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 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.