PhD Position – Co-Optimization and Prototyping of Biophysics-Inspired Algorithms and Circuits

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This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional computing architectures, replacing conventional structureless and deterministic LIF point-neuron models. This is pursued through circuit designs that exploit and control memristor dynamics (, local activity and stochasticity). For example, localized dendritic activation underlies numerous computational functions across hierarchical levels, such as denoising (filtering), increased expressivity (tunable local activation), multi-timescale adaptation (local memory), and stimulus-specific adaptation (multi-task processing). While the co-optimization of dendrite-inspired functional circuits with emerging memory devices has only recently been explored, this doctoral project aims to advance that frontier.


Initially, the research will explore CMOS–memristor hybrid implementations...