An energy-efficient, adiabatic electrode stimulator with inductive energy recycling and feedback current regulation.

In this paper, we present a novel energy-efficient electrode stimulator. Our stimulator uses inductive storage and recycling of energy in a dynamic power supply. This supply drives an electrode in an adiabatic fashion such that energy consumption is minimized. It also utilizes a shunt current-sensor to monitor and regulate the current through the electrode via feedback, thus enabling flexible and safe stimulation. Since there are no explicit current sources or current limiters, wasteful energy dissipation across such elements is naturally avoided. The dynamic power supply allows efficient transfer of energy both to and from the electrode and is based on a DC-DC converter topology that we use in a bidirectional fashion in forward-buck or reverse-boost modes. In an exemplary electrode implementation intended for neural stimulation, we show how the stimulator combines the efficiency of voltage control and the safety and accuracy of current control in a single low-power integrated-circuit built in a standard .35 µm CMOS process. This stimulator achieves a 2x-3x reduction in energy consumption as compared to a conventional current-source-based stimulator operating from a fixed power supply. We perform a theoretical analysis of the energy efficiency that is in accord with experimental measurements. This theoretical analysis reveals that further improvements in energy efficiency may be achievable with better implementations in the future. Our electrode stimulator could be widely useful for neural, cardiac, retinal, cochlear, muscular and other biomedical implants where low power operation is important.

Wireless neural stimulation in freely behaving small animals.

We introduce a novel wireless, low-power neural stimulation system for use in freely behaving animals. The system consists of an external transmitter and a miniature, implantable wireless receiver-stimulator. The implant uses a custom integrated chip to deliver biphasic current pulses to four addressable bipolar electrodes at 32 selectable current levels (10 microA to 1 mA). To achieve maximal battery life, the chip enters a sleep mode when not needed and can be awakened remotely when required. To test our device, we implanted bipolar stimulating electrodes into the songbird motor nucleus HVC (formerly called the high vocal center) of zebra finches. Single-neuron recordings revealed that wireless stimulation of HVC led to a strong increase of spiking activity in its downstream target, the robust nucleus of the arcopallium. When we used this device to deliver biphasic pulses of current randomly during singing, singing activity was prematurely terminated in all birds tested. Thus our device is highly effective for remotely modulating a neural circuit and its corresponding behavior in an untethered, freely behaving animal.

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.