I’m currently a post-doc at the Neuroscience Statistics Research Lab at MIT and am employed by Massachusetts General Hospital. I work under Emery N. Brown and Nancy Kopell in order to investigate how anesthesia enables loss of consciousness. I do this through the classic computational neuroscience method of simulating Hodgkin-Huxley neural networks, focusing on thalamocortical oscillations in particular. This allows us to non-invasively probe how the cell- and synapse-specific effects of anesthetics like propofol can change both your consciousness and your brain’s EEG, producing fascinating dynamics of interacting oscillations. These brain rhythms resemble sleep activity in some ways, but are very different in other ways!
I’m @asoplata on Twitter. Say hi if you’re here!
I’m also very interested in how these same thalamocortical networks contribute to memory consolidation in (mostly) sleep using oscillations of similar frequencies to those of anesthesia, via the co-occurrence of hippocampal sharp-wave ripples with thalamic spindles and thalamocortical slow wave oscillations. The neuroscience of memory is a very active field of research, but the role of sleep in memory has not been studied as much and is likely ripe for discoveries. There’s never enough time to investigate all the directions that are exciting!
I believe the future of science is open science, on a personal, ethical, and practical level. Frankly, though it may surprise you, computational neuroscience has some catching up to do in this department, though it is improving rapidly. I feel very strongly about open-sourcing all the tools, code, and parameters I use to perform this work.
Neural networks in the brain are complicated, messy, and diverse. Simulating them to uncover their fundamental mechanisms is fascinating, requiring both rigorous attention-to-detail and flexibility at the same time. For neuroscience, the vast majority of our work lies ahead of us, despite what it may seem like at times.