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Groh JM, Schmehl MN, Caruso VC, Tokdar ST. Signal switching may enhance processing power of the brain. Trends Cogn Sci 2024; 28:600-613. [PMID: 38763804 DOI: 10.1016/j.tics.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/21/2024]
Abstract
Our ability to perceive multiple objects is mysterious. Sensory neurons are broadly tuned, producing potential overlap in the populations of neurons activated by each object in a scene. This overlap raises questions about how distinct information is retained about each item. We present a novel signal switching theory of neural representation, which posits that neural signals may interleave representations of individual items across time. Evidence for this theory comes from new statistical tools that overcome the limitations inherent to standard time-and-trial-pooled assessments of neural signals. Our theory has implications for diverse domains of neuroscience, including attention, figure binding/scene segregation, oscillations, and divisive normalization. The general concept of switching between functions could also lend explanatory power to theories of grounded cognition.
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Affiliation(s)
- Jennifer M Groh
- Department of Psychology and Neuroscience, Duke University, Durham, NC, 27705, USA; Department of Neurobiology, Duke University, Durham, NC, 27705, USA; Department of Biomedical Engineering, Duke University, Durham, NC, 27705, USA; Department of Computer Science, Duke University, Durham, NC, 27705, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, 27705, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, 27705, USA.
| | - Meredith N Schmehl
- Department of Neurobiology, Duke University, Durham, NC, 27705, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, 27705, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, 27705, USA
| | - Valeria C Caruso
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Surya T Tokdar
- Department of Statistical Science, Duke University, Durham, NC, 27705, USA
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Kent RD. The Feel of Speech: Multisystem and Polymodal Somatosensation in Speech Production. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024; 67:1424-1460. [PMID: 38593006 DOI: 10.1044/2024_jslhr-23-00575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE The oral structures such as the tongue and lips have remarkable somatosensory capacities, but understanding the roles of somatosensation in speech production requires a more comprehensive knowledge of somatosensation in the speech production system in its entirety, including the respiratory, laryngeal, and supralaryngeal subsystems. This review was conducted to summarize the system-wide somatosensory information available for speech production. METHOD The search was conducted with PubMed/Medline and Google Scholar for articles published until November 2023. Numerous search terms were used in conducting the review, which covered the topics of psychophysics, basic and clinical behavioral research, neuroanatomy, and neuroscience. RESULTS AND CONCLUSIONS The current understanding of speech somatosensation rests primarily on the two pillars of psychophysics and neuroscience. The confluence of polymodal afferent streams supports the development, maintenance, and refinement of speech production. Receptors are both canonical and noncanonical, with the latter occurring especially in the muscles innervated by the facial nerve. Somatosensory representation in the cortex is disproportionately large and provides for sensory interactions. Speech somatosensory function is robust over the lifespan, with possible declines in advanced aging. The understanding of somatosensation in speech disorders is largely disconnected from research and theory on speech production. A speech somatoscape is proposed as the generalized, system-wide sensation of speech production, with implications for speech development, speech motor control, and speech disorders.
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Wei 魏赣超 G, Tajik Mansouri زینب تاجیک منصوری Z, Wang 王晓婧 X, Stevenson IH. Calibrating Bayesian Decoders of Neural Spiking Activity. J Neurosci 2024; 44:e2158232024. [PMID: 38538143 PMCID: PMC11063820 DOI: 10.1523/jneurosci.2158-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 05/03/2024] Open
Abstract
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.
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Affiliation(s)
- Ganchao Wei 魏赣超
- Department of Statistical Science, Duke University, Durham, North Carolina 27708
| | | | | | - Ian H Stevenson
- Departments of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
- Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Connecticut Institute for Brain and Cognitive Science, University of Connecticut, Storrs, Connecticut 06269
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Greenidge CD, Scholl B, Yates JL, Pillow JW. Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression. Neural Comput 2024; 36:175-226. [PMID: 38101329 DOI: 10.1162/neco_a_01630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 08/09/2022] [Indexed: 12/17/2023]
Abstract
Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the gaussian process multiclass decoder (GPMD), is well suited to decoding a continuous low-dimensional variable from high-dimensional population activity and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in data sets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three data sets and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.
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Affiliation(s)
| | - Benjamin Scholl
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, U.S.A.
| | - Jacob L Yates
- University of California, Berkeley, School of Optometry, Berkeley, CA 94720, U.S.A.
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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Gong Z, Zhou M, Dai Y, Wen Y, Liu Y, Zhen Z. A large-scale fMRI dataset for the visual processing of naturalistic scenes. Sci Data 2023; 10:559. [PMID: 37612327 PMCID: PMC10447576 DOI: 10.1038/s41597-023-02471-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.
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Affiliation(s)
- Zhengxin Gong
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Ming Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yuxuan Dai
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Yushan Wen
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Youyi Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Zonglei Zhen
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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