Izi Stoll, Ph.D.
The Physical Basis of Consciousness
The goal of our first Grand Challenge was to devise a theoretical framework for understanding consciousness in accordance with physical laws – as a form of non-deterministic computation.
Izi Stoll tackled this Grand Challenge by applying mathematical toolkits from computational physics to model the membrane potential of cortical neurons. Classically, the neuron is viewed like a transistor, either firing or not firing, encoding Shannon entropy. This new approach involves viewing the cortical neuron membrane potential as the mixed sum of all component microstates, thereby encoding von Neumann entropy. So, rather than being considered a binary computational unit, always in an off-state or an on-state, the cortical neuron encodes the probability of shifting from an off-state to an on-state.
A combination of upstream inputs and random electrical noise converge to affect the cortical neuron membrane potential. Because cortical neurons tend to reside near action potential threshold, allowing stochastic events to gate signaling outcomes, the computational units themselves must be modeled probabilistically. Previous approaches have utilized statistical mechanics or random connectivity models to identify the likelihood of neuronal firing. In this model, a Hamiltonian operator represents the distribution of possible system states. Here, the encoding process physically generates information, or von Neumann entropy. Internal consistencies in the dataset then reduce that broad probability distribution into a single thermodynamically-favored system state. This time-dependent computational process effectively encodes the state of the local environment into the system. This iterative computational process yields both representative information content and non-deterministic outcomes for each computational unit.
Izi Stoll received bachelor’s degrees in Biology and Psychology from the University of Illinois in Urbana-Champaign, then received her doctorate in Neurobiology and Behavior from the University of Washington in Seattle. She applies a range of molecular-, cellular-, systems-level, and mathematical approaches to solve scientific problems.