Il Memming Park, Ph.D. - US grants

Experimental neuroscientists can record the signals communicated among the neurons that are collectively involved in producing meaningful behaviors, but making sense of these patterns of activity in terms of specific mental functions is challenging. This research project aims to discover the unseen mental processes that underlie such meaningful behavior from those recordings. The technology developed in this endeavor will uncover new ways of understanding mental processes hidden deep in the noisy signals collected from multiple neurons and will be used to derive new theoretical models (cognitive algorithms) to explain how populations of neurons work together. Such models will contribute to the development of diagnostic tools and neural prosthetics for cognitive dysfunctions in perception, working memory, and decision making, and can also inspire advances in machine learning and artificial intelligence.

The technical goal of this project is to develop a data-driven framework amenable to visualization and interpretation of neural activity underlying cognition. The core of the project is the identification and recovery of an interpretable low-dimensional nonlinear continuous dynamical system that underlies observed neural time series, and its validation through experimental perturbations. This will answer two key scientific questions: (1) How are task and cognitive variables represented in low-dimensional neural trajectories; and (2) What are the laws that govern the time evolution of the neural states. Answering these questions will help us understand how subjects implement and switch between different cognitive strategies, and more importantly, will provide a means for testing previously proposed theoretical models of the neural computations underlying cognition. This project will develop a number of statistical methods that can (i) extract private and shared noise from single-trial electrophysiological observations, (ii) combine recordings from multiple sessions to infer a common cognitive neural dynamics model, and (iii) design control stimulation to perturb the current neural state. Specifically, these tools will be applied to recordings from cortical areas involved in visuomotor decision-making to discover (1) how the co-variability in a population of sensory neurons encodes decision variables, (2) how the cognitive strategy changes when sensory evidence statistics change, and (3) the underlying dynamics that sustain spatial working memory. The success of this project could transform how the field analyzes population activity with low-dimensional structure in the context of cognitive tasks and beyond.

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.

PROJECT SUMMARY Cortical circuits generate dynamic patterns of activity. One of the great challenges of modern neuroscience is to determine the circuit architectures that generate such dynamics patterns, and understand their genesis and functional significance. Most research on brain dynamics focused on stable patterns of activity showing continuous transitions (e.g., oscillations). However, in recent years there has been an increased interest on transient dynamics, including the ones resulting from the sequential switching between metastable states. Extracellular recordings of cortical ensembles indicated that sequences of metastable states, characterized by correlated changes in activity can be detected across subpopulations of neurons. Metastable states have been associated with specific cognitive or sensory variables, suggesting an important role for brain function. Metastability was also observed in the absence of any behavior or stimulation ? suggesting that metastable states may be generated locally and may reflect intrinsic architectures of cortical circuits. Despite evidence for their functional significance, little is known about metastable dynamics in cortical circuits. Indeed, lack of a coordinated and systematic approach to study both temporal and spatial signatures of these patterns has limited progress in this area. This proposal aims at developing an integrated experimental-computational platform for detecting metastable dynamics in cortical ensembles, inferring the circuit organizational principles underlying them, and understanding how plasticity affects metastability. Our team is formed by six PIs with complementary expertise in the experimental and computational approaches necessary to successfully accomplish this program. We will focus on circuits in the superficial layers of the gustatory portion of the insular cortex, a well-established model for understanding metastability. Our long-term goal is to generalize our findings to the study of transient dynamics in other cortical areas and understand their relevance for sensory, motor and/or cognitive tasks. Successfully accomplishing the proposed research will allow us to identify universal principles of collective network dynamics underlying behavior and experience-dependent learning.