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

Brown University

Sc.B.

Biochemistry

Harvard University

Ph.D.

Neurobiology

Articles

Primary somatosensory cortex is essential for texture discrimination but not object detection in mice

IBRO Reports

2019

The sense of touch is a fundamental part of our sensory experience, yet our understanding of the underlying neural circuitry is limited. For example, the primary somatosensory cortex (S1) has long been assumed to play a crucial role in tactile processing. Yet, surprising results from our lab has recently shown that S1 is completely dispensable for a whisker-based go/no-go object detection task in mice. We therefore asked whether finer discrimination of tactile stimuli require the greater computational power of the cortex. In order to test this, we developed a two-alternative forced choice (2AFC) paradigm in which head-fixed mice are trained to either (1) detect objects or (2) discriminate between two textures using only their whiskers.

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Deep and superficial layers of the primary somatosensory cortex are critical for whisker-based texture discrimination in mice

BioRxiv

2020

The neocortex, comprised of multiple distinct layers, processes sensory input from the periphery, makes decisions, and executes actions. Despite extensive investigation of cortical anatomy and physiology, the contributions of different cortical layers to sensory guided behaviors remain unknown. Here, we developed a two-alternative forced choice (2AFC) paradigm in which head-fixed mice use a single whisker to either discriminate textures of parametrically varied roughness or detect the same textured surfaces. Lesioning the barrel cortex revealed that 2AFC texture discrimination, but not detection, was cortex-dependent. Paralyzing the whisker pad had little effect on performance, demonstrating that passive can rival active perception and cortical dependence is not movement-related.

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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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