We aim to replicate traits of biological olfaction by combining mixed-signal Very Large Scale Implementation (VLSI) of massively parallel and ultra-low-power spiking neural networks with synthetic biological components. Our research aims to establish an integrated platform for chemical sensing, enabling the exploration and understanding of biological olfaction's sensing and computing aspects.
Job DescriptionIn the human brain, sensory neurons relay crucial information about our surroundings, including light, touch, sounds, taste, and smell. Through the cutting-edge fields of neuromorphic computing and synthetic biology, we aim to endow artificial systems with a highly accurate, robust, and efficient capability for detecting scents, or olfaction.
Biological systems' ability to smell surpasses conventional chemical detection methods in several key aspects, including sensitivity, specificity, reaction times, encoding capacity, durability, compactness, and energy efficiency. This superior performance is largely attributed to the sophisticated design of the olfactory system, which has been refined through millions of years of evolution across all living creatures, from the smallest insects to the largest mammals. These biological systems rely on membrane proteins equipped with specialised channels that can identify specific odour molecules, leveraging an incredibly effective and flexible computing platform provided by spiking neural networks.
Thus, the main broad research questions are:
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How can we engineer advanced spiking neural networks and leverage VLSI (Very Large Scale Integration) technologies to mirror the complex structure of the biological olfactory system in artificial devices? By focusing on the integration of membrane proteins with specialised channels and incorporating cutting-edge spiking neural networks capable of online learning, we aim to achieve a precise, adaptable, and efficient artificial olfactory system for recognizing odour molecules'To answer these questions, we seek highly motivated, self-driven Ph.D. research that will contribute to the SYNCH project 'Combining SYnthetic Biology & Neuromorphic Computing for CHemosensory perception'. SYNCH will be conducted in the
Neuromorphic Edge Computing Systems Lab, within the Electronic Systems Group (ES) at TU/e. The SYNCH project is a collaborative endeavour involving CAU Kiel University in Germany and the University of Bern in Switzerland.
The Ph.D. research will focus on exploring new neuromorphic microelectronic circuits in VLSI technology, and we will closely collaborate with synthetic biological experts. The research will delve into crucial aspects of computational properties in biological neural systems and the final goal is to create a unified chemical sensing platform by combining neuromorphic electronic systems with synthetic biological mediums—a pioneering endeavour.