Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

Long-term stability of neural signals from microwire arrays implanted in common marmoset motor cortex and striatum

Current neuroprosthetics rely on stable, high quality recordings from chronically implanted microelectrode arrays (MEAs) in neural tissue. While chronic electrophysiological recordings and electrode failure modes have been reported from rodent and larger non-human primate (NHP) models, chronic recordings from the marmoset model have not been previously described. The common marmoset is a New World primate that is easier to breed and handle compared to larger NHPs and has a similarly organized brain, making it a potentially useful smaller NHP model for neuroscience studies. This study reports recording stability and signal quality of MEAs chronically implanted in behaving marmosets. Six adult male marmosets, trained for reaching tasks, were implanted with either a 16-channel tungsten microwire array (five animals) or a Pt-Ir floating MEA (one animal) in the hand-arm region of the primary motor cortex (M1) and another MEA in the striatum targeting the nucleus accumbens (NAcc). Signal stability and quality was quantified as a function of array yield (active electrodes that recorded action potentials), neuronal yield (isolated single units during a recording session), and signal-to-noise ratio (SNR). Out of 11 implanted MEAs, nine provided functional recordings for at least three months, with two arrays functional for 10 months. In general, implants had high yield, which remained stable for up to several months. However, mechanical failure attributed to MEA connector was the most common failure mode. In the longest implants, signal degradation occurred, which was characterized by gradual decline in array yield, reduced number of isolated single units, and changes in waveform shape of action potentials. This work demonstrates the feasibility of longterm recordings from MEAs implanted in cortical and deep brain structures in the marmoset model. The ability to chronically record cortical signals for neural prosthetics applications in the common marmoset extends the potential of this model in neural interface research.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Common marmoset (Callithrix jacchus) as a primate model for behavioral neuroscience studies

BACKGROUND

The common marmoset (Callithrix jacchus) has been proposed as a suitable bridge between rodents and larger primates. They have been used in several types of research including auditory, vocal, visual, pharmacological and genetics studies. However, marmosets have not been used as much for behavioral studies.

NEW METHOD

Here we present data from training 12 adult marmosets for behavioral neuroscience studies. We discuss the husbandry, food preferences, handling, acclimation to laboratory environments and neurosurgical techniques. In this paper, we also present a custom built "scoop" and a monkey chair suitable for training of these animals.

RESULTS

The animals were trained for three tasks: 4 target center-out reaching task, reaching tasks that involved controlling robot actions, and touch screen task. All animals learned the center-out reaching task within 1-2 weeks whereas learning reaching tasks controlling robot actions task took several months of behavioral training where the monkeys learned to associate robot actions with food rewards.

COMPARISON TO EXISTING METHOD

We propose the marmoset as a novel model for behavioral neuroscience research as an alternate for larger primate models. This is due to the ease of handling, quick reproduction, available neuroanatomy, sensorimotor system similar to larger primates and humans, and a lissencephalic brain that can enable implantation of microelectrode arrays relatively easier at various cortical locations compared to larger primates.

CONCLUSION

All animals were able to learn behavioral tasks well and we present the marmosets as an alternate model for simple behavioral neuroscience tasks.

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics

OBJECTIVE

For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance.

APPROACH

Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture.

MAIN RESULTS

In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold.

SIGNIFICANCE

The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Data from proteomic characterization of the role of Snail1 in murine mesenchymal stem cells and 3T3-L1 fibroblasts differentiation

The transcription factor (TF) Snail1 is a major inducer of the epithelial–mesenchymal transition (EMT) during embryonic development and cancer progression. Ectopic expression of Snail in murine mesenchymal stem cells (mMSC) abrogated their differentiation to osteoblasts or adipocytes. We used either stable isotopic metabolic labeling (SILAC) for 3T3-L1 cells or isobaric labeling with tandem mass tags (TMT) for mMSC stably transfected cells with Snail1 or control. We carried out a proteomic analysis on the nuclear fraction since Snail is a nuclear TF that mediates its effects mainly through the regulation of other TFs. Proteomics data have been deposited in ProteomeXchange via the PRIDE partner repository with the dataset identifiers PXD001529 and PXD002157 (Vizcaino et al., 2014) [1]. Data are associated with a research article published in Molecular and Cellular Proteomics (Pelaez-Garcia et al., 2015) [2].

An adaptive brain actuated system for augmenting rehabilitation

For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

Midcervical neuronal discharge patterns during and following hypoxia

Anatomical evidence indicates that midcervical interneurons can be synaptically coupled with phrenic motoneurons. Accordingly, we hypothesized that interneurons in the C3-C4 spinal cord can display discharge patterns temporally linked with inspiratory phrenic motor output. Anesthetized adult rats were studied before, during, and after a 4-min bout of moderate hypoxia. Neuronal discharge in C3-C4 lamina I-IX was monitored using a multielectrode array while phrenic nerve activity was extracellularly recorded. For the majority of cells, spike-triggered averaging (STA) of ipsilateral inspiratory phrenic nerve activity based on neuronal discharge provided no evidence of discharge synchrony. However, a distinct STA phrenic peak with a 6.83 ± 1.1 ms lag was present for 5% of neurons, a result that indicates a monosynaptic connection with phrenic motoneurons. The majority (93%) of neurons changed discharge rate during hypoxia, and the diverse responses included both increased and decreased firing. Hypoxia did not change the incidence of STA peaks in the phrenic nerve signal. Following hypoxia, 40% of neurons continued to discharge at rates above prehypoxia values (i.e., short-term potentiation, STP), and cells with initially low discharge rates were more likely to show STP (P < 0.001). We conclude that a population of nonphrenic C3-C4 neurons in the rat spinal cord is synaptically coupled to the phrenic motoneuron pool, and these cells can modulate inspiratory phrenic output. In addition, the C3-C4 propriospinal network shows a robust and complex pattern of activation both during and following an acute bout of hypoxia.

A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.

Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions

Changes in biotic and abiotic factors can be reflected in the complex impedance spectrum of the microelectrodes chronically implanted into the neural tissue. The recording surface of the tungsten electrode in vivo undergoes abiotic changes due to recording site corrosion and insulation delamination as well as biotic changes due to tissue encapsulation as a result of the foreign body immune response. We reported earlier that large changes in electrode impedance measured at 1 kHz were correlated with poor electrode functional performance, quantified through electrophysiological recordings during the chronic lifetime of the electrode. There is a need to identity the factors that contribute to the chronic impedance variation. In this work, we use numerical simulation and regression to equivalent circuit models to evaluate both the abiotic and biotic contributions to the impedance response over chronic implant duration. COMSOL® simulation of abiotic electrode morphology changes provide a possible explanation for the decrease in the electrode impedance at long implant duration while biotic changes play an important role in the large increase in impedance observed initially.

A tensor-product-kernel framework for multiscale neural activity decoding and control

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.

Abiotic-biotic characterization of Pt/Ir microelectrode arrays in chronic implants

Pt/Ir electrodes have been extensively used in neurophysiology research in recent years as they provide a more inert recording surface as compared to tungsten or stainless steel. While floating microelectrode arrays (FMA) consisting of Pt/Ir electrodes are an option for neuroprosthetic applications, long-term in vivo functional performance characterization of these FMAs is lacking. In this study, we have performed comprehensive abiotic-biotic characterization of Pt/Ir arrays in 12 rats with implant periods ranging from 1 week up to 6 months. Each of the FMAs consisted of 16-channel, 1.5 mm long, and 75 μm diameter microwires with tapered tips that were implanted into the somatosensory cortex. Abiotic characterization included (1) pre-implant and post-explant scanning electron microscopy (SEM) to study recording site changes, insulation delamination and cracking, and (2) chronic in vivo electrode impedance spectroscopy. Biotic characterization included study of microglial responses using a panel of antibodies, such as Iba1, ED1, and anti-ferritin, the latter being indicative of blood-brain barrier (BBB) disruption. Significant structural variation was observed pre-implantation among the arrays in the form of irregular insulation, cracks in insulation/recording surface, and insulation delamination. We observed delamination and cracking of insulation in almost all electrodes post-implantation. These changes altered the electrochemical surface area of the electrodes and resulted in declining impedance over the long-term due to formation of electrical leakage pathways. In general, the decline in impedance corresponded with poor electrode functional performance, which was quantified via electrode yield. Our abiotic results suggest that manufacturing variability and insulation material as an important factor contributing to electrode failure. Biotic results show that electrode performance was not correlated with microglial activation (neuroinflammation) as we were able to observe poor performance in the absence of neuroinflammation, as well as good performance in the presence of neuroinflammation. One biotic change that correlated well with poor electrode performance was intraparenchymal bleeding, which was evident macroscopically in some rats and presented microscopically by intense ferritin immunoreactivity in microglia/macrophages. Thus, we currently consider intraparenchymal bleeding, suboptimal electrode fabrication, and insulation delamination as the major factors contributing toward electrode failure.

Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

Intraspinal transplantation and modulation of donor neuron electrophysiological activity

Rat fetal spinal cord (FSC) tissue, naturally enriched with interneuronal progenitors, was introduced into high cervical, hemi-resection (Hx) lesions. Electrophysiological analyses were conducted to determine if such grafts exhibit physiologically-patterned neuronal activity and if stimuli which increase respiratory motor output also alter donor neuron bursting. Three months following transplantation, the bursting activity of FSC neurons and the contralateral phrenic nerve were recorded in anesthetized rats during a normoxic baseline period and brief respiratory challenges. Spontaneous neuronal activity was detected in 80% of the FSC transplants, and autocorrelation of action potential spikes revealed distinct correlogram peaks in 87% of neurons. At baseline, the average discharge frequency of graft neurons was 13.0 ± 1.7 Hz, and discharge frequency increased during a hypoxic respiratory challenge (p<0.001). Parallel studies in unanesthetized rats showed that FSC tissue recipients had larger inspiratory tidal volumes during brief hypoxic exposures (p<0.05 vs. C2Hx rats). Anatomical connectivity was explored in additional graft recipients by injecting a transsynaptic retrograde viral tracer (pseudorabies virus, PRV) directly into matured transplants. Neuronal labeling occurred throughout graft tissues and also in the host spinal cord and brainstem nuclei, including those associated with respiratory control. These results underscore the neuroplastic potential of host-graft interactions and training approaches to enhance functional integration within targeted spinal circuitry.

A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces

This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.

Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

OBJECTIVE

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance.

APPROACH

Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks.

MAIN RESULTS

The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance.

SIGNIFICANCE

By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

A highly compliant serpentine shaped polyimide interconnect for front-end strain relief in chronic neural implants

While the signal quality of recording neural electrodes is observed to degrade over time, the degradation mechanisms are complex and less easily observable. Recording microelectrodes failures are attributed to different biological factors such as tissue encapsulation, immune response, and disruption of blood-brain barrier (BBB) and non-biological factors such as strain due to micromotion, insulation delamination, corrosion, and surface roughness on the recording site (1–4). Strain due to brain micromotion is considered to be one of the important abiotic factors contributing to the failure of the neural implants. To reduce the forces exerted by the electrode on the brain, a high compliance 2D serpentine shaped electrode cable was designed, simulated, and measured using polyimide as the substrate material. Serpentine electrode cables were fabricated using MEMS microfabrication techniques, and the prototypes were subjected to load tests to experimentally measure the compliance. The compliance of the serpentine cable was numerically modeled and quantitatively measured to be up to 10 times higher than the compliance of a straight cable of same dimensions and material.

A trial of scheduled deep brain stimulation for Tourette syndrome: moving away from continuous deep brain stimulation paradigms

OBJECTIVE

To collect the information necessary to design the methods and outcome variables for a larger trial of scheduled deep brain stimulation (DBS) for Tourette syndrome.

DESIGN

We performed a small National Institutes of Health-sponsored clinical trials planning study of the safety and preliminary efficacy of implanted DBS in the bilateral centromedian thalamic region. The study used a cranially contained constant-current device and a scheduled, rather than the classic continuous, DBS paradigm. Baseline vs 6-month outcomes were collected and analyzed. In addition, we compared acute scheduled vs acute continuous vs off DBS.

SETTING

A university movement disorders center.

PATIENTS

Five patients with implanted DBS.

MAIN OUTCOME MEASURE

A 50% improvement in the Yale Global Tic Severity Scale (YGTSS) total score. RESULTS Participating subjects had a mean age of 34.4 (range, 28-39) years and a mean disease duration of 28.8 years. No significant adverse events or hardware-related issues occurred. Baseline vs 6-month data revealed that reductions in the YGTSS total score did not achieve the prestudy criterion of a 50% improvement in the YGTSS total score on scheduled stimulation settings. However, statistically significant improvements were observed in the YGTSS total score (mean [SD] change, -17.8 [9.4]; P=.01), impairment score (-11.3 [5.0]; P=.007), and motor score (-2.8 [2.2]; P=.045); the Modified Rush Tic Rating Scale Score total score (-5.8 [2.9]; P=.01); and the phonic tic severity score (-2.2 [2.6]; P=.04). Continuous, off, and scheduled stimulation conditions were assessed blindly in an acute experiment at 6 months after implantation. The scores in all 3 conditions showed a trend for improvement. Trends for improvement also occurred with continuous and scheduled conditions performing better than the off condition. Tic suppression was commonly seen at ventral (deep) contacts, and programming settings resulting in tic suppression were commonly associated with a subjective feeling of calmness.

CONCLUSIONS

This study provides safety and proof of concept that a scheduled DBS approach could improve motor and vocal tics in Tourette syndrome. Refinements in neurostimulator battery life, outcome measure selection, and flexibility in programming settings can be used to enhance outcomes in a future larger study. Scheduled stimulation holds promise as a potential first step for shifting movement and neuropsychiatric disorders toward more responsive neuromodulation approaches.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01329198.

Decoding stimuli from multi-source neural responses

Spike trains and local field potentials (LFPs) are two different manifestations of neural activity recorded simultaneously from the same electrode array and contain complementary information of stimuli or behaviors. This paper proposes a tensor product kernel based decoder, which allows modeling the sample from different sources individually and mapping them onto the same reproducing kernel Hilbert space (RKHS) defined by the tensor product of the individual kernels for each source, where linear regression is conducted to identify the nonlinear mapping from the multi-type neural responses to the stimuli. The decoding results of the rat sensory stimulation experiment show that the tensor-product-kernel-based decoder outperforms the decoders with either single-type neural activities.

Increased thalamic gamma band activity correlates with symptom relief following deep brain stimulation in humans with Tourette's syndrome

Tourette syndrome (TS) is an idiopathic, childhood-onset neuropsychiatric disorder, which is marked by persistent multiple motor and phonic tics. The disorder is highly disruptive and in some cases completely debilitating. For those with severe, treatment-refractory TS, deep brain stimulation (DBS) has emerged as a possible option, although its mechanism of action is not fully understood. We performed a longitudinal study of the effects of DBS on TS symptomatology while concomitantly examining neurophysiological dynamics. We present the first report of the clinical correlation between the presence of gamma band activity and decreased tic severity. Local field potential recordings from five subjects implanted in the centromedian nucleus (CM) of the thalamus revealed a temporal correlation between the power of gamma band activity and the clinical metrics of symptomatology as measured by the Yale Global Tic Severity Scale and the Modified Rush Tic Rating Scale. Additional studies utilizing short-term stimulation also produced increases in gamma power. Our results suggest that modulation of gamma band activity in both long-term and short-term DBS of the CM is a key factor in mitigating the pathophysiology associated with TS.

Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework

The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate representation of neural signal (i.e., spikernel and generalized linear model). Moreover, after a significant perturbation of the neuron circuit, the control scheme can successfully drive the elicited responses close to the original target responses.

Coupling biotic and abiotic metrics to create a testbed for predicting neural electrode performance

In this work, we develop an experimental testbed that couples biotic and abiotic metrics for studying, quantifying and predicting the effects of chronic electrode implantation on neural electrode performance. The rationale is based on the observation that long-term functionality is the outcome of the interactions between the dynamics of the neuronal environment and the properties of the electrode itself. By combining and analyzing the substantially richer information available in the spatiotemporal dynamics of neurons with biotic and abiotic metrics such as biochemical markers, histochemistry, SEM imaging, and electrochemistry, we seek to quantitatively improve our understanding of the functional modifications underlying the long-term responses of electrode implants. The goal is to ultimately enable the design of future reliable interfaces. In our preliminary analysis using this biotic-abiotic approach of an electrode 18 days post-implant, we observed both structural and histochemical responses related to chronic electrode implantation. These were coupled to daily functional changes in electrode performance. Interestingly, these changes were not correlated with markers of brain injury at the time of electrode explantation. Future work using this multidisciplinary approach is directed to providing a detailed perspective into long-term microelectrode performance.

Reinforcement learning via kernel temporal difference

This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this paper. Experimental results show the method's applicability for learning motor state decoding during a center-out reaching task performed by a monkey. The results are compared to the implementation of a time delay neural network (TDNN) trained with backpropagation of the temporal difference error. From the experiments, it is observed that kernel TD(0) allows faster convergence and a better solution than the neural network.

Quantifying long-term microelectrode array functionality using chronic in vivo impedance testing

Long-term acquisition of high-quality neural recordings is a cornerstone of neuroprosthetic system design. Mitigating the experimental variability of chronically implanted arrays has been a formidable task because the sensor recording sites can be influenced by biotic and abiotic responses. Several studies have implicated changes in electrical interface impedance as a preliminary marker to infer electrode viability. Microelectrode impedance plays an important role in the monitoring of low amplitude and high-resolution extracellular neural signals. In this work, we seek to quantify long-term microelectrode array functionality and derive an impedance-based predictor for electrode functionality that correlates the recording site electrical properties with the functional neuronal recordings in vivo. High temporal resolution metrics of this type would allow one to assess, predict, and improve electrode performance in the future. In a large cohort of animals, we performed daily impedance measurements and neural signal recordings over long periods (up to 21 weeks) of time in rats using tungsten microwire arrays implanted into the somatosensory cortex. This study revealed that there was a time-varying trend in the modulation of impedance that was related to electrode performance. Single units were best detected from electrodes at time points when the electrode entered into the 40-150 KΩ impedance range. This impedance trend was modeled across the full cohort of animals to predict future electrode performance. The model was tested on data from all animals and was able to provide predictions of electrode performance chronically. Insight from this study can be combined with knowledge of electrode materials and histological analysis to provide a more comprehensive predictive model of electrode failure in the future.

Comprehensive characterization of tungsten microwires in chronic neurocortical implants

The long-term performance of chronic microelectrode array implants for neural ensemble recording is affected by temporal degradation in signal quality due to several factors including structural changes in the recording surface, electrical responses, and tissue immune reactivity. This study combines the information available from the temporal aggregation of both biotic and abiotic metrics to analyze and quantify the combined effects on long-term performance. Study of a 42-day implant showed there was a functional relationship between the measured impedance and the array neuronal yield. This was correlated with structural changes in the recording sites, microglial activation/degeneration, and elevation of a blood biochemical marker for axonal injury. We seek to elucidate the mechanisms of chronic microelectrode array failure through the study of the combined effects of these biotic and abiotic factors.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Functional connectivity dynamics among cortical neurons: a dependence analysis

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.

An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces

One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.

Corrosion of tungsten microelectrodes used in neural recording applications

In neuroprosthetic applications, long-term electrode viability is necessary for robust recording of the activity of neural populations used for generating communication and control signals. The corrosion of tungsten microwire electrodes used for intracortical recording applications was analyzed in a controlled bench-top study and compared to the corrosion of tungsten microwires used in an in vivo study. Two electrolytes were investigated for the bench-top electrochemical analysis: 0.9% phosphate buffered saline (PBS) and 0.9% PBS containing 30 mM of hydrogen peroxide. The oxidation and reduction reactions responsible for corrosion were found by measurement of the open circuit potential and analysis of Pourbaix diagrams. Dissolution of tungsten to form the tungstic ion was found to be the corrosion mechanism. The corrosion rate was estimated from the polarization resistance, which was extrapolated from the electrochemical impedance spectroscopy data. The results show that tungsten microwires in an electrolyte of PBS have a corrosion rate of 300-700 μm/yr. The corrosion rate for tungsten microwires in an electrolyte containing PBS and 30 mM H₂O₂ is accelerated to 10,000-20,000 μm/yr. The corrosion rate was found to be controlled by the concentration of the reacting species in the cathodic reaction (e.g. O₂ and H₂O₂). The in vivo corrosion rate, averaged over the duration of implantation, was estimated to be 100 μm/yr. The reduced in vivo corrosion rate as compared to the bench-top rate is attributed to decreased rate of oxygen diffusion caused by the presence of a biological film and a reduced concentration of available oxygen in the brain.

A symbiotic brain-machine interface through value-based decision making

Background

In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC).

Methodology

The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc.

Conclusions

Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain.

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

Neuronal functional connectivity dynamics in cortex: An MSC-based analysis

The activation of neural ensembles in the cortex is correlated with behavioral states and a change in neuronal functional connectivity patterns is expected. In this paper, we investigate this dynamic nature of functional connectivity in the cortex. Because of the time scale of behavior, a robust method with limited sample size is desirable. In light of this, we utilize mean square contingency (MSC) to measure the pairwise neural dependency to quantify the cortical functional connectivity. Simulation results show that MSC is more robust than cross correlation when the sample size is small. In monkey neural data test, our approach is more effective in detecting the dynamics of functional connectivity associated with the transitions between rest and movement states.

An Actor-Critic architecture and simulator for goal-directed Brain-Machine Interfaces

The Perception-Action Cycle (PAC) is a central component of goal-directed behavior because it links internal percepts with external outcomes in the environment. Using inspiration from the PAC, we are developing a Brain-Machine Interface control architecture that utilizes both motor commands and goal information directly from the brain to navigate to novel targets in an environment. An Actor-Critic algorithm was selected for decoding the neural motor commands because it is a PAC-based computational framework where the perception component is implemented in the critic structure and the actor is responsible for taking actions. We develop in this work a biologically realistic simulator to analyze the performance of the decoder in terms of convergence and target acquisition. Experience from the simulator will guide parameter selection and assist in understanding the architecture before animal experiments. By varying the signal to noise ratio of the neural input and error signal, we were able to demonstrate how the learning rate and initial conditions affect a motor control target selection task. In this framework, the naïve decoder was able to reach targets in the presence of noise in the error signal and neural motor command with 98% accuracy.

Cyber-workstation for computational neuroscience

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Spatio-temporal clustering of firing rates for neural state estimation

Characterizing the dynamics of neural data by a discrete state variable is desirable in experimental analysis and brain-machine interfaces. Previous successes have used dynamical modeling including Hidden Markov Models, but the methods do not always produce meaningful results without being carefully trained or initialized. We propose unsupervised clustering in the spatio-temporal space of neural data using time embedding and a corresponding distance measure. By defining performance measures, the method parameters are investigated for a set of neural and simulated data with promising results. Our investigations demonstrate a different view of how to extract information to maximize the utility of state estimation.

Estimation and visualization of neuronal functional connectivity in motor tasks

In brain-machine interface (BMI) modeling, the firing patterns of hundreds of neurons are used to reconstruct a variety of kinematic variables. The large number of neurons produces an explosion in the number of free parameters, which affects model generalization. This paper proposes a model-free measure of pairwise neural dependence to rank the importance of neurons in neural to motor mapping. Compared to a model-dependent approach such as sensitivity analysis, sixty percent of the neurons with the strongest dependence coincide with the top 10 most sensitive neurons trained through the model. Using this data-driven approach that operates on the input data alone, it is possible to perform neuron selection in a more efficient way that is not subject to assumptions about decoding models. To further understand the functional dependencies that influence neural to motor mapping, we use an open source available graph visualization toolkit called Prefuse to visualize the neural dependency graph and quantify the functional connectivity in motor cortex. This tool when adapted to the analysis of neuronal recordings has the potential to easily display the relationships in data of large dimension.

Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces

Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. We have also proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. This letter presents a systematic testing of this algorithm in a simulated neural spike train decoding experiment and then in BMI data. Compared to a point-process adaptive filtering algorithm with a linear observation model and a gaussian approximation (the counterpart for point processes of the Kalman filter), our sequential Monte Carlo estimation methodology exploits a detailed encoding model (tuning function) derived for each neuron from training data. However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.

Coadaptive brain-machine interface via reinforcement learning

This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.

Neuronal tuning in a brain-machine interface during Reinforcement Learning

In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.

The effects of interictal spikes on single neuron firing patterns in the hippocampus during the development of temporal lobe epilepsy

The interictal spikes (IS) are characteristic signatures of epileptic tissue, and relevant in presurgical evaluation of epilepsy. However, the mechanism and effects of IS remain unclear. In this study, we examined the relationship between IS and single neuron firing patterns, using an epileptic rat with temporal lobe epilepsy (TLE). We investigated the firing of interneurons and pyramidal cells in the CA3 region of the hippocampus. The results show that IS are associated with decreased single neuron firing rates compared with IS-free epochs. Furthermore, the paroxysmal interictal-spiking patterns are associated with sustained decrease in single neuron firing rates. We also found that IS stopped as approaching to seizures. These results demonstrate that IS might be responsible for the development of TLE and ictal events by changing the firing patterns of hippocampal neurons.

Flexible polymer substrate and tungsten microelectrode array for an implantable neural recording system

This paper describes the process flow and testing of a substrate for a fully implantable neural recording system. Tungsten microwires are hybrid-packaged on a micromachined flexible polymer substrate forming an intracortical microelectrode array for brain machine interfaces. The microelectrode array is characterized on the bench top and tested in vivo. The microelectrode noise floor is less than 2 microV and acute recording results show a signal to noise ratio of 9.9-17.3 dB. The technique of hybrid fabrication of the electrodes on a flexible substrate provides a general platform for the development of an implantable neural recording system.

Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants

The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants.

Peri-event cross-correlation over time for analysis of interactions in neuronal firing

Several methods have been described in the literature to verify the presence of couplings between neurons in the brain. In this paper we introduce the peri-event cross-correlation over time (PECCOT) to describe the interaction among the two neurons as a function of the event onset. Instead of averaging over time, the PECCOT averages the cross-correlation over instances of the event. As a consequence, the PECCOT is able to characterize with high temporal resolution the interactions over time among neurons. To illustrate the method, the PECCOT is applied to a simulated dataset and for analysis of synchrony in recordings of a rat performing a go/no go behavioral lever press task. We verify the presence of synchrony before the lever press time and its suppression afterwards.

Improved linear BMI systems via population averaging

We investigate population averaging as a preprocessing stage for linear FIR BMIs. Population averaging is a biologically-inspired technique based on spatial constraints and neuronal correlation. We achieve a statistically significant improvement in accuracy while substantially (45%) reducing model parameters. Further analysis is performed to show that population averaging improves model accuracy by reducing variance in estimating the firing rate from spike bins. However, we find that population averaging provides a greater accuracy improvement than other groupings which also reduce firing rate variance. Our results suggest that appropriate spatial organization of neural signals enhances BMI performance.