Characterization of neurons in the primate medial intraparietal area reveals a joint representation of intended reach direction and amplitude.

To support accurate memory-guided reaching, the brain must represent both the direction and amplitude of reaches in a movement plan. Several cortical areas have been shown to represent the direction of a planned reaching movement, but the neuronal representation of reach amplitude is still unclear, especially in sensory-motor integration areas. To investigate this, we recorded from neurons in the medial intraparietal area (MIP) of monkeys performing a variable amplitude memory reach task. In one monkey, we additionally recorded from the dorsal premotor cortex (PMd) for direct cross-area comparisons. In both areas, we found modest but significant proportions of neurons with movement-planning activity sensitive to reach amplitude. However, reach amplitude was under-represented relative to direction in the neuronal population, with approximately one third as many selective neurons. We observed an interaction between neuronal selectivity for amplitude and direction; neurons in both areas exhibited significant modulation of neuronal activity by reach amplitude in some but not all directions. Consistent with an encoding of reach goals as a position in visual space, the response patterns of MIP/PMd neurons were best predicted by 2D Gaussian position encoding model, in contrast to a number of alternative direction and amplitude tuning models. Taken together, these results suggest that amplitude and direction jointly modulate activity in MIP, as in PMd, to form representations of intended reach position.

Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward.

To restore movements to paralyzed patients, neural prosthetic systems must accurately decode patients' intentions from neural signals. Despite significant advancements, current systems are unable to restore complex movements. Decoding reward-related signals from the medial intraparietal area (MIP) could enhance prosthetic performance. However, the dynamics of reward sensitivity in MIP is not known. Furthermore, reward-related modulation in premotor areas has been attributed to behavioral confounds. Here we investigated the stability of reward encoding in MIP by assessing the effect of reward history on reward sensitivity. We recorded from neurons in MIP while monkeys performed a delayed-reach task under two reward schedules. In the variable schedule, an equal number of small- and large-rewards trials were randomly interleaved. In the constant schedule, one reward size was delivered for a block of trials. The memory period firing rate of most neurons in response to identical rewards varied according to schedule. Using systems identification tools, we attributed the schedule sensitivity to the dependence of neural activity on the history of reward. We did not find schedule-dependent behavioral changes, suggesting that reward modulates neural activity in MIP. Neural discrimination between rewards was less in the variable than in the constant schedule, degrading our ability to decode reach target and reward simultaneously. The effect of schedule was mitigated by adding Haar wavelet coefficients to the decoding model. This raises the possibility of multiple encoding schemes at different timescales and reinforces the potential utility of reward information for prosthetic performance.

Applying Time-sharing technique in a multimodal compact low-power CMOS neurochip for simultaneous neurochemical and action potential recording.

Brain is an electrochemical system and recent studies suggest simultaneous measurement of interrelated brain's electrical and neurochemical activity may lead to better understanding of brain function in addition to developing optimal neural prosthetics. By exploiting opamp Time-sharing technique to minimized power dissipation and silicon area, we have fabricated a power efficient implantable CMOS microsystem for simultaneous measurement of Action Potential (AP) and neurotransmitter concentration. Both AP-recording and neurotransmitter sensing subsystems share a single 653 nW amplifier which senses picoscale to microscale current that corresponds to micromolar neurotransmitter concentration and microscale AP voltage. This microsystem is fabricated in CMOS 0.18 µm technology and tested using recorded signals from dorsal premotor cortex (PMd) area of a macaque monkey in our lab.

Effects of TMS coil geometry on stimulation specificity.

Transcranial magnetic stimulation has become an established tool in experimental cognitive neuroscience and has more recently been applied clinically. The current spatial extent of neural activation is several millimeters but with greater specificity, transcranial magnetic stimulation can potentially deliver real time feedback to reinforce or extinguish behavior by exciting or inhibiting localized neural circuits. The specificity of transcranial magnetic stimulation is a function of the stimulation coil geometry. In this paper, a practical multilayer framework for the design of miniaturized stimulation coils is presented. This framework is based on a magnet wire fabricated from 2500 braided ultrafine wires. Effects of coil bending angle on stimulation specificity are examined using realistic finite element method simulations. A novel stimulation coil with one degree of freedom is also proposed that shows improved specificity over the conventional fixed coils. This type of coil could be potentially used as a feedback system for a bidirectional brain machine interface.

A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

Parietal reach region encodes reach depth using retinal disparity and vergence angle signals.

Performing a visually guided reach requires the ability to perceive the egocentric distance of a target in three-dimensional space. Previous studies have shown that the parietal reach region (PRR) encodes the two-dimensional location of frontoparallel targets in an eye-centered reference frame. To investigate how a reach target is represented in three dimensions, we recorded the spiking activity of PRR neurons from two rhesus macaques trained to fixate and perform memory reaches to targets at different depths. Reach and fixation targets were configured to explore whether neural activity directly reflects egocentric distance as the amplitude of the required motor command, which is the absolute depth of the target, or rather the relative depth of the target with reference to fixation depth. We show that planning activity in PRR represents the depth of the reach target as a function of disparity and fixation depth, the spatial parameters important for encoding the depth of a reach goal in an eye centered reference frame. The strength of modulation by disparity is maintained across fixation depth. Fixation depth gain modulates disparity tuning while preserving the location of peak tuning features in PRR neurons. The results show that individual PRR neurons code depth with respect to the fixation point, that is, in eye centered coordinates. However, because the activity is gain modulated by vergence angle, the absolute depth can be decoded from the population activity.

Decoding trajectories from posterior parietal cortex ensembles.

High-level cognitive signals in the posterior parietal cortex (PPC) have previously been used to decode the intended endpoint of a reach, providing the first evidence that PPC can be used for direct control of a neural prosthesis (Musallam et al., 2004). Here we expand on this work by showing that PPC neural activity can be harnessed to estimate not only the endpoint but also to continuously control the trajectory of an end effector. Specifically, we trained two monkeys to use a joystick to guide a cursor on a computer screen to peripheral target locations while maintaining central ocular fixation. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. Using a goal-based Kalman filter that incorporates target information into the state-space, we showed that the decoded estimate of cursor position could be significantly improved. Finally, we tested whether we could decode trajectories during closed-loop brain control sessions, in which the real-time position of the cursor was determined solely by a monkey's neural activity in PPC. The monkey learned to perform brain control trajectories at 80% success rate (for 8 targets) after just 4-5 sessions. This improvement in behavioral performance was accompanied by a corresponding enhancement in neural tuning properties (i.e., increased tuning depth and coverage of encoding parameter space) as well as an increase in off-line decoding performance of the PPC ensemble.

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.

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.

NeuroMEMS: Neural Probe Microtechnologies.

Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

A floating metal microelectrode array for chronic implantation.

Implantation of multi-electrode arrays is becoming increasingly more prevalent within the neuroscience research community and has become important for clinical applications. Many of these studies have been directed towards the development of sensory and motor prosthesis. Here, we present a multi-electrode system made from biocompatible material that is electrically and mechanically stable, and employs design features allowing flexibility in the geometric layout and length of the individual electrodes within the array. We also employ recent advances in laser machining of thin ceramic substrates, application of ultra-fine line gold conductors to ceramic, fabrication of extremely flexible cables, and fine wire management techniques associated with juxtaposing metal microelectrodes within a few hundred microns of each other in the development of a floating multi-electrode array (FMA). We implanted the FMA in rats and show that the FMA is capable of recording both spikes and local field potentials.

A new multi-site probe array with monolithically integrated parylene flexible cable for neural prostheses.

This work presents a new multi-site probe array applied with parylene technology, used for neural prostheses to record high-level cognitive neural signals. Instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on probes are insulated by parylene, which is a polymer material with high electrical resistivity, mechanical flexibility, biocompatibility and easy deposition process. As a result, the probes exhibit better electrical and mechanical properties. The all dry process is demonstrated to fabricate these probe arrays with monolithically integrated parylene flexible cables using double-side-polished (DSP) wafers. With the parylene flexible cables, the probes can be easily assembled to a high density 3-D array for chronic implantation.

Selecting the signals for a brain-machine interface.

Brain-machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.

Asymmetric integration recorded from vestibular-only cells in response to position transients.

Angular and translational accelerations excite the semicircular canals and otolith organs, respectively. While canal afferents approximately encode head angular velocity due to the biomechanical integration performed by the canals, otolith signals have been found to approximate head translational acceleration. Because central vestibular pathways require velocity and position signals for their operation, the question has been raised as to how the integration of the otolith signals is accomplished. We recorded responses from 62 vestibular-only neurons in the vestibular nucleus of two monkeys to position transients in the naso-occipital and interaural orientations and varying directions in between. Responses to the transients were directionally asymmetric; one direction elicited a response that approximated the integral of the acceleration of the stimulus. In the opposite direction, the cells simply encoded the acceleration of the motion. We present a model that suggests that a neural integrator is not needed. Instead a neuron with a long membrane time constant and an excitatory postsynaptic potential duration that increases with the firing rate of the presynaptic cell can emulate the observed behavior.