Cognitive based neural prosthetics

Intense activity in neural prosthetic research has recently demonstrated the possibility of robotic interfaces that respond directly to the nervous system. The question remains of how the flow of information between the patient and the prosthetic device should be designed to provide a safe, effective system that maximizes the patient’s access to the outside world. Much recent work by other investigators has focused on using decoded neural signals as low-level commands to directly control the trajectory of screen cursors or robotic end-effectors. Here we review results that show that high-level, or cognitive, signals can be decoded from planned arm movements. These results, coupled with fundamental limitations in signal recording technology, motivate an approach in which cognitive neural signals play a larger role in the neural interface. This proposed paradigm predicates that neural signals should be used to instruct external devices, rather than control their detailed movement. This approach will reduce the effort required of the patient and will take advantage of established and on-going robotics research in intelligent systems and human-robot interfaces

Recording advances for neural prosthetics

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented

Brucellosis remains a neglected disease inthe developing world: a call forinterdisciplinary action

Brucellosis places significant burdens on the human healthcare system and limits the economic growth of individuals, communities, and nations where such development is especially important to diminish the prevalence of poverty. The implementation of public policy focused on mitigating the socioeconomic effects of brucellosis in human and animal populations is desperately needed. When developing a plan to mitigate the associated consequences, it is vital to consider both the abstract and quantifiable effects. This requires an interdisciplinary and collaborative, or One Health, approach that consists of public education, the development of an infrastructure for disease surveillance and reporting in both veterinary and medical fields, and campaigns for control in livestock and wildlife species

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Wavelet Methods and Inverse Problems

Archaeological investigations are designed to acquire information without damaging the archaeological site. Magnetometry is one of the important techniques for producing a surface grid of readings, which can be used to infer underground features. The inversion of this data, to give a fitted model, is an inverse problem. This type of problem can be ill-posed or ill-conditioned, making the estimation of model parameters less stable or even impossible. More precisely, the relationship between archaeological data and parameters is expressed by a likelihood. It is not possible to use the standard regression estimate obtained through the likelihood, which means that no maximum likelihood estimate exists. Instead, various constraints can be added through a prior distribution with an estimate produced using the posterior distribution. Current approaches incorporate prior information describing smoothness, which is not always appropriate. The biggest challenge is that the reconstruction of an archaeological site as a single layer requires various physical features such as depth and extent to be assumed. By applying a smoothing prior in the analysis of stratigraphy data, however, these features are not easily estimated. Wavelet analysis has proved to be highly efficient at eliciting information from noisy data. Additionally, complicated signals can be explained by interpreting only a small number of wavelet coefficients. It is possible that a modelling approach, which attempts to describe an underlying function in terms of a multi-level wavelet representation will be an improvement on standard techniques. Further, a new method proposed uses an elastic-net based distribution as the prior. Two methods are used to solve the problem, one is based on one-stage estimation and the other is based on two stages. The one-stage considers two approaches a single prior for all wavelet resolution levels and a level-dependent prior, with separate priors at each resolution level. In a simulation study and a real data analysis, all these techniques are compared to several existing methods. It is shown that the methodology using a single prior provides good reconstruction, comparable even to several established wavelet methods that use mixture priors

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals

Forward estimation of movement state in posterior parietal cortex

During goal-directed movements, primates are able to rapidly and accurately control an online trajectory despite substantial delay times incurred in the sensorimotor control loop. To address the problem of large delays, it has been proposed that the brain uses an internal forward model of the arm to estimate current and upcoming states of a movement, which are more useful for rapid online control. To study online control mechanisms in the posterior parietal cortex (PPC), we recorded from single neurons while monkeys performed a joystick task. Neurons encoded the static target direction and the dynamic movement angle of the cursor. The dynamic encoding properties of many movement angle neurons reflected a forward estimate of the state of the cursor that is neither directly available from passive sensory feedback nor compatible with outgoing motor commands and is consistent with PPC serving as a forward model for online sensorimotor control. In addition, we found that the space–time tuning functions of these neurons were largely separable in the angle–time plane, suggesting that they mostly encode straight and approximately instantaneous trajectories