Scott K. Arfin

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, Parkinsons 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.

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.

Fast startup CMOS current references

We describe an approximately-PTAT CMOS current reference circuit that is useful for large analog systems. An innovative capacitively-coupled startup circuit that draws no static power is also presented. Experimental results from 0.5 mum and 0.18 mum implementations are shown. Both references are cascoded and use no resistors. The 0.18 mum version operates down to VDD = 0.5V

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, 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.

Principles of high–fidelity, high–density 3–d neural recording

New probe technologies, neural amplifier systems, and data acquisition systems enable the extracellular electrical recording of ever greater numbers of neurons in the live mammalian brain. These recordings have the potential to increase our understanding of neuronal network dynamics, but much remains uncharacterized about the possibilities and limitations of extracellular techniques. We explore these possibilities and limitations in the context of spike sorting and probe design. Spike sorting is a critical analysis step for extracellular data, which attempts to separate raw electrode traces into the activity patterns of individual neurons. Given the labor associated with manual spike sorting of large datasets, the necessity for automated spike sorting method will only increase. An automated method would ideally commit no errors in spike assignment – that is, it would associate each extracted individual neuron with all the spikes fired by a single neuron, and with no spikes not fired by that same neuron. The elimination of errors would reduce the reliance on manual validation, saving large amounts of analysis time, and also reduce downstream biases in data analyses introduced by errors in spike sorting. We explore designs for multi-electrode probes and spike sorting methods that in combination allow high accuracy in spike assignment with many neurons extracted. To this end, first, we ask if an automated spike sorting method with zero spike assignment errors is possible. Second, we explore what multi-electrode probe designs produce optimal yield using this method. We have constructed a spike sorting method for the case of spatially dense high channel count extracellular recordings, first applying a well-established source separation technique called Independent Components Analysis (ICA) to continuous recordings. Second, we apply a classifier to the ICA components, keeping only putative single neuron units that are well separated from noise and other units. To test this algorithm, we simulated multielectrode probe data that encompasses many of the realistic variations and noisinesses of natural neural data, including spatial non-linearities in spike shape from individual cells. Running this algorithm against this simulated data, for a wide range of classifier parameters, we find that no spike assignment errors are committed. This result is robust to changes in neural firing rate, neural density, Gaussian noise, and increasing electrode density on the probes. However, for probes with electrode counts similar to those in commercial probes (10-50), only a handful of neurons are extracted. Exploring the space of probe designs, we find that designing probes with higher electrode density (for a fixed area) can compensate for this low yield. As the electrode density on the probe increases, the number of neurons extracted increases to some saturation. We also find that as the electrode density increases, we are able to extract neurons with spike peak magnitudes below the thresholding noise floor. Thus, the construction of very high density multielectrode arrays, coupled to the algorithm here proposed, may yield experimental approaches for recording very large numbers of neurons in the live brain, and automatically analyzing the resulting spike trains.

Dynamic-range analysis and maximization of micropower G m -C bandpass filters by adaptive biasing

We analyze and present an input gain-varying scheme for maximizing dynamic range in a well-known Gm-C bandpass filter by both minimizing noise for small input signals, and by achieving balanced swing levels at all filter nodes for large input signals. A micropower bandpass filter suitable for use in cochlear implants and other power-constrained biomedical applications was implemented and tested in subthreshold CMOS. At a center frequency of 1.4kHz and quality factor of 4, the filter has 70dB of dynamic range (57dB maximum SNR, 2.5% total harmonic distortion (THD)) and consumes 2.55µW of power.