Neuromagnetic Fields and Brain-Inspired Hybrid Analog-Digital Computation

Nicolelis, Miguel A L

Henriquez, Craig S

Subramanian, Vivek Anand

2019-04-02T16:27:36Z

2019-07-09T08:17:09Z

2018

Biomedical Engineering

Brain-inspired computing architectures such as neural networks and neuromorphic chips have demonstrated promise in performing complex pattern recognition tasks by coarsely mimicking synaptic activity in software and hardware. In this dissertation, we take a departure from these more traditional methods which are confined by what we know about the dynamics of synaptic computation and introduce a brain-inspired hybrid analog-digital computing paradigm involving magnetic fields. We first review biomagnetic fields - a wide array of topics is covered to spark the interest of the reader in the field of neuro-biomagnetism and to provide a general overview of the field that explains (1) various techniques to measure, quantify, and model the magnetic signals generated by neurons; (2) how magnetic stimulation can affect neurons; and (3) the clinical relevance of these findings. These highlight the importance of magnetism in biology and neural signal processing and provide motivation for engineering magnetically-based computational devices. We then introduce a new hybrid analog-digital computing device inspired by the interplay between neural activity and its induced magnetic fields. We show that magnetic fields can interact nonlinearly in analog in a ferromagnetic medium. Specifically, the magnetic flux induced by two alternating magnetic fields can be employed to perform an absolute difference, or smooth XOR, operation. The physical structure of the analog device is based on a white matter tractography analysis; hence, we call it the neuromagnetic reactor. We also describe our design of a scalable implementation of a perceptron in hardware, which provides a digital 0-1 output. We demonstrate in a synthetic environment that these two systems together allow an organism to learn from and react appropriately to its environment. Although the design presented here is a proof-of-concept, it can be improved to yield not only new ways to study brain function but also new brain-inspired computing architectures based on magnetic fields.

https://hdl.handle.net/10161/18264

Computer engineering

Biomedical engineering

Artificial intelligence

analog

biomagnetism

Digital

hybrid computing

magnetic field

perceptron

Neuromagnetic Fields and Brain-Inspired Hybrid Analog-Digital Computation

Dissertation

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