Kate Hong

Associate Professor Carnegie Mellon University

  • Pittsburgh PA

Kate Hong's research is in understanding the organization and function of neural circuits that underlie sensory-guided behaviors.

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Carnegie Mellon University

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Biography

Kate Hong's research interests include systems neuroscience, characterization of neural circuits, diseases & disorders, sensation & perception, behavioral methods, computational, mathematical & statistical methods and physiological & anatomical methods. Her work combines animal behavior, high-speed imaging, motion tracking, in vivo electrophysiology and optogenetic methods to determine how cortical and subcortical activity cooperate to mediate (tactile) sensory-motor transformations in parallel, providing a foundation for understanding behavioral deficits and recovery mechanisms associated with cortical injury.

Areas of Expertise

Behavioral Methods
Characterization of Neural Circuits
Computational, Mathematical & Statistical Methods
Diseases & Disorders
Physiological & Anatomical Methods
Sensation & Perception
Systems Neuroscience

Media Appearances

Biology Professor Receives Grant for Autism Research

Mellon College of Science  online

2022-11-07

Kate Hong, an assistant professor of biological sciences and a member of Carnegie Mellon University's Neuroscience Institute, has received a Simons Foundation Autism Research Initiative (SFARI) grant for research into the interaction between sensory processing and decision-making in individuals with autism spectrum disorder (ASD).

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CMNI Welcomes Two New Faculty: Kate Hong and Matt Smith

Carnegie Mellon Neuroscience Institute  online

2019-05-13

The Carnegie Mellon Neuroscience Institute is excited to welcome two new faculty members on board: Kate Hong, who will join in January 2020 as an Assistant Professor jointly in CMNI and Biological Sciences; and Matt Smith, who will also join in January 2020 as an Associate Professor with tenure in CMNI and Biomedical Engineering.

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Media

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Accomplishments

Molecular Basis of Cognition Team Award

2022

Education

Harvard University

Ph.D.

Neurobiology

Brown University

Sc.B.

Biochemistry

Articles

Effects of arousal and movement on secondary somatosensory and visual thalamus

Elife

2021

Sensory information reaches the neocortex through multiple anatomical pathways in the thalamus. Prior work has disagreed on whether these encode parallel components of the same sensory signals or differ in how they mix sensory signals with information about behavioral state. Studying the somatosensory system in awake mice, the authors provide evidence supporting the second view. The authors find similar state dependent activity in a higher order visual thalamic nucleus. This is a timely study in that many have observed state-dependent activity throughout the cortex and thalamus, but the mechanisms of this activity are incompletely understood. This study brings us closer to revealing the source of this signal by ruling out major excitatory inputs including afferents carrying movement information, feedback from the cortex and inputs from the colliculus in the midbrain.

Sensorimotor strategies and neuronal representations for shape discrimination

Neuron

2021

Humans and other animals can identify objects by active touch, requiring the coordination of exploratory motion and tactile sensation. Both the motor strategies and neural representations employed could depend on the subject's goals. We developed a shape discrimination task that challenged head-fixed mice to discriminate concave from convex shapes. Behavioral decoding revealed that mice did this by comparing contacts across whiskers. In contrast, a separate group of mice performing a shape detection task simply summed up contacts over whiskers. We recorded populations of neurons in the barrel cortex, which processes whisker input, and found that individual neurons across the cortical layers encoded touch, whisker motion, and task-related signals.

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A rapid whisker-based decision underlying skilled locomotion in mice

Elife

2021

Skilled motor behavior requires rapidly integrating external sensory input with information about internal state to decide which movements to make next. Using machine learning approaches for high-resolution kinematic analysis, we uncover the logic of a rapid decision underlying sensory-guided locomotion in mice. After detecting obstacles with their whiskers mice select distinct kinematic strategies depending on a whisker-derived estimate of obstacle location together with the position and velocity of their body. Although mice rely on whiskers for obstacle avoidance, lesions of primary whisker sensory cortex had minimal impact. While motor cortex manipulations affected the execution of the chosen strategy, the decision-making process remained largely intact.

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