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Reciprocal structure–function relationships underlie both healthy and pathological behaviours in complex neural networks. Thus, understanding neuropathology and network dysfunction requires a thorough investigation of the complex interactions between structural and functional network reconfigurations in response to perturbation. Such adaptations are often difficult to study in vivo. For example, subtle, evolving changes in synaptic connectivity, transmission and the electrophysiological shift from healthy to pathological states, for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheimer's, are difficult to study in the brain. Engineered in vitro neural networks are powerful models that enable selective targeting, manipulation and monitoring of dynamic neural network behaviour at the micro‐ and mesoscale in physiological and pathological conditions. In this study, we engineered feedforward cortical neural networks using two‐nodal microfluidic devices with controllable connectivity interfaced with microelectrode arrays (mMEAs). We induced P301L mutated tau protein to the presynaptic node of these networks and monitored network dynamics over three weeks. Induced perturbation resulted in altered structural organization and extensive axonal retraction starting in the perturbed node. Perturbed networks also exhibited functional changes in intranodal activity, which manifested as an overall decline in both firing rate and bursting activity, with a progressive increase in synchrony over time and a decrease in internodal signal propagation between pre‐ and post‐synaptic nodes. These results provide insights into dynamic structural and functional reconfigurations at the micro‐ and mesoscale as a result of evolving pathology and illustrate the utility of engineered networks as models of network function and dysfunction. [ABSTRACT FROM AUTHOR]

Abstract Electrical stimulation of spinal neurons has emerged as a valuable tool to enhance rehabilitation after spinal cord injury. In separate parameterizations, it has shown promise for improving voluntary movement, reducing symptoms of autonomic dysreflexia, improving functions mediated by muscles of the pelvic floor (e.g., bowel, bladder, and sexual function), reducing spasms and spasticity, and decreasing neuropathic pain, among others. This diverse set of actions is related both to the density of sensorimotor neural networks in the spinal cord and to the intrinsic ability of electrical stimulation to modulate neural transmission in multiple spinal networks simultaneously. It also suggests that certain spinal stimulation parameterizations may be capable of providing multi-modal therapeutic benefits, which would directly address the complex, multi-faceted rehabilitation goals of people living with spinal cord injury. This review is intended to identify and characterize reports of spinal stimulation-based therapies specifically designed to provide multi-modal benefits and those that report relevant unintended effects of spinal stimulation paradigms parameterized to enhance a single consequence of spinal cord injury.

Developing scalable electrical stimulating platforms for cell and tissue engineering applications is limited by external power source dependency, wetting resistance, microscale size requirements, and suitable flexibility. Here, a versatile and scalable platform is developed to enable tunable electrical stimulation for biological applications by harnessing the giant magnetoelastic effect in soft systems, converting gentle air pressure (100-400 kPa) to yield a current of up to 10.5 mA and a voltage of 9.5 mV. The platform can be easily manufactured and scaled up for integration in multiwell magnetoelastic plates via 3D printing. The authors demonstrate that the electrical stimulation generated by this platform enhances the conversion of fibroblasts into neurons up to 2-fold (104%) and subsequent neuronal maturation up to 3-fold (251%). This easily configurable electrical stimulation device has broad applications in high throughput organ-on-a-chip systems, and paves the way for future development of neural engineering, including cellular therapy via implantable self-powered electrical stimulation devices.

Electrical stimulation of spinal neurons has emerged as a valuable tool to enhance rehabilitation after spinal cord injury. In separate parameterizations, it has shown promise for improving voluntary movement, reducing symptoms of autonomic dysreflexia, improving functions mediated by muscles of the pelvic floor (e.g., bowel, bladder, and sexual function), reducing spasms and spasticity, and decreasing neuropathic pain, among others. This diverse set of actions is related both to the density of sensorimotor neural networks in the spinal cord and to the intrinsic ability of electrical stimulation to modulate neural transmission in multiple spinal networks simultaneously. It also suggests that certain spinal stimulation parameterizations may be capable of providing multi-modal therapeutic benefits, which would directly address the complex, multi-faceted rehabilitation goals of people living with spinal cord injury. This review is intended to identify and characterize reports of spinal stimulation-based therapies specifically designed to provide multi-modal benefits and those that report relevant unintended effects of spinal stimulation paradigms parameterized to enhance a single consequence of spinal cord injury. [ABSTRACT FROM AUTHOR]

Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or monitor conditions by recording from the peripheral nerves. The recent growth of Machine Learning (ML) has led to the application of a wide variety of ML techniques to PNIs, especially in circumstances where the goal is classification or regression. However, the extent to which ML has been applied to PNIs or the range of suitable ML techniques has not been documented. Therefore, a scoping review was conducted to determine and understand the state of ML in the PNI field. The review searched five databases and included 63 studies after full-text review. Most studies incorporated a supervised learning approach to classify activity, with the most common algorithms being some form of neural network (artificial neural network, convolutional neural network or recurrent neural network). Unsupervised, semi-supervised and reinforcement learning (RL) approaches are currently underutilized and could be better leveraged to improve performance in this domain.

Electrical stimulation has shown promise in clinical studies to treat nerve injuries. This work is aimed to create an aligned bioelectronic construct that can be used to bridge a nerve gap, directly interfacing with the damaged nerve tissue to provide growth support. The conductive three‐dimensional bioelectronic scaffolds described herein are composite materials, comprised of conductive polypyrrole (PPy) nanoparticles embedded in an aligned collagen hydrogel. The bioelectronic constructs are seeded with dorsal root ganglion derived primary rat neurons and electrically stimulated in vitro. The PPy loaded constructs support a 1.7‐fold increase in neurite length in comparison to control collagen constructs. Furthermore, upon electrical stimulation of the PPy‐collagen construct, a 1.8‐fold increase in neurite length is shown. This work illustrates the potential of bioelectronic constructs in neural tissue engineering and lays the groundwork for the development of novel bioelectronic materials for neural interfacing applications. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until reaching the output layer. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies its impact. The FCP algorithm is executed on a post-hoc basis, i.e., once the learning process is completed. Aiming to illustrate the FCP algorithm, this paper develops a case study concerning bias detection in a fairness problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features. The source code and supplementary material for this paper are available at https://github.com/igraugar/fcp. [ABSTRACT FROM AUTHOR]

Conductive biomaterials provide an important control for engineering neural tissues, where electrical stimulation can potentially direct neural stem/progenitor cell (NS/PC) maturation into functional neuronal networks. It is anticipated that stem cell-based therapies to repair damaged central nervous system (CNS) tissues and ex vivo, "tissue chip" models of the CNS and its pathologies will each benefit from the development of biocompatible, biodegradable, and conductive biomaterials. Here, technological advances in conductive biomaterials are reviewed over the past two decades that may facilitate the development of engineered tissues with integrated physiological and electrical functionalities. First, one briefly introduces NS/PCs of the CNS. Then, the significance of incorporating microenvironmental cues, to which NS/PCs are naturally programmed to respond, into biomaterial scaffolds is discussed with a focus on electrical cues. Next, practical design considerations for conductive biomaterials are discussed followed by a review of studies evaluating how conductive biomaterials can be engineered to control NS/PC behavior by mimicking specific functionalities in the CNS microenvironment. Finally, steps researchers can take to move NS/PC-interfacing, conductive materials closer to clinical translation are discussed.

Abstract Recently, the 3D printing of conductive hydrogels has undergone remarkable advances in the fabrication of complex and functional structures. In the field of neural engineering, an increasing number of reports have been published on tissue engineering and bioelectronic approaches over the last few years. The convergence of 3D printing methods and electrically conducting hydrogels may create new clinical and therapeutic possibilities for precision regenerative medicine and implants. In this review, we summarize (i) advancements in preparation strategies for conductive materials, (ii) various printing techniques enabling the fabrication of electroconductive hydrogels, (iii) the required physicochemical properties of the printed constructs, (iv) their applications in bioelectronics and tissue regeneration for neural engineering, and (v) unconventional approaches and outlooks for the 3D printing of conductive hydrogels. This review provides technical insights into 3D printable conductive hydrogels and encompasses recent developments, specifically over the last few years of research in the neural engineering field.

Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a brain-computer interface (BCI) system aimed at restoring function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5–11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (<12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals and assess its usefulness for specific applications. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is a neural computing paradigm especially well-suited for learning dynamical systems by leveraging an untrained reservoir layer, providing high-dimensional input encoding with fading memory property. Since only the readout weights are trained under RC, linear regression learning algorithms are sufficient, leading to significant improvements in computational complexity and energy efficiency as compared to other deep neural networks (DNNs). RC offers an alternative solution to sidestep the shortcomings of data scarcity and the vanishing gradient problem. More importantly, such a network structure is amenable to hardware implementation using a variety of devices, circuits, and systems, making RC a good candidate to replace sophisticated DNNs as a lightweight classifier at the edge for internet of things (IoT) applications. In this article, we provide an overview of recent advances in RC hardware and their applications for mobile edge intelligence. Specifically, we will demonstrate the design strategies of RC in opto-electronic configuration, fully digital system, and silicon with the mixed-signal integrated circuit approach. Moreover, we will expose a novel implementation approach using emerging materials, designing the way for RC to be used in the next-generation neuromorphic computing systems. Building upon these efficient RC models, their applicability and effectiveness against the state-of-the-art are then demonstrated through diverse machine learning benchmarks spanning the area of IoT, communication networks, and healthcare. [ABSTRACT FROM AUTHOR]

ObjectiveIndividuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI.MethodsBased on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control.ResultsCommunication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home.SignificanceWireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.

Recently, the 3D printing of conductive hydrogels has undergone remarkable advances in the fabrication of complex and functional structures. In the field of neural engineering, an increasing number of reports have been published on tissue engineering and bioelectronic approaches over the last few years. The convergence of 3D printing methods and electrically conducting hydrogels may create new clinical and therapeutic possibilities for precision regenerative medicine and implants. In this review, we summarize (i) advancements in preparation strategies for conductive materials, (ii) various printing techniques enabling the fabrication of electroconductive hydrogels, (iii) the required physicochemical properties of the printed constructs, (iv) their applications in bioelectronics and tissue regeneration for neural engineering, and (v) unconventional approaches and outlooks for the 3D printing of conductive hydrogels. This review provides technical insights into 3D printable conductive hydrogels and encompasses recent developments, specifically over the last few years of research in the neural engineering field. [ABSTRACT FROM AUTHOR]

ObjectiveRecent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits.ApproachThis progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate.Main resultsDespite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products.SignificanceThis article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.

Neural engineering is an emerging and multidisciplinary field in which engineering approaches are applied to neuroscience problems. Women are underrepresented in engineering fields, and indeed in science, technology, engineering, and mathematics (STEM) fields generally. Underrepresentation of women is particularly notable at later academic career stages, suggesting that even though women are interested in the field, barriers exist that ultimately cause them to leave. Here, we investigate many of the obstacles to women's success in the field of neural engineering and provide recommendations and materials to overcome them. We conducted a review of the literature from the past 15 years regarding the experiences of women in academic careers, as well as reports on the number of women in fields closely related to neural engineering from the National Science Foundation (NSF) and the American Society for Engineering Education (ASEE). Additionally, we interviewed six women in neural engineering who are involved in initiatives and outreach concerning the inclusion and experiences of women in engineering. Throughout the literature and interviews, we identified common themes spanning the role of identity and confidence, professional relationships, career-related hurdles, and personal and professional expectations. We explore each of these themes in detail and provide resources to support the growth of women as they climb within the field of neural engineering. [ABSTRACT FROM AUTHOR]

The field of tissue engineering has benefited greatly from the broad development of natural and synthetic polymers. Extensive work in neural engineering has demonstrated the value of conductive materials to improve spontaneous neuron activity as well as lowering the necessary field parameters for exogenous electrical stimulation. Further, cell fate is directly coupled to the mechanical properties of the cell culture substrate. Increasing the conductivity of hydrogel materials often necessitates the addition of dopant materials that facilitate electron mobility. However, very little electron transfer is observed in native cell signaling and most of these materials are opaque, severely limiting microscopy applications commonly employed to assess cell culture morphology and function. To overcome these shortcomings, the inclusion of an ionic liquid, choline acrylate, into the backbone of a modified collagen polymer increases the bulk conductivity 5-fold at a 1:1 ratio while maintaining optical transmission of visible light. Here, we explore how the inclusion of choline acrylate influences bulk material properties including the mechanical, swelling, and optical properties of our hydrogels, referred to as Gel-Amin hydrogels, as a material for tissue culture. Despite an increase in swelling over traditional GelMA materials, the conductive hydrogels support whole dorsal root ganglia encapsulation and outgrowth. Our results indicate that our Gel-Amin system holds potential for neural engineering applications and lowering the required charge injection for the application of exogenous electrical stimulation. This is this first time an ionic liquid-hydrogel system has been used to culture and support primary neurons in vitro. [ABSTRACT FROM AUTHOR]

Neural tissue is an electrical responsible organ. The electricity plays a vital role in the growth and development of nerve tissue, as well as the repairing after diseases. The interface between the nervous system and external device for information transmission is called neural electroactive interface. With the development of new materials and fabrication technologies, more and more new types of neural interfaces are developed and the interfaces can play crucial roles in treating many debilitating diseases such as paralysis, blindness, deafness, epilepsy, and Parkinson's disease. Neural interfaces are developing toward flexibility, miniaturization, biocompatibility, and multifunctionality. This review presents the development of neural electrodes in terms of different materials for constructing electroactive neural interfaces, especially focus on the piezoelectric materials‐based indirect neuromodulation due to their features of wireless control, excellent effect, and good biocompatibility. We discussed the challenges we need to consider before the application of these new interfaces in clinical practice. The perspectives about future directions for developing more practical electroactive interface in neural engineering are also discussed in this review. This article is categorized under:Implantable Materials and Surgical Technologies > Nanomaterials and ImplantsImplantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement [ABSTRACT FROM AUTHOR]

Glassy carbon, in general, is made by the pyrolysis of polymeric materials and has been the subject of research for at least fifty years. However, as understanding its microstructure is far from straightforward, it continues to be an area of active research. Glassy carbon adopts different allotropes depending on the hybridizations of the C–C bond, that is, sp, sp2, or sp3. Furthermore, a variety of short-range ordering effects can interact with each other and this, along with the effects of microporosity, grain boundaries, and defects, render this a fascinating material. Following the nanoarchitectonics concept of bottom-up creation of functional materials, we use methane rather than a polymer to form glassy carbon. Here we show that tubular glassy carbon microneedles with fullerene-like tips form when methane undergoes pyrolysis on a curved alumina surface. X-ray diffraction of these glassy carbon tubules shows long-range order with a d-spacing of 4.89 Å, which is indicative of glassy carbon. Raman spectroscopy shows the material to be graphitic in nature, and SEM shows the fullerene-like structure of the material. This work provides new insights into the structure of glassy carbons relevant to the application of glassy carbons as a biomaterial, for example, as a new form of carbon-based microneedles. Since metallic needles can introduce toxic/allergenic species into susceptible subjects, this alternative carbon-based microneedle form has great potential as a replacement biomedical material for metallic needles in the field of neural engineering and as acupuncture needles.

Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a brain-computer interface (BCI) system aimed at restoring function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5–11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (

Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis. Several technological challenges require to be faced, as the development of real-time spiking neural network emulators which could bear the increasing amount of data provided by new generation High-Density Multielectrode Arrays. This work focuses on the development of a real-time spiking neural network emulator addressing fully-connected neural networks. This work presents a new way to increase the number of synapses supported by real-time neural network accelerators. The proposed solution has been implemented on the Xilinx Zynq 7020 All-Programmable SoC and can emulate fully connected spiking neural networks counting up to 3,098 Izhikevich neurons and 9.6e6 synapses in real-time, with a resolution of 0.1 ms.

Neural engineering is an emerging and multidisciplinary field in which engineering approaches are applied to neuroscience problems. Women are underrepresented in engineering fields, and indeed in science, technology, engineering, and mathematics (STEM) fields generally. Underrepresentation of women is particularly notable at later academic career stages, suggesting that even though women are interested in the field, barriers exist that ultimately cause them to leave. Here, we investigate many of the obstacles to women’s success in the field of neural engineering and provide recommendations and materials to overcome them. We conducted a review of the literature from the past 15 years regarding the experiences of women in academic careers, as well as reports on the number of women in fields closely related to neural engineering from the National Science Foundation (NSF) and the American Society for Engineering Education (ASEE). Additionally, we interviewed six women in neural engineering who are involved in initiatives and outreach concerning the inclusion and experiences of women in engineering. Throughout the literature and interviews, we identified common themes spanning the role of identity and confidence, professional relationships, career-related hurdles, and personal and professional expectations. We explore each of these themes in detail and provide resources to support the growth of women as they climb within the field of neural engineering.

Controlling nerve cells to form pre‐designed 3D neural networks that recapitulate the intricate neural interconnectivity in the brain is essential for developing neuronal interfaces and new regeneration approaches. Here, nerve cells within 3D biomaterials are dynamically localized using nano‐based magnetic manipulations. Nerve cells are transformed into magnetic units and their organizational layout is manipulated using external magnetic field gradients. Iron oxide nanoparticles are incorporated into both Pheochromocytoma cell‐line 12 (PC12) cells and primary mice cortical neurons and the magnetized cells are subjected to multiple magnetic fields using pre‐designed magnetic arrays. Their movement is controlled inside multi‐layered 3D collagen scaffolds, which simulate the innate properties of in‐vivo tissue structures. Via these magnetic manipulations, functional 3D microarchitectures of neural networks are created. In light of the clustered and layered structure of the mammalian central nervous system, this strategy paves the way to creating customized 3D tissue architectures for bioengineering applications, enabling a broad range of advanced implementations and providing efficient models for investigating cellular and tissue behavior. [ABSTRACT FROM AUTHOR]

STEM integration has become a national and international priority, but our understanding of student learning experiences in integrated STEM courses, especially those that integrate life sciences and engineering design, is limited. Our team has designed a new high school curriculum unit that focuses on neural engineering, an emerging interdisciplinary field that brings together neuroscience, technology, and engineering. Through the implementation of the unit in a high school engineering design course, we asked how incorporating life sciences into an engineering course supported student learning and what challenges were experienced by the students and their teacher. To address these questions, we conducted an exploratory case study consisting of a student focus group, an interview with the teacher, and analysis of student journals. Our analysis suggests that students were highly engaged by the authentic and collaborative engineering design process, helping solidify their self-efficacy and interest in engineering design. We also identified some challenges, such as students' lower interest in life sciences compared to engineering design and the teacher lacking a life sciences background. These preliminary findings suggest that neural engineering can provide an effective context to the integration of life sciences and engineering design but more scaffolding and teacher support is needed for full integration. [ABSTRACT FROM AUTHOR]

Computational models of electrical stimulation, block and recording of autonomic nerves enable analysis of mechanisms of action underlying neural responses and design of optimized stimulation parameters. We reviewed advances in computational modeling of autonomic nerve stimulation, block, and recording over the past five years, with a focus on vagus nerve stimulation, including both implanted and less invasive approaches. Few models achieved quantitative validation, but integrated computational pipelines increase the reproducibility, reusability, and accessibility of computational modeling. Model-based optimization enabled design of electrode geometries and stimulation parameters for selective activation (across fiber locations or types). Growing efforts link models of neural activity to downstream physiological responses to represent more directly the therapeutic effects and side effects of stimulation. Thus, computational modeling is an increasingly important tool for analysis and design of bioelectronic therapies., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

They summarize the state-of-the-art requirements for brain-implanted devices to realize the wireless interface and address the operating principles and applications of wireless interfaces based on modalities. Keywords: neurotechnology; biomedical engineering; neural engineering EN neurotechnology biomedical engineering neural engineering 1 4 4 06/23/22 20220725 NES 220725 In the nervous system, huge amounts of neurons constantly induce and transmit electrophysiological signals to communicate between neurons and brain regions. In a thought-provoking article by Luca Selmi and colleagues [2], after discussing recent progress in electronic sensor architecture for I in vitro i neural recording, authors modelled the neuron/sensor interface and readout electronics with a mixed-mode finite-element/circuit approach. [Extracted from the article]

Here I identify two gaps in cochlear implants that have been limiting their performance and acceptance. First, cochlear implant performance has remained largely unchanged, despite the number of publications tripling per decade in the last 30 years. Little has been done so far to address a fundamental limitation in the electrode-to-neuron interface, with the electrode size being a thousand times larger than the neuron diameter while the number of electrodes being a thousand times less. Both the small number and the large size of electrodes produce broad spatial activation and poor frequency resolution that limit current cochlear implant performance. Second, a similarly rapid growth in cochlear implant volume has not produced an expected decrease in unit price in the same period. The high cost contributes to low market penetration rate, which is about 20% in developed countries and less than 1% in developing countries. I will discuss changes needed in both research strategy and business practice to close the gap between prosthetic and normal hearing as well as that between haves and have-nots.

This work presents a single-chip battery-less neural recorder with 12 on-die microelectrodes. It can be powered wirelessly up to 16 cm away from a horn antenna at 915 MHz and only consumes 104 $\mu \text{W}$ dc power for accessing ten enabled recording sites simultaneously, transmitting at 5 Mb/s. The implantable device integrated with a flexible antenna weighs only 0.43 g. In vivo measurements on an unrestricted mouse have been successfully conducted, showing response to visual stimuli. [ABSTRACT FROM AUTHOR]

This Discussion Note calls for attention to the cultural practices of Neural Engineering as part of the life sciences as practices and technologies of manufacturing life. Through focusing on Disability, Ableism, and especially Technoableism within the field, I point out instances of onto-epistemological violence, which influence the likelihood of survival of disabled people individually and as a (cultural and political) group. By drawing on Crip Technoscience, a method assemblage is introduced that allows to address these issues in an intersectional-kyriarchal understanding of interlocking systems of privilege and oppression through generative critique and productive collaborative work. Criptical Neural Engineering is dedicated to disability justice and disability gain. It centers disabled people as epistemic activists and demands response-ability and accountability from non-disabled people, specifically engineers that want to build adaptive technologies with disabled people. [ABSTRACT FROM AUTHOR]

The techniques for noninvasive measurement of brain function such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) have been used in diagnosing brain conditions. However, the conventional techniques have critical limitations of spatial or temporal resolution. Here, we developed a novel technique which enables the precise measurement of dynamic brain signals and localized identification of active brain regions. In this technique, termed as magnetically biased field (MBF), human brain signal is measured as the fluctuation of a transcranial static magnetic field emitted by a coil placed on the scalp. The validity of MBF was confirmed by the measurement of somatosensory evoked signals. Fast somatosensory evoked signals were successfully observed. Localized maximum positive and negative deflections appeared at the region which represents the right primary somatosensory area contralateral to the stimulated hand. The ability of MBF to detect dynamic brain activity precisely can have numerous applications such as diagnosing brain diseases and brain-machine interfaces.

In this work we propose a new biophysical computational model of brain regions relevant to Parkinson’s Disease (PD) based on local field potential data collected from the brain of marmoset monkeys. PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex (BG-T-C) neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled spectral signatures of local field potentials and single-neuron mean firing rates from healthy and parkinsonian marmoset brain data. This is the first computational model of PD based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results indicate that the proposed model may facilitate the investigation of the mechanisms of PD and eventually support the development of new therapies. The DE method could also be applied to other computational neuroscience problems in which biological data is used to fit multi-scale models of brain circuits.

Developing novel neuroprobes that enable parallel multisite, long-term intracellular recording and stimulation of neurons in freely behaving animals is a neuroscientist's dream. When fulfilled, it is expected to significantly enhance brain research at fundamental mechanistic levels including that of subthreshold signaling and computations. Here we assess the feasibility of merging the advantages of in vitro vertical nanopillar technologies that support intracellular recordings with contemporary concepts of in vivo extracellular field potential recordings to generate the dream neuroprobes that read the entire electrophysiological signaling repertoire. [ABSTRACT FROM AUTHOR]

Multi-Electrode Arrays and High-Density Multi-Electrode Arrays of sensors are a key instrument in neuroscience research. Such devices are evolving to provide ever-increasing temporal and spatial resolution, paving the way to unprecedented results when it comes to understanding the behaviour of neuronal networks and interacting with them. However, in some experimental cases, in-place low-latency processing of the sensor data acquired by the arrays is required. This poses the need for high-performance embedded computing platforms capable of processing in real-time the stream of samples produced by the acquisition front-end to extract higher-level information. Previous work has demonstrated that Field-Programmable Gate Array and All-Programmable System-On-Chip devices are suitable target technology for the implementation of real-time processors of High-Density Multi-Electrode Arrays data. However, approaches available in literature can process a limited number of channels or are designed to execute only the first steps of the neural signal processing chain. In this work, we propose an All-Programmable System-On-Chip based implementation capable of sorting neural spikes acquired by the sensors, to associate the shape of each spike to a specific firing neuron. Our system, implemented on a Xilinx Z7020 All-Programmable System-On-Chip is capable of executing on-line spike sorting up to 5500 acquisition channels, 43x more than state-of-the-art alternatives, supporting 18KHz acquisition frequency. We present an experimental study on a commonly used reference dataset, using on-line refinement of the sorting clusters to improve accuracy up to 82%, with only 4% degradation with respect to off-line analysis.

Developing novel neuroprobes that enable parallel multisite, long-term intracellular recording and stimulation of neurons in freely behaving animals is a neuroscientist’s dream. When fulfilled, it is expected to significantly enhance brain research at fundamental mechanistic levels including that of subthreshold signaling and computations. Here we assess the feasibility of merging the advantages of in vitro vertical nanopillar technologies that support intracellular recordings with contemporary concepts of in vivo extracellular field potential recordings to generate the dream neuroprobes that read the entire electrophysiological signaling repertoire.

Neural engineering, an emerging interdisciplinary subject, is aimed at using engineering techniques to investigate the function and manipulate the behavior of the nervous system. The development of technology along with the advancement in Science helps to apply increasing multimodal research into the field of neural engineering, which has promoted the development of neural engineering. In this paper, a bibliometric analysis of 808 articles in Web of Science from 2003 to 2019 was conducted to determine the current status and future trends of multimodal neural engineering study. This paper conducted a five-step bibliometric analysis based on the proposed multimodal neural engineering research framework (NE-MUL). The results showed that in the past 17 years, multimodal research not only made great contributions to the development of neural engineering, but also brought this field a series of new problems (multimodal fusion, recurrent multimodal learning, multimodal convolutional network, etc.) This paper generated a map of existing research findings with their relationship and provided future researchers with meaningful suggestions and assistance. [ABSTRACT FROM AUTHOR]

We present a computational model performing the n-back task.This task requires a number of cognitive processes includingrapid binding, updating, and retrieval of items in workingmemory. The model is implemented in spiking leakyintegrate-and-fire neurons with physiologically constrained parameters,and anatomically constrained organization. Themethods of the Semantic Pointer Architecture (SPA) are usedto construct the model. Accuracies and reaction times producedby the model are shown to match human data. Namely,characteristic decline in accuracy and response speed with increaseof n is reproduced. Furthermore, the model providesevidence, contrary to some past proposals, that an active removalprocess of items in working memory is not necessaryfor an accurate performance on the n-back task

STEM integration has become a national and international priority, but our understanding of student learning experiences in integrated STEM courses, especially those that integrate life sciences and engineering design, is limited. Our team has designed a new high school curriculum unit that focuses on neural engineering, an emerging interdisciplinary field that brings together neuroscience, technology, and engineering. Through the implementation of the unit in a high school engineering design course, we asked how incorporating life sciences into an engineering course supported student learning and what challenges were experienced by the students and their teacher. To address these questions, we conducted an exploratory case study consisting of a student focus group, an interview with the teacher, and analysis of student journals. Our analysis suggests that students were highly engaged by the authentic and collaborative engineering design process, helping solidify their self-efficacy and interest in engineering design. We also identified some challenges, such as students’ lower interest in life sciences compared to engineering design and the teacher lacking a life sciences background. These preliminary findings suggest that neural engineering can provide an effective context to the integration of life sciences and engineering design but more scaffolding and teacher support is needed for full integration.

How do we begin to consider ways to broaden the reach of our field, both to engage younger students and also those from different ethnicities, classes, and socio-economic backgrounds? Where are the opportunities to make our research, our laboratory, our surrounding community more open, inclusive, and diverse? By what means can we increase outreach in our communities, providing avenues for diverse students to explore STEM topics and engage in research? [ABSTRACT FROM AUTHOR]