientific journals in these fields are also clearly separate. Researchers involved in Neural Engineering have a choice of publishing their research in either neuroscience-oriented journals such as Journal of Neuroscience, Journal of Neurophysiology and Brain Research or in Engineering journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Systems and Rehabilitation and Annals of Biomedical Engineering. There is no journal currently available focusing on the interdisciplinary field of Neural Engineering. In order to capitalize on the potential of Neural Engineering to investigate Neural function and to solve problems related to Neural disorders, it is necessary to break down the traditional barriers between neuroscientists and engineers not just in the laboratory but also in the publication of scientific papers. We do, therefore, need a new journal that provides a platform for this emerging interdisciplinary field of Neural Engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that spans the disciplines. Journal of Neural Engineering will provide this platform. The new journal will publish full-length articles of the highest quality and importance in the field of Neural Engineering at the molecular, cellular and systems levels. The scope of Journal of Neural Engineering encompasses experimental, computational and theoretical aspects of Neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, Neural prostheses, artificial and biological Neural circuits, Neural control, Neural tissue regeneration, Neural signal processing, Neural modeling and neuro-computation. The scope of the journal has both depth and breadth in areas relevant to the interface between neuroscience and Engineering. There will be two Editors-in-Chief, with expertise covering both Engineering and neuroscience. Experts in the areas encompassed by the journal's scope have been identified for the Editorial Board and the composition of the board will be continually updated to address the developments in this new and exciting field. The first issue of this new journal covers a variety of topics that combine neuroscience and Engineering: mental state recognition from EEG signals, analysis of body motion in Parkinson's patients, non-linear dynamics of the respiratory system, automatic identification of saccade-related visual evoked potentials, multiple electrode stimulators, algorithms to estimate the causal relationship between brain sources, diffusion tensor imaging in the brain and phase synchronization of Neural activity in vitro. This broad array of manuscripts focusing on Neural imaging, neurophysiology, Neural signal processing, neuroelectronics and neuro-dynamics can be found for the first time within the pages of a single journal: Journal of Neural Engineering. I am grateful to Institute of Physics Publishing and Jane Roscoe in particular for putting together this new journal to accommodate the fast-growing field of Neural Engineering. I am also grateful to Andrew Schwartz who has agreed to be the co-Editor-in-Chief for the journal.