Project Summary / Abstract
High-throughput experimental neuroscience has made it possible to observe behavior of many
animals, as well as a large groups of neurons simultaneously, providing an exciting opportunity for
figuring out how the neural system performs computations that underlie perception, cognition, and
behavior. However, there is a major bottleneck in the scientific cycle of data analysis and data
collection due to the complexity and scale of noisy, high-dimensional data. The primary objective of
this project is to develop tools for tracking the internal state of the brain that are not directly
measurable from both the behavior and neural signals, and to generate optimal stimulus
corresponding to the current brain state. These external stimuli can be used to perturb the animal’s
belief or strategy about the world such that the animal would behave differently.
Aim 1: Our team will develop a neural state tracking system that will parse out and display complex
neural signals recorded from the animal brain in real-time. The neural state tracking algorithm will
also extract the law that the neural system operates under, allowing neuroscientist to generate a new
class of hypotheses about the population level implementation underlying intelligent behavior.
Aim 2: To causally test hypothesis on how population of neurons compute and produce meaningful
behavior, it is necessary to be able to perturb the internal computation process. We will develop a
feedback control system to perturb the neural dynamics at a short time scale with a novel control
scheme for neural computation that respects the brain’s own degrees of freedom.
Aim 3: By understanding and tracking the time evolution of internal strategy throughout learning, we
can learn how to optimize the training of animal behavior. In this aim, we will develop statistical
models of learning and a computational system to generate the best stimuli based on the past
performance of the animal.
The statistical tools developed in this project will likely accelerate fundamental discoveries in
neuroscience. Clinically, this research can extend to monitoring, diagnosing, and building next-
generation real-time feedback stimulation devices for disorders with a neurodynamic or behavioral
component such as Parkinson’s disease, autism, learning disorders, obsessive compulsive disorder,
and epilepsy.