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.
The overarching goal of this proposal is to develop a combined experimental and computational platform for investigating dynamic patterns of activity in cortical ensembles. The research proposed here will address a set of fundamental and challenging problems in modern neuroscience that arose from the identification of state transitions in circuit activity, a phenomenon known as metastability. The proposed studies are designed to detect transient spatio-temporal dynamics in patterns of cortical activity, unveil the circuit architectures capable of generating them, and understand how they are shaped by plasticity. If successful, this study will provide an entirely novel framework for understanding neural circuit activity and its relationship to brain function. Our team of six PIs is especially well-suited to tackle these important problems successfully. We have complementary background and expertise in the experimental and computational approaches necessary to successfully accomplish this research program. The research we propose focuses on circuits in the superficial layers of the gustatory portion of the insular cortex, an area that represents a well-established model for investigating transient dynamics, especially those resulting from the sequential switching between metastable states that are known to be engaged in cognitive and perceptual tasks. Our long-term goal is to identify universal principles of collective network dynamics underlying behavior and experience- dependent learning during healthy life, aging, and disease. The results from the work proposed here will provide novel experimental and theoretical approaches that can be applied to the study of metastability in all cortical areas, and facilitate the investigation of the relevance of metastability for motor and/or cognitive tasks.
