My advisors are Robert Malinow (Center for Neural Circuits) and Stephan Anagnostaras (Molecular Cognition Lab). I'm primarily interested in the molecular and neural network dynamics that underlie learning and memory, and study these processes using a mixture of neurobiological, computational modeling, and programming tools.
During the last US presidential election we were bombarded daily with crazy news stories on both Hillary and Donald; some of which came from news orgs I'd never heard of before. With so many news sources, all with their own agendas and political leanings, how was anyone to know whether each morning's new dirt on Clinton or Trump was truly damning or just another sensationalized report?
Initial idea: if a left-leaning news org published something negative about Clinton, it'd likely be a trustworthy news piece. Vice versa for Trump; if a conservative news source put out a negative report on Trump he must have done something egregious. The tricky part: how can one synthesize an unbiased value for how far left/right a news organization leans?
Solution: scrape the data from Reddit. Check out the blog post to see the results, and where your fav news org falls on the spectrum...
The ‘Hot Hand’ phenomenon is a popular belief that players (athletes/gamblers/etc.) who have just completed one-or-more successful attempts have increased odds of being successful in their next attempt. This notion is known as a ‘hot streak’ or ‘hot hand’. The statistical validity of this belief can be investigated using actual data.Tips for betting on hot-hands.
Demo on how to hack a KORG drum pad for psych experiments. The KORG padKONTROL has 16 velocity-sensitive (pressure-sensitive) touch pads, two twist knobs, an X-Y modulation touch pad, and various other affordance gizmos. This things is perfect for collecting participant data from any study were things like reaction time, stress level, stop-signal timing, etc can be the dependent variableDrumpad or cool Psych apparatus?
Currently, I'm examining the membrane diffusion of neurotransmitter receptors and modeling how these particles swarm and potentiate synapses.
To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state models.
Many forms of adaptive behavior engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory.
This project aims to provide annotated sets of molecular pathways involved in neural plasticity underlying learning and memory systems.