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Proceedings of the 38th International Conference on neural Information Processing Systems

作者： Yuki Minai Joana Soldado-Magraner Matthew A. Smith Byron M. Yu Neuroscience Institute Carnegie Mellon University and Machine Learning Department Carnegie Mellon University and Center for the Neural Basis of Cognition Neuroscience Institute Carnegie Mellon University and Department of Electrical and Computer Engineering Carnegie Mellon University and Center for the Neural Basis of Cognition Neuroscience Institute Carnegie Mellon University and Department of Biomedical Engineering Carnegie Mellon University and Center for the Neural Basis of Cognition Neuroscience Institute Carnegie Mellon University and Department of Biomedical Engineering Carnegie Mellon University and Department of Electrical and Computer Engineering Carnegie Mellon University and Center for the Neural Basis of Cognition

ISBN: (纸本)9798331314385

Brain stimulation has the potential to create desired neural population activity states. However, it is challenging to search the large space of stimulation parameters, for example, selecting which subset of electrodes to be used for stimulation. In this scenario, creating a model that maps the configuration of stimulation parameters to the brain's response can be beneficial. Training such an expansive model usually requires more stimulation-response samples than can be collected in a given experimental session. Furthermore, changes in the properties of the recorded activity over time can make it challenging to merge stimulation-response samples across sessions. To address these challenges, we propose MiSO (MicroStimulation Optimization), a closed-loop stimulation framework to drive neural population activity toward specified states by optimizing over a large stimulation parameter space. MiSO consists of three key components: 1) a neural activity alignment method to merge stimulation-response samples across sessions, 2) a statistical model trained on the merged samples to predict the brain's response to untested stimulation parameter configurations, and 3) an online optimization algorithm to adaptively update the stimulation parameter configuration based on the model's predictions. In this study, we implemented MiSO with a factor analysis (FA) based alignment method, a convolutional neural network (CNN), and an epsilon greedy optimization algorithm. We tested MiSO in closed-loop experiments using electrical microstimulation in the prefrontal cortex of a non-human primate. Guided by the CNN predictions, MiSO successfully searched amongst thousands of stimulation parameter configurations to drive the neural population activity toward specified states. More broadly, MiSO increases the clinical viability of neuromodulation technologies by enabling the use of many-fold larger stimulation parameter spaces.

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MiSO: Optimizing brain stimulation to create neural population activity states 38

MiSO: Optimizing brain stimulation to create neural populati...

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38th Conference on neural Information Processing Systems, NeurIPS 2024

作者： Minai, Yuki Soldado-Magraner, Joana Smith, Matthew A. Yu, Byron M. Neuroscience Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Biomedical Engineering Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Center for the Neural Basis of Cognition United States

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Fractional Tackles: Leveraging Player Tracking Data for Within-Play Tackling Evaluation in American Football

arXiv

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arXiv 2024年

作者： Nguyen, Quang Jiang, Ruitong Ellingwood, Meg Yurko, Ronald Department of Statistics & Data Science Neuroscience Institute Center for the Neural Basis of Cognition Carnegie Mellon University PittsburghPA15213 United States

Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a "contact window" of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier’s forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data. © 2024, CC BY.

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Manifold Transform by Recurrent Cortical Circuit Enhances Robust Encoding of Familiar Stimuli

arXiv

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arXiv 2024年

作者： Wang, Weifan Niu, Xueyan Lee, Tai-Sing Department of Biomedical Engineering Carnegie Mellon University United States Center for Neural Science New York University United States Center for Neural Basis of Cognition Carnegie Mellon University United States

A ubiquitous phenomenon observed throughout the primate hierarchical visual system is the sparsification of the neural representation of visual stimuli as a result of familiarization by repeated exposure, manifested as the sharpening of the population tuning curves and suppression of neural responses at the population level. In this work, we investigated the computational implications and circuit mechanisms underlying these neurophysiological observations in an early visual cortical circuit model. We found that such a recurrent neural circuit, shaped by BCM Hebbian learning, can also reproduce these phenomena. The resulting circuit became more robust against noises in encoding the familiar stimuli. Analysis of the geometry of the neural response manifold revealed that recurrent computation and familiar learning transform the response manifold and the neural dynamics, resulting in enhanced robustness against noise and better stimulus discrimination. This prediction is supported by preliminary physiological evidence. Familiarity training increases the alignment of the slow modes of network dynamics with the invariant features of the learned images. These findings revealed how these rapid plasticity mechanisms can improve contextual visual processing in even the early visual areas in the hierarchical visual system. © 2024, CC BY.

关键词： Recurrent neural networks

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Talker identification as a categorization problem 44

Talker identification as a categorization problem

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Roark, Casey L. Feng, Gangyi Chandrasekaran, Bharath Communication Science and Disorders University of Pittsburgh PittsburghPA15213 United States Center for the Neural Basis of Cognition PittsburghPA15213 United States Department of Linguistics and Modern Languages The Chinese University of Hong Kong Shatin N.T. Hong Kong Brain and Mind Institute The Chinese University of Hong Kong Shatin N.T. Hong Kong

Learning to identify a person's voice is a key component of speech perception. In this study, we use a categorization framework to provide insights about the mechanisms supporting talker identification. Native Mandarin Chinese listeners learned to categorize sentences in three tasks with different language contexts - native Mandarin talkers speaking Mandarin, native English talkers speaking English, and native Mandarin talkers speaking English. We compared learning when listeners received fully informative or minimal feedback. Using decision bound models, we examined the strategies participants used in each of the three tasks. Regardless of language context, full feedback was initially better for learning than minimal feedback but was no different after the second block. Across tasks, participants often used strategies based on mean fundamental frequency to separate the talkers. These results demonstrate that talker identification is a categorization problem, which enables leveraging existing category learning frameworks to understand the mechanisms of this important ability. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： categorization category learning talker identification

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Whose Signals Are Being Amplified? Toward a More Equitable Clinical Psychophysiology ( vol 12 , 10.1177/21677026221112117 , 2022)

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CLINICAL PSYCHOLOGICAL science 2024年第2期12卷 NP1-NP1页

作者： Bradford, D. E. DeFalco, A. Perkins, E. R. Carbajal, I Kwasa, J. Goodman, F. R. Jackson, F. Richardson, L. N. S. Woodley, N. Neuberger, L. Sandoval, J. A. Huang, H. J. Joyner, K. J. School of Psychological Science Oregon State University Department of Psychology Florida State University Center for the Neural Basis of Cognition Carnegie Mellon University Department of Psychological and Brain Sciences George Washington University Jackson Psychological Services LLC Atlanta GA Department of Mechanical and Aerospace Engineering University of Central Florida Pure Avidity Salon Orlando FL Nicholson School of Communication and Media University of Central Florida Department of Psychology University of California Berkeley

Research using psychophysiological methods holds great promise for refining clinical assessment, identifying risk factors, and informing treatment. Unfortunately, unique methodological features of existing approaches limit inclusive research participation and, consequently, generalizability. In this brief overview and commentary, we provide a snapshot of the current state of representation in clinical psychophysiology with a focus on the forms and consequences of ongoing exclusion of Black participants. We illustrate issues of inequity and exclusion that are unique to clinical psychophysiology and consider intersections among social constructions of Blackness and biased design of current technology used to measure electroencephalography, skin conductance, and other signals. We then highlight work by groups dedicated to quantifying and addressing these limitations. We discuss the need for reflection and input from a wider variety of affected individuals to develop and refine new technologies given the risk of further widening disparities. Finally, we provide broad recommendations for clinical-psychophysiology research.

关键词： electrophysiology minority groups prejudice psychophysiology racial and ethnic attitudes and relations

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Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

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Computational Brain and Behavior 2024年第4期7卷 588-607页

作者： Woensdregt, Marieke Fusaroli, Riccardo Rich, Patricia Modrák, Martin Kolokolova, Antonina Wright, Cory Warlaumont, Anne S. Department of Cognitive Science and Artificial Intelligence Radboud University Nijmegen Netherlands Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Language and Computation in Neural Systems Max Planck Institute for Psycholinguistics Nijmegen Netherlands Department of Linguistics Cognitive Science and Semiotics School of Communication and Culture Aarhus University Aarhus Denmark Interacting Minds Center School of Culture and Society Aarhus University Aarhus Denmark Linguistic Data Consortium University of Pennsylvania Philadelphia PA United States Department of Philosophy University of Bayreuth Bayreuth Germany Institute of Microbiology of the Czech Academy of Sciences Prague Czech Republic Second Faculty of Medicine Charles University Prague Czech Republic Department of Computer Science Memorial University of Newfoundland St. John’s NL Canada Department of Philosophy California State University Long Beach Long Beach CA United States Department of Communication University of California Los Angeles Los Angeles CA United States

In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities);in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains. © The Author(s) 2024.

关键词： Cognitive science Evidence Explanation Theoretical virtues Theory development Underdetermination

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A calcium imaging large dataset reveals novel functional organization in macaque V4

arXiv

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arXiv 2023年

作者： Wang, Tianye Yao, Haoxuan Lee, Tai Sing Hong, Jiayi Li, Yang Jiang, Hongfei Andolina, Ian Max Tang, Shiming Peking University School of Life Sciences Beijing China Peking‐Tsinghua Center for Life Sciences Beijing China IDG/McGovern Institute for Brain Research Peking University Beijing China Peking University Beijing China Center for the Neural Basis of Cognition and Computer Science Department Carnegie Mellon University Pittsburgh United States The Center for Excellence in Brain Science and Intelligence Technology State Key Laboratory of Neuroscience Key Laboratory of Primate Neurobiology Institute of Neuroscience Chinese Academy of Sciences Shanghai China

The topological organization and feature preferences of primate visual area V4 have been primarily studied using artificial stimuli. Here, we combined large‐scale calcium imaging with deep learning methods to characterize and understand how V4 processes natural images. By fitting a deep learning model to an unprecedentedly large dataset of columnar scale cortical responses to tens of thousands of natural stimuli and using the model to identify the images preferred by each cortical pixel, we obtained a detailed V4 topographical map of natural stimulus preference. The map contains distinct functional domains preferring a variety of natural image features, ranging from surface‐related features such as color and texture to shape‐related features such as edge, curvature, and facial features. These predicted domains were verified by additional widefield calcium imaging and single‐cell resolution two‐photon imaging. Our study reveals the systematic topological organization of V4 for encoding image features in natural scenes. © 2023, CC BY-NC-SA.

关键词： Large dataset

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The neural time course of size constancy in natural scenes

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Cortex 2025年 188卷 1-12页

作者： Yeh, Lu-Chun Gayet, Surya Kaiser, Daniel Peelen, Marius V. Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Neural Computation Group Department of Mathematics and Computer Science Physics Geography Justus Liebig University Gießen Germany Helmholtz Institute Experimental Psychology Utrecht University Utrecht Netherlands Germany Center for Mind Brain and Behavior (CMBB) Philipps University Marburg Justus Liebig University Gießen Technical University Darmstadt Germany

Accurate real-world size perception relies on size constancy, a mechanism that integrates an object's retinal size with distance information. The neural time course of extracting pictorial distance cues from scenes and integrating them with retinal size information - a process referred to as scene-based size constancy - remains unknown. In two experiments, participants viewed objects with either large or small retinal sizes, presented at near or far distances in outdoor scene photographs, while performing an unrelated one-back task. We applied multivariate pattern analysis (MVPA) to time-resolved EEG data to decode the retinal size of large versus small objects, depending on their distance (near versus far) in the scenes. The objects were either perceptually similar in size (large-near versus small-far) or perceptually dissimilar in size (large-far versus small-near), reflecting size constancy. We found that the retinal size of objects could be decoded from 80 ms after scene onset onwards. Distance information modulated size decoding at least 120 ms later: from 200 ms after scene onset when objects were fixated, and from 280 ms when objects were viewed in the periphery. These findings reveal the neural time course of size constancy based on pictorial distance cues in natural scenes. © 2025 The Author(s)

关键词： EEG MVPA Natural scenes Size constancy Size perception

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Neurophysiology of gaze orientation: Core neuronal networks

arXiv

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arXiv 2023年

作者： Goffart, Laurent Quinet, Julie Bourrelly, Clara Aix Marseille Université CNRS Institut de Neurosciences de la Timone Marseille France Aix Marseille Université CNRS Centre Gilles Gaston Granger Aix-en-Provence France Department of Optometry and Vision Science The University of Alabama at Birmingham Birmingham United States University of Pittsburgh Center for the Neural Basis of Cognition Pittsburgh United States

The appearance of an object triggers a shift of gaze toward its location. This orienting response consists of a rapid rotation of the eyes, the saccade, sometimes accompanied by a head rotation. In this chapter, instead of describing the path leading from the target-evoked retinal activity to the changes in muscle tension, we shall take the reverse path. Starting from the muscle contractions, we shall proceed upstream and describe the core neuronal networks in the brainstem and cerebellum that enable us to rapidly and accurately orient the foveae towards visual targets located at different eccentricities and depths. Copyright © 2023, The Authors. All rights reserved.

关键词： Eye movements

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