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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., National Eye Institute (grant R01-EY13337), United States National Institutes of Health (grants R01-NS056140 and R01-EY15545), McGovern Institute for Brain Research at MIT. Neurotechnology (MINT) Program

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.

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.

This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, 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; radio-frequency (RF) impedance modulation for low-power data telemetry; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons. Experimental results from chips that have recorded from and stimulated neurons in the zebra-finch brain and from RF power-link systems are presented. Circuit simulations that have successfully processed prerecorded data from a monkey brain and from an RF data telemetry system are also presented.

This work presents the first parylene-insulated silicon probes, which are used for neural prostheses to record high-level cognitive neural signals. With parylene technology, our probes have several advantages compared with the current devices. First, instead of inorganic materials (e.g. silicon dioxide, silicon nitride), the electrodes and conduction traces on the probes are insulated by parylene, an easily-deposited polymer with mechanical flexibility and biocompatibility. As a result, the probes exhibit better electrical and mechanical properties. Second, flexible parylene cables are monolithically integrated with the probes, which arm the probes with very high flexibility to be easily assembled to a high density 3-D array and at the same time provide an ideal method to transmit neural signals through skull during chronic recording. The all dry fabrication process and a 4 X 4 probe array (64 electrodes) were demonstrated. The probes were successfully tested electrically and mechanically in rat cortex. Neural signals were properly recorded.

This work presents the first parylene-insulated silicon probes, which are used for neural prostheses to record high-level cognitive neural signals. With parylene technology, our probes have several advantages compared with the current devices. First, instead of inorganic materials (e.g. SiO_2 and Si_3N_4), the electrodes and conduction traces on the probes are insulated by parylene, an easily-deposited polymer with mechanical flexibility and biocompatibility. As a result, the probes exhibit better electrical and mechanical properties. Second, flexible parylene cables are monolithically integrated with the probes, which arm the probes with very high flexibility to be easily assembled to a high density 3-D array and at the same time provide an ideal method to transmit neural signals through skull during chronic recording. The all dry fabrication process and an 8x2 probe array (64 electrodes) were demonstrated. The probes were successfully tested electrically and mechanically in rat and monkey cortex. Neural signals were properly recorded.

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.