How does measurable brain activity become consciousness, emotion, and decision, and how much of it is chemistry?
I develop machine learning models for real-time neurochemical inference from electrochemical data acquired via intracranial electrodes in human patients. My current focus is to building deep learning architectures that generalize neurotransmitter concentration estimates across probes, labs, and recording conditions. The long term direction is to fuse neurochemical data with electrophysiology and behavior. Ultimately, this opens the door to investigating how neurotransmitter dynamics shape human phenomenology — emotion, subjective experience, and conscious state transitions that are difficult or impossible to model outside human subjects.
The neurochemistry is my latest approach to a question I've pursued for over a decade. While my earlier work focused on the mathematical formalization of the Integrated Information Theory (IIT) of consciousness and neural markers of awareness, a critical gap remains: physically grounding these abstract structures in the wet, dynamical systems of the brain. Decoding sub-second neuromodulator dynamics provides a direct path to test how chemical state transitions drive conscious experience.
Deep learning models (InceptionTime, meta-encoders) that decode sub-second monoamine dynamics — dopamine, serotonin, norepinephrine — from electrochemical recordings in human patients. The goal is probe-invariant, lab-invariant inference that works across electrode types and recording conditions, moving toward multimodal integration of neurochemical, electrophysiological, and behavioral data.
Mathematical foundations of Integrated Information Theory. Uniqueness proofs for intrinsic information measures using functional equations and Minkowski-type inequalities. Earlier work on Bayesian source localization of neural signals, the role of predictive coding and evidence accumulation during unconscious processing, and neural markers of perceptual awareness during sleep and in infants.
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