Summary/Abstract
Animals constantly make decisions, such as how to evaluate a potential threat or where to look for food. Yet the
same animal in the same environment can produce different decisions on different occasions, because its
internal state interacts powerfully with external inputs to determine behavior. This proposal’s overarching goal
is to understand how internal states influence decisions and to identify the underlying neural mechanisms. In a
mouse decision-making task, these experiments will examine the effects of three types of internal state changes:
those arising spontaneously with engagement and disengagement in a task, those resulting from changing
expectations during the task, and those resulting from learning within and across days. To determine how internal
states affect brain activity and behavior, the team will apply cutting-edge technical advances on a brainwide
scale, including statistical tools to infer internal states from behavior; simultaneous recordings from large
populations of neurons across many regions during behavior and during optogenetic perturbations; assays that
map functionally and molecularly defined cell-type-specific, cross-region connectivity; and computational
approaches to model how cross-region neural communication depends on internal states.
These ambitious goals go beyond the capabilities of an individual laboratory and are ideally suited for an already-
productive consortium. This team is part of the International Brain Laboratory, which has already developed a
standardized mouse decision-making task and standardized methods for training, neural measurement, and data
analysis, along with a working, scalable infrastructure for sharing data. The proposed research leverages this
existing infrastructure and takes it in a new direction. Projects 1-5 will examine simultaneously recorded
population activity, evaluate causality, study neural activity and behavior during learning in normal and autism
model mice, identify cell types by measuring neuronal activity, gene expression, and axonal projection patterns
in the same populations of neurons, and build a comprehensive computational model of all these experimental
results. Cores A-D will support the collection, replicability, management, and analysis of the large datasets
produced by this brainwide examination of neural circuits.
Taken together, the proposed research will rigorously define the neural basis of multiple internal states and
evaluate their impact on the flow of decision-relevant information through the brain. The results will greatly
advance the field by generating a comprehensive, mechanistic understanding of how internal states are reflected
in the brain, and how these states interact with external inputs to guide decisions. Moreover, the team will
produce and disseminate open-source tools and protocols that will enable other laboratories to collect and
manage large-scale datasets produced through brainwide measurements.