Retinal and Neural Dynamics using Memristor-CMOS Architectures

This thesis is dedicated to extensive studies on the application of emerging nanoscale technologies, namely ReRAM and memristive systems, to reconstructing the neural dynamics within the retina and the brain. In its present operative form, visual perception by way of prosthesis is limited to flashes of light and shapes of varying sizes, locations, and brightness. As such there is a need to understand the structure of the retina with respect to the connectivity between the various retinal cells in order to produce an appropriate model for the signal flow to cope with the significant computational resources needed to emulate a functional retina. By developing a neuromorphic vision chip that gives a spike-based response as inspired by the retinal ganglion cells, we are able to converge towards designing smart image sensors that operate at high resolutions with ultra-low power, much like the retina. Rather than relying on continuous analogue signals, or clock-based digital approaches, the work presented in this thesis developed a chip that functions similarly to the retina in that it only consumes power when there are changes in a scene, commonly referred to as event-driven sensors. Such a design can be used in computer vision and machine learning applications by significantly reducing the amount of information required to be processed in what is already a computationally expensive task, and with further work on interfacing, could be used in therapeutic healthcare by passing the sequence of spike signals through the visual cortex in enabling the brain to construct meaningful visual information which could potentially assist the visually impaired. The major original contributions reported in this thesis include:  Development of an end-to-end biophysically accurate model of the vertebrate retina in MATLAB;  Implementation of the model into a user-based program written in C++ to enable variation of light inputs, cell and junction electrical and chemical characteristics, and probing transient responses at any point within the full system;  Fast convergence to a solution by mapping the system of nonlinear ordinary differential equations (ODEs) into a system of nonlinear integral equations, accelerating retinal cell simulations by up to over 50% against conventional ODE solvers, enabling real-time performance of our simulator;  A model that simplifies the hardware mapping process as a result of using integrators rather than differentiators;  Conversion of the model into hardware, designing each individual cell circuit using memristorCMOS technology in the 180nm SKHynix process;  Design of an ultra-low power retinomorphic event-based image sensor by interconnecting the cell circuits above;  Advancement of core memristor theory in modelling and application;  Processing the retinomorphic chip response on a neural network accelerator, using optimisation techniques for in ReRAM crossbar arrays;  Characterise device bistability in memristive crossbar arrays;  Development of a frequency modulation scheme in improving parallelisation for processing feedforward neural networks on ReRAM crossbars;  Fabrication of a 10 × 10 titanium dioxide memristor crossbar array;  Crossbar cell design that merges read and write processes on stacked memristor crossbar arrays. The dominant trends towards using resistive switching in nanoscale devices (namely memristors) as a means to process and store information has provided a new opportunity for efficient processing of neural networks and other artificial intelligence-based algorithms in hardware, beyond Moore’s Law. By furthering our understanding of the likeness of concepts in neuroscience, algorithms inspired by neural processes, and devices, the work presented in this thesis drives towards application of these emerging technologies to the development of ultra-dense, fast and efficient learning mechanisms, as comparable to the biological brain.