Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts’ law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasks—a spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)—with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts’ law), path efficiency (PE), and completion time (CT). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R2= 0.57, and R2= 0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R2= 0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individual's tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts’ law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.

To achieve the full potential of neural prostheses, such as those used for the treatment of epilepsy, some means to close the loop around the electrical stimulation protocol has to be provided. To be able to close the loop, it is necessary to record the effect of stimulation itself on the surrounding tissue. Such recording is made impossible by the presence of a long-lasting stimulation artifact, which is due to the presence of residual charge in the electrode. Custom electronics, implementing a novel feedback scheme, can reduce or eliminate such charge and, by making the electronics scalable, a large number of electrodes can be simultaneously accessed. In this work we present such an artifact elimination system.

This paper presents an asynchronous VLSI architecture for modeling the oscillatory patterns seen in segmented biological systems. The architecture emulates the intersegmental synaptic connectivity observed in these biological systems. The communications network uses address-event representation (AER), a common neuromorphic protocol for data transmission. The asynchronous circuits are synthesized using communicating hardware processes (CHP) procedures. The architecture is scalable, supports multichip communication, and operates independent of the type of silicon neuron (spiking or burst envelopes). A 16-segment prototype system was developed, tested, and implemented; data from this system are presented.

Biological systems are able to perform movements in unpredictable environments more elegantly than traditionally engineered robotic systems. A current limitation of robotic systems is their inability to simultaneously and independently control both joint angle and joint stiffness without electromechanical feedback loops, which can reduce system stability. In this paper, we describe the development and physical implementation of a servo-actuated robotic joint that uses antagonistic, series-elastic actuation with novel nonlinear spring mechanisms. These mechanisms form a real-time mechanical feedback loop that provides the joint with angle and stiffness control through differential and common-mode actuation of the servos, respectively. This approach to joint control emulates the mechanics of antagonistic muscle groups used by animals, and we experimentally show that it is capable of independently controlling both joint angle and joint stiffness using a simple open-loop control algorithm.

Nonlinear antennas combine advances in nonlinear dynamics, active antenna design, and analog microelectronics to generate beam steering and beam forming across an array of nonlinear oscillators. Nonlinear antennas exploit two phenomena typically shunned in traditional designs: nonlinear unit cells and interelement coupling. The design stems from nonlinear coupled differential equation analysis that by virtue of the dynamic control is far less complex than the linear counterparts by eliminating the need for phase shifters and beam forming computers. These advantages arise from incorporating nonlinear dynamics rather than limiting the system to linear quasisteady state operation. A theoretical framework describing beam shaping and beam forming by exploiting the phase, amplitude, and coupling dynamics of nonlinear oscillator arrays is presented. Experimental demonstration of nonlinear beam steering is realized using analog microelectronics.

The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip non-volatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and pFET hot-electron injection to program values. With these designs, we achieve greater than 13-bit output precision with a 39 dB power supply rejection ratio and no crosstalk between memory cells.

We have developed analog VLSI circuits to model the behavior demonstrated by biological sarcomeres, the force generating components of muscle tissue. The circuits are based upon the mathematical description of crossbridge populations developed by A.F. Huxley. We have implemented the sarcomere circuit using a standard 1.2 /spl mu/ process, and have demonstrated transient and steady-state behaviors experimentally.

We present a novel architecture for detecting spatiotemporal synchrony among pulse trains. This is useful for performing meaningful computation in a system where analog information is conveyed via the precise timing of spikes in addition to the mean spike rate-without explicitly leaving the spike domain. Using a resistive network to restrict the spatial extent of the window of meaningful synchrony, and an array of pseudo-AFGAs to restrict the temporal extent of the window, we are able to perform the synchrony detection task in continous time, without sampling. We have implemented this architecture using only 10 transistors per spatial channel, and have fabricated three 32-node linear arrays using a 1.2 /spl mu/ CMOS process. We present experimental results.

We developed an analog very large-scale integrated system of two mutually inhibitory silicon neurons that display several different stable oscillations. For example, oscillations can be synchronous with weak inhibitory coupling and alternating with relatively strong inhibitory coupling. All oscillations observed experimentally were predicted by bifurcation analysis of a corresponding mathematical model. The synchronous oscillations do not require special synaptic properties and are apparently robust enough to survive the variability and constraints inherent in this physical system. In biological experiments with oscillatory neuronal networks, blockade of inhibitory synaptic coupling can sometimes lead to synchronous oscillations. An example of this phenomenon is the transition from alternating to synchronous bursting in the swimming central pattern generator of lamprey when synaptic inhibition is blocked by strychnine. Our results suggest a simple explanation for the observed oscillatory transitions in the lamprey central pattern generator network: that inhibitory connectivity alone is sufficient to produce the observed transition.

An optoelectronic processor that should allow the implementation of multiresolution Gabor filtering at TV frame rates is described. The system takes the form of a TV camera with modified optics and some simple processing incorporated in the focal-plane detector array. The output of the camera is an array of images, each produced by putting the input image through an angularly-oriented Gabor-type spatial frequency filter and converting the output to baseband. Key elements of the system include: (a) bandpass spatial filtering by pupil modification of an incoherent imaging system, (b) separation of spatial lowpass and spatial bandpass structures by temporal modulation of the bandpass distribution, (c) detection of the temporally modulated distribution by VLSI circuitry in the focal-plane detector array, and (d) multiplexing of multiple spatial bandpass channels in a single smart pixel array.

An analog circuit that performs a loser-take-all computation is presented. This circuit takes an array of analog input current and produces an array of binary output voltages/currents. The circuit functions such that the output at the node with the minimum input current is a non-zero constant while all of the other outputs are equal to zero. This circuit can also be used to compute the global minimum of the input array. Theory of operation for this circuit is developed and compared with experimental results.

Circuitry that detects the runner-up or second highest input value in an array of inputs is presented. We are able to detect the runner-up by inhibiting the winner in a winner-take-all circuit. In another runner-up circuit designed to alleviate competition from the cells surrounding the winner, this inhibition is extended to include both the winner and its nearest neighbors.

A two-dimensional analog VLSI array that performs basic morphological image processing operations is presented. The system uses a smart pixel approach that facilitates the parallel computation of continuous real-time outputs. Photodetectors within the array of smart pixels also allow for parallel optical inputs. The processing is performed by current-mode circuitry implemented with CMOS technology. The signal values are encoded as analog current values. The processing array uses local communication to perform the morphological operation known as dilation, and outputs the difference between the original input image and the dilation image to detect edges. The system was fabricated using a standard 2.0 /spl mu/m digital CMOS process. Example output data of the fabricated circuitry are presented.>

Circuitry that performs primitive attention tasks is presented. With the addition of feedback to a winner-take-all circuit we are able to mediate the selection of a single location within a sensory array through the use of hysteresis.>

A simple servo controller built from components having neuronlike features is described. This VLSI servo controller uses pulses for control and is orders of magnitude smaller than a conventional system. The basic circuit elements are described. A key element is a component and neuronlike capability that takes voltages as inputs and generates a pulse train as the output. It is shown how the circuits are combined to a proportional and derivative controller. The advantages of using a pulsed output representation to improve slow-speed operation of a friction-limited system is demonstrated. The utility of exploiting parallelism, aggregation, and redundancy to improve system-level performance given imprecise low-level components is discussed. Experimental results illustrate the properties of the system compared with conventional controllers.