Jack Wills

Research challenges and applications for underwater sensor networking

This paper explores applications and challenges for underwater sensor networks. We highlight potential applications to off-shore oilfields for seismic monitoring, equipment monitoring, and underwater robotics. We identify research directions in short-range acoustic communications, MAC, time synchronization, and localization protocols for high-latency acoustic networks, long-duration network sleeping, and application-level data scheduling. We describe our preliminary design on short-range acoustic communication hardware, and summarize results of high-latency time synchronization

Low-power acoustic modem for dense underwater sensor networks

Significant progress has been made in terrestrial sensor networks to revolutionize sensing and data collection. To bring the concept of long-lived, dense sensor networks to the underwater environment, there is a compelling need to develop low-cost and low-power acoustic modems for short-range communications. This paper presents our work in designing and developing such a modem. We describe our design rationale followed by details of both hardware and software development. We have performed preliminary tests with transducers for in-air communications.

Restoring lost cognitive function

A prosthetic device that functions in a biomimetic manner to replace information transmission between cortical brain regions is considered. In such a prosthesis, damaged CNS neurons is replaced with a biomimetic system comprised of silicon neurons. The replacement silicon neurons would have functional properties specific to those of the damaged neurons and would both receive as inputs and send as outputs electrical activity to regions of the brain with which the damaged region previously communicated. Thus, the class of prosthesis proposed is one that would replace the computational function of the damaged brain and restore the transmission of that computational result to other regions of the nervous system.

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals.

Implantable biomimetic microelectronic systems design

In this article, design examples will be presented for a biomimetic microelectronic system for a retinal prosthesis that electrically stimulates the retinal neurons. The system replaces the functionality of vision in blind patients affected by retinitis pigmentosa and age-related macular degeneration. The components and signal processing needed for a cortical prosthesis are described. Integration of all the components of a wireless biomimetic microelectronic system, such as input signal conditioning, power telemetry, data telemetry, stimulation amplifier and control circuitry (microstimulator), and a neural recording and processing device, into a single chip or a package is a tremendous challenge, requiring innovative approaches at both circuit and system levels and consideration of the multiple trade-offs between size, power consumption, flexibility in functionality, and reliability of the microelectronics. The chips described in this paper are prototypes for testing their implemented functionalities. The die sizes do not reflect the actual size of the implant. When the microelectronics are finally integrated, the circuits will be optimized to minimize the area. The use of submicron CMOS technology will also help reduce the die area. It should be noted that the biocompatible package encapsulating the electronics will increase the implant size.

The design and analysis of a RF CMOS bandpass filter

A high-Q filter capable of operating in the GHz range is proposed. This filter, which is suitable for RF applications, utilizes two effective Q-enhancement techniques to circumvent the low-Q characteristics inherent in the circuit. Simulation results employing standard 0.5 /spl mu/m CMOS technology have successfully verified that the center frequency tuning and hybrid Q-tuning techniques operate between 625 MHz and 1.68 GHz center frequencies with Q ranging from 12 to over 300. Two tunable bandpass filters with center frequencies at 876 MHz and 1.68 GHz have been designed to have 37 dB and 31.4 dB voltage gain, -30 dBm and -31 dBm IIP3, and 4.8 dB and 5.5 dB NF, respectively. Both filters have quality factors equal to 33 and power dissipation is equal to 24.3 mW.

Novel Charge-Metering Stimulus Amplifier for Biomimetic Implantable Prosthesis

A novel charge-metering stimulus amplifier is proposed for high performance neural prosthesis stimulation. The new charge-based approach with feedback has low charge error 0.5%, low charge imbalance 0.2%, and high power efficiency. Charge-metering stimulus amplifier can be implemented using advanced 0.18mum CMOS technology and is compatible with mixed-signal system-on-a-chip (SoC) implantable devices. It also has the capability to monitor the electrode-tissue contact quality and estimate the interface impedance in real-time for robust stimulation.

CMOS 12 bits 50kS/s micropower SAR and dual-slope hybrid ADC

In this paper a 12 bits 50kS/s micropower hybrid ADC is proposed for biomimetic microelectronic systems using 0.18um CMOS process. The hybrid ADC combines SAR and dual-slope architectures to achieve 12 bits, power consumption 60uW, and small silicon die size. This hybrid ADC shows very good figure-of-merits (FOM) on both power consumption and silicon die size compared with conventional low power SAR ADC. A fully differential GmC integrator is proposed for the dual-slope operation with low voltage discrete-time CMFB.

An algorithm for real-time extraction of population EPSP and population spike amplitudes from hippocampal field potential recordings

A new method is presented for extracting the amplitude of excitatory post synaptic potentials (EPSPs) and spikes in real time. It includes a low pass filter (LPF), a differentiator, a threshold function, and an intelligent integrator. It was applied to EPSP and population spike data recorded in the Dentate Gyrus and the CA1 hippocampus in vitro. The accuracy of the extraction algorithm was evaluated via the extraction normalized mean square error (eNMSE) and was found to be very high (eNMSE < 5%). The preservation of neuronal information was confirmed using the Volterra-Poisson modeling approach. Volterra-Poisson kernels were computed using amplitudes extracted with both proposed and traditional methods. The accuracy of the computed kernels and the resulting model was evaluated via the prediction normalized mean square error (pNMSE) and was found to be very high (pNMSE < 5%). The similarity between the kernels computed when the proposed method was used to extract the field potential amplitude and their counterparts when the traditional method was used to extract the field potential amplitude confirms the preservation of the neuronal dynamics. The proposed method represents a new class of real time field potential amplitude extraction algorithms with complexity that can be included in hardware implementations.

Role of the Hippocampus in Memory Formation : Restorative Encoding Memory Integration Neural Device As a Cognitive Neural Prosthesis

Remind, which stands for “restorative encoding memory integration neural device,” is a Defense Advanced Research Projects Agency (DARPA)-sponsored program to construct the first-ever cognitive prosthesis to replace lost memory function and enhance the existing memory capacity in animals and, ultimately, in humans. Reaching this goal involves understanding something fundamental about the brain that has not been understood previously: how the brain internally codes memories. In developing a hippocampal prosthesis for the rat, we have been able to demonstrate a multiple-input, multiple-output (MIMO) nonlinear model that predicts in real time the spatiotemporal codes for specific memories required for correct performance on a standard learning/memory task, i.e., delayed-nonmatch-to-sample (DNMS) memory. The MIMO model has been tested successfully in a number of contexts; most notably, in animals with a pharmacologically disabled hippocampus, we were able to reinstate long-term memories necessary for correct DNMS behavior by substituting a MIMO model-predicted code, delivered by electrical stimulation to the hippocampus through an array of electrodes, resulting in spatiotemporal hippocampal activity that is normally generated endogenously. We also have shown that delivering the same model-predicted code to electrode-implanted control animals with a normally functioning hippocampus substantially enhances animals memory capacity above control levels. These results in rodents have formed the basis for extending the MIMO model to nonhuman primates; this is now underway as the last step of the REMIND program before developing a MIMO-based cognitive prosthesis for humans.