Jeff LaCoss

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.

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.

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.

A Novel Variable-Gain Micro-Power Band-Pass Auto-Zeroing CMOS Amplifier

A micropower, low-noise, bandpass amplifier for biomedical implants is presented. Operating at low frequency, the amplifier is fully integrated without any external passive components. Low-frequency noise and offset is reduced through the autozeroing technique. The autozeroing frequency and noise bandwidth is optimized to reduce noise folding. The design consists of a novel variable gain amplifier as the first stage, a low-Gm high-pass filter as the second stage, and a low-pass Gm-C amplifier as the last stage. Subthreshold operation is utilized in all input pair transistors to reduce power consumption, while a low-Gm OTA (operational transconductance amplifier) is realized with a current division technique. A cross-couple parallel pair of source degeneration transistors is utilized to increase the linearity crucial to neural spike detection. The design is realized in a CMOS 0.18mum process. It has an offset of 600muV, a variable gain from 42dB to 0dB, and 50 to 900Hz bandwidth while occupying 0.245mm2 area. The total circuit consumes only 26muW in a 1.8V power supply; the input referred noise is estimated to be 5.6muVrms.

Real time hardware neural spike amplitude extraction

A novel algorithm for population spike (PS) amplitude extraction suitable for real time hardware processing was developed. The extraction method was implemented digitally and experimentally tested on a field programmable gate array (FPGA) device using 16-bit quantization. The accuracy of the implementation was tested using PS signals recorded from hippocampal slices. The PS response of the dentate gyrus granule cells were generated in a multi-electrode array (MEA) setup. Spike amplitudes extracted in real time by the hardware were compared with values resulting from floating-point computation in software. Results showed successful implementation of hardware algorithm with average normalized mean square error (NMSE) less than 2%.

CMOS charge-metering microstimulator for implantable prosthetic device

Charge-metering microstimulator has been shown with higher charge accuracy, less charge-imbalance, and higher power efficiency than conventional current-mode and voltage-mode stimulators. In this paper, a discrete-time charge-metering microstimulator is proposed for implantable prosthetic device. Design techniques like half-clock sampling, high-voltage isolation, integration folding, and periodic discharging are proposed. Low power design is applied through system and function blocks. CMOS 0.18 um process was used with 1.8 V supply voltage and 50 uW power consumption.

A micro-power low-noise auto-zeroing CMOS amplifier for cortical neural prostheses

A novel architecture to realize a low-power, low-noise amplifier for cortical neural prostheses is presented. The design consists of a low-noise variable gain amplifier as the first stage, a low-Gm high-pass filter as the second stage, and a low-pass Gm-C amplifier as the last stage. Discrete-time autozeroing is utilized to reduce the offset and noise. The bandwidth and autozeroing frequency of the amplifier is optimized to reduce noise folding. A current division technique is utilized to achieve a low-Gm OTA (Operational Transconductance Amplifier) so that low frequency operation is realized without any external capacitors. All the input pair transistors are biased in sub-threshold operation to reduce power consumption. A cross-couple parallel pair of source degeneration transistors is employed to increase the linearity crucial to neural spike detection. This design achieves variable gain from 470 (55 dB) to 1. In a CMOS 0.18 um process with 1.8 V power supply, the total circuit occupies 0.245 mm2 with 26 uW power consumption and 1.8 kHz bandwidth. Total harmonic distortion is less than 1%, while input noise is 4.24 uVrms within the band of interest.

Reverse Engineering the Brain: A Hippocampal Cognitive Prosthesis for Repair and Enhancement of Memory Function

This chapter provides an update, including some of the most recent experimental and theoretical studies describing the development of a cognitive prosthesis designed to restore the ability to form new long-term memories—a memory capability lost after damage to the hippocampal formation and surrounding temporal lobe brain structures. This chapter also will describe recent studies demonstrating that the same device and procedures used to restore lost hippocampal memory function also can be used to enhance memory capability in otherwise normal animals. The animal model used in studying the loss and recovery of hippocampal memory is delayed nonmatch-to-sample (DNMS) behavior in the rat. 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 VLSI for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a comprehensive 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.