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

> **NIH NIH U19** · STANFORD UNIVERSITY · 2022 · $60,000

## Abstract

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...

## Key facts

- **NIH application ID:** 10698364
- **Project number:** 3U19NS118284-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Karl A. Deisseroth
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $60,000
- **Award type:** 3
- **Project period:** 2021-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10698364

## Citation

> US National Institutes of Health, RePORTER application 10698364, Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior (3U19NS118284-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10698364. Licensed CC0.

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