Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior

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Overall - Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior Project Summary A central challenge in neuroscience involves understanding how assemblies of cortical neurons, comprised of different cell types and inhabiting different layers, work together to generate coherent dynamical internal states, that then interact with external sensory inputs to generate state-dependent behaviors on a moment-by-moment basis. Key impediments to meeting this foundational challenge include lack of adequate technological and computational tools to monitor, control, identify and model neural state dynamics emerging from cortical cell assemblies spanning multiple cortical cell-types and layers. We propose to develop an unprecedented confluence of technology and computation to achieve such capabilities by building on our team’s significant prior work. In particular, our combined technology and computation platform will enable us to: (1) perform volumetric imaging of thousands of cortical cells during behavior to collect both relevant spatiotemporal activity patterns and 3D positioning; (2) simultaneously write arbitrary spatiotemporal patterns into tens to hundreds of individually identified cells at millisecond temporal resolution using 2-photon multiSLM methods; and (3) using hydrogel tissue-chemistry and single-cell sequencing methods, obtain deep molecular cell-type information in the same neurons that were both measured and controlled during behavior. This unprecedented simultaneous read/write/cell-typing technology will be tightly integrated with computational methods that can: (1) employ state of the art systems identification methods to identify and extract neural states and the dynamical laws governing their interactions with external inputs; and (2) amongst the astronomical number of possible spatiotemporal stimulation patterns, predict interesting ones that can best refine models, yield conceptual insights, and yield the capacity for optimal control of cortical circuit dynamics, with potential clinical relevance. This combined technology and computation will empower next-generation experiments that allow us to learn the dynamical language (in terms of state space dynamics) of cortical circuits, play back modified versions of this language for both insight and control, and understand how this language emerges from the concerted activity of multiple cell-types across layers. Our technology/computation platform will be validated in multiple experiments across species and brain regions, guided by deep and long-standing theories of internal state dynamics in computational neuroscience. Throughout, new methods will be collaboratively validated in the diverse preparations of our experimental labs (such cross-cutting interactions are shown in blue text). In particular we will focus on testing theories underlying several foundational classes of neural computation: (1) ability of sensory networks to generate accurate percepts by detecting and amplifying weak sensory inputs amidst spontaneous background activity; (2) Bayesian integration of multisensory inputs to convert sensorimotor experiences into internal estimates of external state variables and their uncertainty; and (3) triggering and maintenance of discrete internal attractor states capable of controlling stable behavior.