Eric Yttri
Assistant Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Eric Yttri's research goal is to establish how neural circuits lead to these action selection decisions.
Biography
The selection of actions is central to how we interact with the world, a reality that is often not fully appreciated until this ability is lost through impairments like stroke, Parkinson's Disease and OCD. The goal of Eric Yttri's research is to establish how neural circuits lead to these action selection decisions. The vital ability to make appropriate actions requires the coordination of motor, reward and cognitive brain systems. While compelling research has been accomplished in individual brain areas, studying elements of neuronal circuits in isolation yields an incomplete and potentially misleading picture. His research approach is inclusive yet specific: interrogating the functional interactions between areas in a manner more typical of cognitive neuroscience (e.g., fMRI) while also identifying the computational contributions of individual cell types within each region. His work uses electrophysiological, behavioral and computational tools to build upon the distributed action execution model, delineating a specific role for each individual cell in the motor system.
Areas of Expertise (4)
Motor Coordination
Cognitive Brain Function
Neural Circuits
Neuroscience
Media Appearances (5)
Carnegie Mellon University Hosts Interdisciplinary AI Conference
India Education Diary online
2023-03-04
“It was fascinating to talk to all of the outside speakers that are asking very different questions and using very different models,” Yttri said. “Despite some people looking at proteins, RNA or neuroscience, the methods and thought processes we all use are remarkably similar.”
Gov. Wolf’s Health Department Mandates Masks For Schools, Child Care Facilities
90.5 WESA online
2021-09-01
As part of 90.5 WESA’s Good Question, Kid! Series, Eric Yttri, assistant professor of biological sciences and neuroscience researcher at Carnegie Mellon University, explains why songs get stuck in our heads.
New Algorithm to Revolutionize the Study of Behavior
Carnegie Mellon University online
2021-09-01
Yttri said B-SOiD provides a huge improvement and opens up several avenues for new research. "It removes user bias and, more importantly, removes the time cost and arduous work," he said. "We can accurately process hours of data in a matter of minutes."
Machine learning algorithm revolutionizes how scientists study behavior
Medical Xpress online
2021-08-31
To Eric Yttri, assistant professor of biological sciences and Neuroscience Institute faculty at Carnegie Mellon University, the best way to understand the brain is to watch how organisms interact with the world. "Behavior drives everything we do," Yttri said.
The weird Upside Down science behind ‘Stranger Things’
CNN online
2019-07-04
In the lab, they can also create a kind of mind control. It’s not unlike the way the Mindflayer controls Will. “In neuroscience, we have a much less sinister but similar notion of that control,” Yttri said. “We can record neurons and essentially thoughts in the brain, read those out, decode them and then encode them into actions.”
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Industry Expertise (1)
Advanced Medical Equipment
Education (2)
Washington University: Ph.D., Neuroscience
College of William and Mary: B.S., Neuroscience
Links (3)
Articles (4)
Mapping the neuroethological signatures of pain, analgesia, and recovery in mice
Neuron2023 Ongoing pain is driven by the activation and modulation of pain-sensing neurons, affecting physiology, motor function, and motivation to engage in certain behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in non-verbal animals. Here, in mice, we used site-specific electrophysiology to define key time points corresponding to peripheral sensitivity in acute paw inflammation and chronic knee pain models. Using supervised and unsupervised machine learning tools, we uncovered sensory-evoked coping postures unique to each model. Through 3D pose analytics, we identified movement sequences that robustly represent different pain states and found that commonly used analgesics do not return an animal's behavior to a pre-injury state.
207. Dorsal Striatal Indirect Pathway Neurons Mediate Response Inhibition to Uncertain Cues
Biological Psychiatry2023 Background The dorsomedial striatum (DMS) is critical for both response inhibition and value-based decision making. Here we assess how the DMS mediates both functions simultaneously. Methods Mice (n= 10; 5 female) expressing either channelrhodopsin-2 (ChR2) in either direct or indirect pathway medium spiny neurons (dMSNs or iMSNs) were trained to perform an auditory conditioning task with three cue that predicted reward on 0%, 50% or 100% of trials. To manipulate activity, dMSNs or iMSNs were stimulated during the cue period. In a separate cohort of animals (n= 3; 1 female), MSN activity was recorded using Neuropixel probes, and single units were identified using Kilosort and Phy software.
Open-source tools for behavioral video analysis: Setup, methods, and best practices
Elife2023 Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional ‘center of mass’ tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording.
A-SOiD, an active learning platform for expert-guided, data efficient discovery of behavior
bioRxiv2022 Behavior identification and quantification techniques have undergone rapid development. To this end, supervised or unsupervised methods are chosen based upon their intrinsic strengths and weaknesses (e.g. user bias, training cost, complexity, action discovery). Here, a new active learning platform, A-SOiD, blends these strengths and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data while attaining expansive classification through directed unsupervised classification. In socially-interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated two additional ethologically-distinct mouse interactions via unsupervised classification.
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