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The aim of this study is the fabrication of unprecedented neuroelectrodes, replete with exceptional biological and electrical attributes. Commencing with the synthesis of polyethylene glycol and polyethyleneimine-modified iron oxide nanoparticles, the grafting of Dimyristoyl phosphatidylcholine was embarked upon to generate DMPC-SPION nanoparticles. Subsequently, the deposition of DMPC-SPIONs onto a nickel–chromium alloy electrode facilitated the inception of an innovative neuroelectrode–DMPC-SPION. A meticulous characterization of DMPC-SPIONs ensued, encompassing zeta potential, infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction analyses. Evaluations pertaining to hemolysis and cytotoxicity were conducted to ascertain the biocompatibility and biosafety of DMPC-SPIONs. Ultimately, a comprehensive assessment of the biocompatibility, electrochemical properties, and electrophysiological signal acquisition capabilities of DMPC-SPION neuroelectrodes was undertaken. These findings conclusively affirm the exemplary biocompatibility, electrochemical capabilities, and outstanding capability in recording electrical signals of DMPC-SPION neuroelectrodes, with an astounding 91.4% augmentation in electrode charge and a noteworthy 13% decline in impedance, with peak potentials reaching as high as 171 μV and an impressive signal-to-noise ratio of 15.92. Intriguingly, the novel DMPC-SPION neuroelectrodes herald an innovative pathway towards injury repair as well as the diagnosis and treatment of neurological disorders. [ABSTRACT FROM AUTHOR]

In clinical applications and studies neural nerve impulse recordings are extensively used. The spike detection method is critical for determining when a spike is activated. The minimal ratio of signal to noise for multi-electrode cuff recordings is generally less than 10. For applications involving implantable neural recording, this work provides a brandnew, incredibly hardware-efficient (low complexity, low computation), adaptive spike detection algorithm. A mean reduction filter is used in this to first remove any lowfrequency elements from the data without adding more phase distortion. A new operator called an Amplitude Slope Operator (ASO) is also added as a hardware-effective substitute for NEO for increasing the SNR of the data. The adjustable threshold is computed on a periodic basis by subtracting any detected spikes from the running mean while simultaneously running a subthreshold detection to remove some of the undetected spikes from the background activity. The method was first created in MATLAB employing floating-point math, and it has since been converted to work with fixed-point math. The Least mean squares (LMS) adaptive filter is proposed and implemented in this research using MATLAB and the Xilinx Spartans 3E-100 (xc3s100e) hardware and software tools. The proposed filter improves noise rejection while also reducing power consumption and hardware footprint. Furthermore, the results show that its LMS adaptive filter works effectively in online neural recording, with significant improvements in the signal-to-noise ratio (SNR). As a result, this enhancement may result in greater spike detection accuracy. In records with SNR = 5, it can achieve a sensitive of >80% with such a false-positive rate of 6 Hz, and it improves performance than an optimum threshold detector employing a band - pass filter in records with SNR > 3. In comparison to the traditional technique of digital filter algorithm, the current filter design delivers a significant decrease of 10% in power usage and 25% in hardware requirements. As a result, this research might point to a critical advance in brain recording to be used as a control input for prostheses devices. [ABSTRACT FROM AUTHOR]

Stellate ganglia within the intrathoracic cardiac control system receive and integrate central, peripheral, and cardiopulmonary information to produce postganglionic cardiac sympathetic inputs. Pathological anatomical and structural remodeling occurs within the neurons of the stellate ganglion (SG) in the setting of heart failure (HF). A large proportion of SG neurons function as interneurons whose networking capabilities are largely unknown. Current therapies are limited to targeting sympathetic activity at the cardiac level or surgical interventions such as stellectomy, to treat HF. Future therapies that target the SG will require understanding of their networking capabilities to modify any pathological remodeling. We observe SG networking by examining cofluctuation and specificity of SG networked activity to cardiac cycle phases. We investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced HF and control subjects during extended in-vivo extracellular microelectrode recordings. We find that information processing and cardiac control in chronic HF by the SG, relative to controls, exhibits: (i) more frequent, short-lived, high magnitude cofluctuations, (ii) greater variation in neural specificity to cardiac cycles, and (iii) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude.

The landscape of human memory and event cognition research has witnessed a transformative journey toward the use of naturalistic contexts and tasks. In this review, we track this progression from abrupt, artificial stimuli used in extensively controlled laboratory experiments to more naturalistic tasks and stimuli that present a more faithful representation of the real world. We argue that in order to improve ecological validity, naturalistic study designs must consider the complexity of the cognitive phenomenon being studied. Then, we review the current state of "naturalistic" event segmentation studies and critically assess frequently employed movie stimuli. We evaluate recently developed tools like lifelogging and other extended reality technologies to help address the challenges we identified with existing naturalistic approaches. We conclude by offering some guidelines that can be used to design ecologically valid cognitive neuroscience studies of memory and event cognition. [ABSTRACT FROM AUTHOR]

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity, or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a non-linear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the TensorFlow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity , or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a non-linear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the TensorFlow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity. [ABSTRACT FROM AUTHOR]

Cardiopulmonary sympathetic control is exerted via stellate ganglia (SG); however, little is known about how neuronal firing patterns in the stellate ganglion relate to dynamic physiological function in the heart and lungs. We performed continuous extracellular recordings from SG neurons using multielectrode arrays in chloralose-anesthetized pigs (n = 6) for 8-9 h. Respiratory and left ventricular pressures (RP and LVP, respectively) and the electrocardiogram (ECG) were recorded concomitantly. Linkages between sampled spikes and LVP or RP were determined using a novel metric to evaluate specificity in neural activity for phases of the cardiac and pulmonary cycles during resting conditions and under various cardiopulmonary stressors. Firing frequency (mean 4.6 ± 1.2 Hz) varied spatially across the stellate ganglion, suggesting regional processing. The firing pattern of most neurons was synchronized with both cardiac (LVP) and pulmonary (RP) activity indicative of cardiopulmonary integration. Using the novel metric to determine cardiac phase specificity of neuronal activity, we found that spike density was highest during diastole and near-peak systole. This specificity was independent of the actual LVP or population firing frequency as revealed by perturbations to the LVP. The observed specificity was weaker for RP. Stellate ganglion neuronal populations exhibit cardiopulmonary integration and profound specificity toward the near-peak systolic phase of the cardiac cycle. This novel approach provides practically deployable tools to probe stellate ganglion function and its relationship to cardiopulmonary pathophysiology. [ABSTRACT FROM AUTHOR]

During normal viewing, we direct our eyes between objects in three-dimensional (3D) space many times a minute. To accurately fixate these objects, which are usually located in different directions and at different distances, we must generate eye movements with appropriate versional and vergence components. These combined saccade-vergence eye movements result in disjunctive saccades with a vergence component that is much faster than that generated during smooth, symmetric vergence eye movements. The neural control of disjunctive saccades is still poorly understood. Recent anatomical studies suggested that the central mesencephalic reticular formation (cMRF), located lateral to the oculomotor nucleus, contains premotor neurons potentially involved in the neural control of these eye movements. We have therefore investigated the role of the cMRF in the control of disjunctive saccades in trained rhesus monkeys. Here, we describe a unique population of cMRF neurons that, during disjunctive saccades, display a burst of spikes that are highly correlated with vergence velocity. Importantly, these neurons show no increase in activity for either conjugate saccades or symmetric vergence. These neurons are termed saccade-vergence burst neurons (SVBNs) to maintain consistency with modeling studies that proposed that such a class of neuron exists to generate the enhanced vergence velocities observed during disjunctive saccades. Our results demonstrate the existence and characteristics of SVBNs whose activity is correlated solely with the vergence component of disjunctive saccades and, based on modeling studies, are critically involved in the generation of the disjunctive saccades required to view objects in our 3D world. [ABSTRACT FROM AUTHOR]

A dual-mode, reconfigurable ADC for neural recording application is presented in this paper. In order to quantize the extracellular action potential (EAP or spike) of neural signal, the proposed ADC works as a 10 bits successive approximation register (SAR) ADC in the normal mode to process all the raw data. In other side, to reduce dynamic power, the ADC is designed to configure as a single-slope (SS) ADC in compression mode, and only essential parts of spike waveforms are processed. Fabricated in a 0.18 µm CMOS process, the proposed ADC consumes 2.83 μW in SAR mode and 24.71 μW in SS mode at supply voltage of 1.8 V, respectively. In SAR mode, the achieved SNDR and SFDR are 60.5 dB and 75.5 dB when a Nyquist frequency input signal is received @ the sampling rate of 25 kS/s. While in SS mode, the achieved SNDR and SFDR are 57.7 dB and 60.8 dB. [ABSTRACT FROM AUTHOR]

This paper presents a fully self-biased, low-power LNA for neural recordings. Due to the capacitor coupling and low-supply voltage in LNA, the appropriate dc bias voltages for saturating the input transistors NMOS and PMOS of LNA should be separately provided. This paper focuses on the effects of different feedback ways to obtain input dc bias voltage, and proposes a completely self-biased structure to obtain the bias voltage directly from the inner nodes of the circuit. This connected way avoids using extra dc biasing circuit totally, saves capacitor area effectively and reduces the high-pass corner frequency greatly. Furthermore, the proposed method eliminates the likelihood for initial DC latch-up in the traditional way, making the circuit more stable. Simulated in a 0.18-µm CMOS process, the LNA consumes 1.2 µA from a 0.6 V supply, and achieves an input referred noise of 4.98 µVrms (1-10 kHz), corresponding to a noise efficiency factor of 2.13. Simulated CMRR and THD exceed 77 dB and 75 dB, separately. [ABSTRACT FROM AUTHOR]

Reducing the mechanical mismatch between the stiffness of a neural implant and the softness of the neural tissue is still an open challenge in neuroprosthetics. The emergence of conductive hydrogels in the last few years has considerably widened the spectrum of possibilities to tackle this issue. Nevertheless, despite the advancements in this field, further improvements in the fabrication of conductive hydrogel-based electrodes are still required. In this work, we report the fabrication of a conductive hydrogel-based microelectrode array for neural recording using a hybrid material composed of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate), and alginate. The mechanical properties of the conductive hydrogel have been investigated using imaging techniques, while the electrode arrays have been electrochemically characterized at each fabrication step, and successfully validated both in vitro and in vivo. The presence of the conductive hydrogel, selectively electrodeposited onto the platinum microelectrodes, allowed achieving superior electrochemical characteristics, leading to a lower electrical noise during recordings. These findings represent an advancement in the design of soft conductive electrodes for neuroprosthetic applications.

Reducing the mechanical mismatch between the stiffness of a neural implant and the softness of the neural tissue is still an open challenge in neuroprosthetics. The emergence of conductive hydrogels in the last few years has considerably widened the spectrum of possibilities to tackle this issue. Nevertheless, despite the advancements in this field, further improvements in the fabrication of conductive hydrogel-based electrodes are still required. In this work, we report the fabrication of a conductive hydrogel-based microelectrode array for neural recording using a hybrid material composed of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate), and alginate. The mechanical properties of the conductive hydrogel have been investigated using imaging techniques, while the electrode arrays have been electrochemically characterized at each fabrication step, and successfully validated both in vitro and in vivo. The presence of the conductive hydrogel, selectively electrodeposited onto the platinum microelectrodes, allowed achieving superior electrochemical characteristics, leading to a lower electrical noise during recordings. These findings represent an advancement in the design of soft conductive electrodes for neuroprosthetic applications. [ABSTRACT FROM AUTHOR]

Rodent models are decisive for translational research in healthy and pathological conditions of motor function thanks to specific similarities with humans. Here, we present an upgraded version of the M-Platform, a robotic device previously designed to train mice during forelimb retraction tasks. This new version significantly extends its possibilities for murine experiments during motor tasks: 1) an actuation system for friction adjustment allows to automatically adapt pulling difficulty; 2) the device can be used both for training, with a retraction task, and for assessment, with an isometric task; and 3) the platform can be integrated with a neurophysiology systems to record simultaneous cortical neural activity. Results of the validation experiments with healthy mice confirmed that the M-Platform permits precise adjustments of friction during the task, thus allowing to change its difficulty and that these variations induce a different improvement in motor performance, after specific training sessions. Moreover, simultaneous and high quality (high signal-to-noise ratio) neural signals can be recorded from the rostral forelimb area (RFA) during task execution. With the novel features presented herein, the M-Platform may allow to investigate the outcome of a customized motor rehabilitation protocol after neural injury, to analyze task-related signals from brain regions interested by neuroplastic events and to perform optogenetic silencing or stimulation during experiments in transgenic mice. [ABSTRACT FROM AUTHOR]

Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq—all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems. [ABSTRACT FROM AUTHOR]

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces. [ABSTRACT FROM PUBLISHER]

Electrophysiology is a decades-old technique widely used for monitoring activity of individual neurons and local field potentials. Optogenetics has revolutionized neuroscience studies by offering selective and fast control of targeted neurons and neuron populations. The combination of these two techniques is crucial for causal investigation of neural circuits and understanding their functional connectivity. However, electrical artifacts generated by light stimulation interfere with neural recordings and hinder the development of compact closed-loop systems for precise control of neural activity. Here, we demonstrate that transparent graphene micro-electrodes fabricated on a clear polyethylene terephthalate film eliminate the light-induced artifact problem and allow development of a compact battery-powered closed-loop optogenetics system. We extensively investigate light-induced artifacts for graphene electrodes in comparison to metal control electrodes. We then design optical stimulation module using micro-LED chips coupled to optical fibers to deliver light to intended depth for optogenetic stimulation. For artifact-free integration of graphene micro-electrode recordings with optogenetic stimulation, we design and develop a compact closed-loop system and validate it for different frequencies of interest for neural recordings. This compact closed-loop optogenetics systemcan be used for various applications involving optogenetic stimulation and electrophysiological recordings. [ABSTRACT FROM AUTHOR]

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

The long-term reliability of neural interfaces and stability of high-quality recordings are still unsolved issues in neuroscience research. High surface area PEDOT-PSS-CNT composites are able to greatly improve the performance of recording and stimulation for traditional intracortical metal microelectrodes by decreasing their impedance and increasing their charge transfer capability. This enhancement significantly reduces the size of the implantable device though preserving excellent electrical performances. On the other hand, the presence of nanomaterials often rises concerns regarding possible health hazards, especially when considering a clinical application of the devices. For this reason, we decided to explore the problem from a new perspective by designing and testing an innovative device based on nanostructured microspheres grown on a thin tether, integrating PEDOT-PSS-CNT nanocomposites with a soft synthetic permanent biocompatible hydrogel. The pHEMA hydrogel preserves the electrochemical performance and high quality recording ability of PEDOT-PSS-CNT coated devices, reduces the mechanical mismatch between soft brain tissue and stiff devices and also avoids direct contact between the neural tissue and the nanocomposite, by acting as a biocompatible protective barrier against potential nanomaterial detachment. Moreover, the spherical shape of the electrode together with the surface area increase provided by the nanocomposite deposited on it, maximize the electrical contact and may improve recording stability over time. These results have a good potential to contribute to fulfill the grand challenge of obtaining stable neural interfaces for long-term applications.

Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely reduced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principal component analysis. [ABSTRACT FROM PUBLISHER]

We investigated the potential of deep brain stimulation ( DBS) in the central nucleus of the amygdala (CeA) in rats to modulate functional reward mechanisms. The CeA is the major output of the amygdala with direct connections to the hypothalamus and gustatory brainstem, and indirect connections with the nucleus accumbens. Further, the CeA has been shown to be involved in learning, emotional integration, reward processing, and regulation of feeding. We hypothesized that DBS, which is used to treat movement disorders and other brain dysfunctions, might block reward motivation. In rats performing a lever-pressing task to obtain sugar pellet rewards, we stimulated the CeA and control structures, and compared stimulation parameters. During CeA stimulation, animals stopped working for rewards and rejected freely available rewards. Taste reactivity testing during DBS exposed aversive reactions to normally liked sucrose tastes and even more aversive taste reactions to normally disliked quinine tastes. Interestingly, given the opportunity, animals implanted in the CeA would self-stimulate with 500 ms trains of stimulation at the same frequency and current parameters as continuous stimulation that would stop reward acquisition. Neural recordings during DBS showed that CeA neurons were still active and uncovered inhibitory-excitatory patterns after each stimulus pulse indicating possible entrainment of the neural firing with DBS. In summary, DBS modulation of CeA may effectively usurp normal neural activity patterns to create an 'information lesion' that not only decreased motivational 'wanting' of food rewards, but also blocked 'liking' of rewards. [ABSTRACT FROM AUTHOR]

Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human–machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment. [ABSTRACT FROM AUTHOR]

Background Several species of the New World monkeys have been used as experimental models in biomedical and neurophysiological research. However, a method for controlled arm reaching tasks has not been developed for these species. New method We have developed a fully automated, pneumatically driven, portable, and reconfigurable experimental apparatus for arm-reaching tasks suitable for these small primates. Results We have utilized the apparatus to train two owl monkeys in a visually-cued arm-reaching task. Analysis of neural recordings demonstrates directional tuning of the M1 neurons. Comparison with existing method(s) Our apparatus allows automated control, freeing the experimenter from manual experiments. Conclusion The presented apparatus provides a valuable tool for conducting neurophysiological research on New World monkeys. [ABSTRACT FROM AUTHOR]

The long-term reliability of neural interfaces and stability of high-quality recordings are still unsolved issues in neuroscience research. High surface area PEDOT-PSS-CNT composites are able to greatly improve the performance of recording and stimulation for traditional intracortical metal microelectrodes by decreasing their impedance and increasing their charge transfer capability. This enhancement significantly reduces the size of the implantable device though preserving excellent electrical performances. On the other hand, the presence of nanomaterials often rises concerns regarding possible health hazards, especially when considering a clinical application of the devices. For this reason, we decided to explore the problem from a new perspective by designing and testing an innovative device based on nanostructured microspheres grown on a thin tether, integrating PEDOT-PSS-CNT nanocomposites with a soft synthetic permanent biocompatible hydrogel. The pHEMA hydrogel preserves the electrochemical performance and high quality recording ability of PEDOT-PSS-CNT coated devices, reduces the mechanical mismatch between soft brain tissue and stiff devices and also avoids direct contact between the neural tissue and the nanocomposite, by acting as a biocompatible protective barrier against potential nanomaterial detachment. Moreover, the spherical shape of the electrode together with the surface area increase provided by the nanocomposite deposited on it, maximize the electrical contact and may improve recording stability over time. These results have a good potential to contribute to fulfill the grand challenge of obtaining stable neural interfaces for long-term applications. [ABSTRACT FROM AUTHOR]

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

Despite significant advances in robotics, commercially advanced prosthetics provide only a small fraction of the functionality of the amputated limb that they are meant to replace. Peripheral nerve interfacing could provide a rich controlling link between the body and these advanced prosthetics in order to increase their overall utility. Here, we report on the development of a fully integrated regenerative microchannel interface with 30 microelectrodes and signal extraction capabilities enabling evaluation in an awake and ambulatory rat animal model. In vitro functional testing validated the capability of the microelectrodes to record neural signals similar in size and nature to those that occur in vivo. In vitro dorsal root ganglia cultures revealed striking cytocompatibility of the microchannel interface. Finally, in vivo, the microchannel interface was successfully used to record a multitude of single-unit action potentials through 63% of the integrated microelectrodes at the early time point of 3 weeks. This marks a significant advance in microchannel interfacing, demonstrating the capability of microchannels to be used for peripheral nerve interfacing. [ABSTRACT FROM AUTHOR]

Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45–54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 \mu V, and consumes 90 \mu W . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition. [ABSTRACT FROM PUBLISHER]

The distributed nature of nervous systems makes it necessary to record from a large number of sites in order to break the neural code, whether single cell, local field potential (LFP), micro-electrocorticograms (μECoG), electroencephalographic (EEG), magnetoencephalographic (MEG) or in vitro micro-electrode array (MEA) data are considered. High channel-count recordings also optimize the yield of a preparation and the efficiency of time invested by the researcher. Currently, data acquisition (DAQ) systems with high channel counts (>100) can be purchased from a limited number of companies at considerable prices. These systems are typically closed-source and thus prohibit custom extensions or improvements by end users.We have developed MANTA, an open-source MATLAB-based DAQ system, as an alternative to existing options. MANTA combines high channel counts (up to 1440 channels/PC), usage of analog or digital headstages, low per channel cost (1 year, recording reliably from 128 channels. It offers a growing list of features, including integrated spike sorting, PSTH and CSD display and fully customizable electrode array geometry (including 3D arrays), some of which are not available in commercial systems. MANTA runs on a typical PC and communicates via TCP/IP and can thus be easily integrated with existing stimulus generation/control systems in a lab at a fraction of the cost of commercial systems.With modern neuroscience developing rapidly, MANTA provides a flexible platform that can be rapidly adapted to the needs of new analyses and questions. Being open-source, the development of MANTA can outpace commercial solutions in functionality, while maintaining a low price-point.

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms. [ABSTRACT FROM AUTHOR]

Background: Cuff electrodes have been widely used chronically in different clinical applications. This neural interface has been dominantly used for nerve stimulation while interfering noise is the major issue when employed for recording purposes. Advancements have been made in rejecting extra-neural interference by using continuous ring contacts in tripolar topologies. Ring contacts provide an average of the neural activity, and thus reduce the information retrieved. Splitting these contacts into smaller recording areas could potentially increase the information content. In this study, we investigate the impact of such discretization on the Signal-to-Noise Ratio (SNR). The effect of contacts positioning and an additional short circuited pair of electrodes were also addressed. Methods: Different recording configurations using ring, dot, and a mixed of both contacts were studied in vitro in a frog model. An interfering signal was induced in the medium to simulate myoelectric noise. The experimental setup was design in such a way that the only difference between recordings was the configuration used. The inter-session experimental differences were taken care of by a common configuration that allowed normalization between electrode designs. Results: It was found that splitting all contacts into small recording areas had negative effects on noise rejection. However, if this is only applied to the central contact creating a mixed tripole configuration, a considerable and statistically significant improvement was observed. Moreover, the signal to noise ratio was equal or larger than what can be achieved with the best known configuration, namely the short circuited tripole. This suggests that for recording purposes, any tripole topology would benefit from splitting the central contact into one or more discrete contacts. Conclusions: Our results showed that a mixed tripole configuration performs better than the configuration including only ring contacts. Therefore, splitting the central ring contact of a cuff electrode into a number of dot contacts not only provides additional information but also an improved SNR. In addition, the effect of an additional pair of short circuited electrodes and the "end effect" observed with the presented method are in line with previous findings by other authors. [ABSTRACT FROM AUTHOR]

The distributed nature of nervous systems makes it necessary to record from a large number of sites in order to decipher the neural code, whether single cell, local field potential (LFP), micro-electrocorticograms (μECoG), electroencephalographic (EEG), magnetoencephalographic (MEG) or in vitro micro-electrode array (MEA) data are considered. High channel-count recordings also optimize the yield of a preparation and the efficiency of time invested by the researcher. Currently, data acquisition (DAQ) systems with high channel counts (>100) can be purchased from a limited number of companies at considerable prices. These systems are typically closed-source and thus prohibit custom extensions or improvements by end users. We have developed MANTA, an open-source MATLAB-based DAQ system, as an alternative to existing options. MANTA combines high channel counts (up to 1440 channels/PC), usage of analog or digital headstages, low per channel cost (<$90/channel), feature-rich display and filtering, a user-friendly interface, and a modular design permitting easy addition of new features. MANTA is licensed under the GPL and free of charge. The system has been tested by daily use in multiple setups for >1 year, recording reliably from 128 channels. It offers a growing list of features, including integrated spike sorting, PSTH and CSD display and fully customizable electrode array geometry (including 3D arrays), some of which are not available in commercial systems. MANTA runs on a typical PC and communicates via TCP/IP and can thus be easily integrated with existing stimulus generation/control systems in a lab at a fraction of the cost of commercial systems. With modern neuroscience developing rapidly, MANTA provides a flexible platform that can be rapidly adapted to the needs of new analyses and questions. Being open-source, the development of MANTA can outpace commercial solutions in functionality, while maintaining a low price-point. [ABSTRACT FROM AUTHOR]

Abstract: Due to the difficulty in spinal cord regeneration with biological methods, the microelectronic neural bridge, a new concept based on microelectronic technology, is presented. The microelectronic system has been realized in the forms of hybrid and integrated circuits. The integrated circuits for neural signal detection, stimulation, and regeneration are realized in a CMOS process. In animal experiments with 100 toads, 48 rats, and 3 rabbits, nerve signals have been successfully detected from spinal cords and sciatic nerves, and functional electrical stimulation has been carried out for spinal cords and sciatic nerves. When the microelectronic system is bridged between the controlling and stimulated nerve, the relevant motion of legs and nerve signal waveforms, which are stimulated by the evoked or spontaneous nerve signal through such a system, have been observed. Therefore, the feasibility of the presented method was demonstrated. [Copyright &y& Elsevier]

Time-frequency domain signal processing of neural recordings, from high-density microelectrode arrays implanted in the cortex, is highly desired to ease the bandwidth bottleneck associated with data transfer to extra-cranial processing units. Because of its energy compactness features, discrete wavelet transform (DWT) has been shown to provide efficient data compression for neural records without compromising the information content. This paper describes an area-power minimized hardware implementation of the lifting scheme for multilevel, multichannel DWT with quantized filter coefficients and integer computation. Performance tradeoffs and key design decisions for implantable neuroprosthetics are presented. A 32-channel 4-level version of the circuit has been custom designed in 0.18-mum CMOS and occupies only 0.22 mm2 area and consumes 76 muW of power, making it highly suitable for implantable neural interface applications requiring wireless data transfer. [ABSTRACT FROM PUBLISHER]

We report on our advances in sensory feedback data processing and control system design for functional electrical stimulation (FES) assisted correction of foot drop. We have applied 2 methods of signal purification on the bin integrated electroneurogram (i.e., optimized low pass filtering and wavelet denoising) before training adaptive logic networks (ALN). ALN generated stimulation control pulses, which correspond to the swing phase of the impaired leg when dorsal flexion of the foot is necessary to provide safe ground clearance. However, the obtained control signal contained sporadic stimulation spikes in the stance phase, which can collapse the subject, and infrequent broken stimulation pulses in the swing phase, which can result in unpredictable consequences. In this study, we have introduced adaptive restriction rules (ARR), which are initially used as previously reported and then dynamically adapted during the use of the system. Our results suggest that ARR provide a safer and more reliable stimulation pattern than fixed restriction rules. [ABSTRACT FROM AUTHOR]

In an effort to assess the safety and efficacy of focal intracortical microstimulation (ICMS) of cerebral cortex with an array of penetrating electrodes as might be applied to a neuroprosthetic device to aid the deaf or blind, the authors have chronically implanted 3 trained cats in primary auditory cortex with the 100-electrode Utah Intracortical Electrode Array (UIEA). Eleven of the 100 electrodes were hard-wired to a percutaneous connector for chronic access. Prior to implant, cats were trained to “lever-press” in response to pure tone auditory stimulation. After implant, this behavior was transferred to “lever-presses” in response to current injections via single electrodes of the implanted arrays. Psychometric function curves relating injected charge level to the probability of response were obtained for stimulation of 22 separate electrodes in the 3 implanted cats. The average threshold charge/phase required for electrical stimulus detection in each cat was, 8.5, 8.6, and 11.6 nC/phase respectively, with a maximum charge/phase of 26 nC/phase and a minimum of 1.5 nC/phase thresholds were tracked for varying time intervals, and 7 electrodes from 2 cats were tracked for up to 100 days. Electrodes were stimulated for no more than a few minutes each day. Neural recordings taken from the same electrodes before and after multiple electrical stimulation sessions were very similar in signal/noise ratio and in the number of recordable units, suggesting that the range of electrical stimulation levels used did not damage neurons in the vicinity of the electrodes. Although a few early implants failed, the authors conclude that ICMS of cerebral cortex to evoke a behavioral response can be achieved with the penetrating UIEA. Further experiments in support of a sensory cortical prosthesis based on ICMS are warranted [ABSTRACT FROM PUBLISHER]

Summary Ferrets are a major developmental animal model due to their early parturition. Here we show for the first time that ferrets could be used to study development of higher-level visual processes previously identified in primates. In primates, complex motion processing involves primary visual cortex (V1), which generates local motion signals, and higher-level visual area MT, which integrates these signals over more global spatial regions. Our data show similar transformations in motion signals between ferret V1 and higher-level visual area PSS, located in the posterior bank of the suprasylvian sulcus. We found that PSS neurons, like MT neurons, were tuned for stimulus motion and showed strong suppression between opposing direction inputs. Most strikingly, PSS, like MT, exhibited robust global motion signals when tested with coherent plaids—the classic test for motion integration across multiple moving elements. These PSS responses were described well by computational models developed for MT. Our findings establish the ferret as a strong animal model for development of higher-level visual processing. Highlights • Visual area PSS is a higher-level motion area in ferrets • PSS neurons show signatures of motion integration • PSS responses can be fit by a motion pathway model similar to ones for the primate • Complex motion processing in ferrets parallels that in primates Ferrets are a major animal model for development of early visual stages. Lempel and Nielsen establish signatures of complex motion processing in a higher visual area in the ferret, area PSS. This will allow developmental research in the ferret to expand into higher-level vision. [ABSTRACT FROM AUTHOR]