CNBC Calendar

University of Pittsburgh Department of Neuroscience: Special Seminars as part of Faculty Search.

Dr. Alan Emanuel, Harvard University

“Contributions of Peripheral Mechanoreceptor Subtypes to the Cortical Representation of Touch”


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CMU Neural Engineering Seminar 

When: Monday, November 29, 2021, 3-4PM ET

Where: Join Zoom Meeting https://cmu.zoom.us/j/92714209784?pwd=VUJkeVZZQUZQT2lxMUtGQVVJcnM1Zz09

Decoding local and global cognitive signals from neuronal populations

Matt Smith, Ph.D.

Associate Professor of Biomedical Engineering and the Neuroscience Institute, Carnegie Mellon University

Abstract: Neural activity observed within a brain area may reflect local computations within the area, inputs from another area, or shared computations/inputs across many areas. Ideally, these distinct mechanisms could be studied separately. However, because multiple processes can influence groups of neurons, it is not obvious how to separate the neural signals that should be attributed to each process. We investigated the different behavioral roles of neural variability shared across hemispheres and neural variability local to each hemisphere. To do this, we applied dimensionality reduction methods to bilateral prefrontal cortex (PFC) array recordings. We were able to identify latent variables representing activity shared across hemispheres, as well as latent variables representing activity local to each hemisphere. We found that variability shared across hemispheres was dominated by a process that slowly evolved across trials and was highly correlated with trial-to-trial fluctuations in mean pupil diameter – a potential neural correlate of fluctuations in arousal. By decoding this signal from neuronal population activity, we were able to predict a constellation of aspects of the animal’s task and eye movement behavior. Overall, our work demonstrates how distributed cognitive processes and states can be hidden in subtle shifts in the responsivity of individual neurons, but accessed and decoded from simultaneously recorded populations of neurons.

About the Speaker: Matt Smith (B.S. Canisius College, Ph.D. New York University) is an Associate Professor of Biomedical Engineering and the Neuroscience Institute. His laboratory studies visual perception, cognition, and decisions. They use a combination of electrophysiological and imaging techniques, combined with computational approaches, to gain insight into how populations of neurons work together to support perception and action.

University of Pittsburgh Department of Neuroscience: Special Seminars as part of Faculty Search.

Dr. Adam Kohn, Albert Einstein College of Medicine

“Corticocortical communication”


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Title: Neural variability: structure, sources, control, and data augmentation

Abstract: Variability is an important aspect of neural systems, both in the brain and in artificial networks. In the brain, neurons respond differently from trial to trial, even to repeated presentations of the exact same stimulus and this variability is often correlated across neurons. Previous work has posited that shared trial-to-trial variability (i.e., correlated neuronal variability) is behaviorally relevant and could have important implications for computations and information encoding. In the first three sections of this thesis, I present work to further the understanding of shared variability in the brain. To better understand the structure of shared variability, we related pairwise neuronal correlations to population dimensionality reduction methods. To investigate volitional control of shared variability in non-motor brain areas, we designed a brain computer interface for prefrontal cortex. Finally, to elucidate sources of variability, we developed a method called pCCA-FA to partition local (i.e., single brain area) and global (i.e., brain-wide) factors that contribute to shared variability. Variability also plays an important role in learning, in both the brain and in artificial neural networks (i.e., deep learning). Data augmentation increases the size, quality, and variability of datasets for improved training of deep learning models. In the final section, we empirically evaluated how different augmentation setups perform for different model architectures for image classification. We introduced a new augmentation, called StyleAug, which outperforms other state-of-the-art augmentations for training vision transformers (ViTs).

Committee:

Matthew Smith (Advisor, CMU)
Byron Yu (Advisor, CMU)
Rob Kass (CMU)
Bruno Averbeck (NIH/NIMH)
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Meeting ID: 960 7080 6108
Passcode: 676978

University of Pittsburgh Department of Neuroscience: Special Seminars as part of Faculty Search

Dr. Laura Colgin, The University of Texas at Austin

“The Relationship Between Hippocampal Place Cell Ensemble Coding and Spatial Memory Performance”


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University of Pittsburgh Department of Neuroscience: Special Seminars as part of Faculty Search.

Dr. Matthew Colonnese, the George Washington University

“The secret life of the fetal brain: Short lived circuits and the strange rules of developing networks”


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