Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics

Abstract

Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease. There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated. The original contributions of this dissertation include:

A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time;

Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection;

Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials;

A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning.