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Weng G, Clark K, Akbarian A, Noudoost B, Nategh N. Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas. Front Comput Neurosci 2024; 18:1273053. [PMID: 38348287 PMCID: PMC10859875 DOI: 10.3389/fncom.2024.1273053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/09/2024] [Indexed: 02/15/2024] Open
Abstract
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
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Affiliation(s)
- Geyu Weng
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Kelsey Clark
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Amir Akbarian
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Behrad Noudoost
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
| | - Neda Nategh
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States
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Kessler M, Mamach M, Beutelmann R, Bankstahl JP, Bengel FM, Klump GM, Berding G. Activation in the auditory pathway of the gerbil studied with 18F-FDG PET: effects of anesthesia. Brain Struct Funct 2018; 223:4293-4305. [PMID: 30203305 DOI: 10.1007/s00429-018-1743-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 08/29/2018] [Indexed: 01/20/2023]
Abstract
Here, we present results from an 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) study in the Mongolian gerbil, a preferred animal model in auditory research. One major issue in preclinical nuclear imaging, as well as in most of the neurophysiological methods investigating auditory processing, is the need of anesthesia. We compared the usability of two types of anesthesia which are frequently employed in electrophysiology, ketamine/xylazine (KX), and fentanyl/midazolam/medetomidine (FMM), for valid measurements of auditory activation with 18F-FDG PET. Gerbils were placed in a sound-shielding box and injected with 18F-FDG. Two acoustic free-field conditions were used: (1) baseline (no stimulation, 25 dB background noise) and (2) 90 dB frequency-modulated tones (FM). After 40 min of 18F-FDG uptake, a 30 min acquisition was performed using a small animal PET/CT system. Blood glucose levels were measured after the uptake phase before scanning. Standardized uptake value ratios for relevant regions were determined after implementing image and volume of interest templates. Scans demonstrated a significantly higher uptake in the inferior colliculus with FM stimulation compared to baseline in awake subjects (+ 12%; p = 0.02) and with FMM anesthesia (+ 13%; p = 0.0012), but not with KX anesthesia. In non-auditory brain regions, no significant difference was detected. Blood glucose levels were significantly higher under KX compared to FMM anesthesia (17.29 ± 0.42 mmol/l vs. 14.30 ± 1.91 mmol/l; p = 0.024). These results suggest that valid 18F-FDG PET measurements of auditory activation comparable to electrophysiology can be obtained from gerbils during opioid-based anesthesia due to its limited effects on interfering blood glucose levels.
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Affiliation(s)
- M Kessler
- Department of Nuclear Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.,Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - M Mamach
- Department of Nuclear Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.,Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.,Department of Medical Physics and Radiation Protection, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - R Beutelmann
- Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.,Division for animal Physiology and Behaviour Group, Department for Neuroscience, School of Medicine and Health Sciences, University of Oldenburg, Carl von Ossietzky Str. 9-11, 26129, Oldenburg, Germany
| | - J P Bankstahl
- Department of Nuclear Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - F M Bengel
- Department of Nuclear Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - G M Klump
- Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.,Division for animal Physiology and Behaviour Group, Department for Neuroscience, School of Medicine and Health Sciences, University of Oldenburg, Carl von Ossietzky Str. 9-11, 26129, Oldenburg, Germany
| | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany. .,Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.
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Holdgraf CR, Rieger JW, Micheli C, Martin S, Knight RT, Theunissen FE. Encoding and Decoding Models in Cognitive Electrophysiology. Front Syst Neurosci 2017; 11:61. [PMID: 29018336 PMCID: PMC5623038 DOI: 10.3389/fnsys.2017.00061] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.
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Affiliation(s)
- Christopher R. Holdgraf
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Office of the Vice Chancellor for Research, Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, United States
| | - Jochem W. Rieger
- Department of Psychology, Carl-von-Ossietzky University, Oldenburg, Germany
| | - Cristiano Micheli
- Department of Psychology, Carl-von-Ossietzky University, Oldenburg, Germany
- Institut des Sciences Cognitives Marc Jeannerod, Lyon, France
| | - Stephanie Martin
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Robert T. Knight
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Frederic E. Theunissen
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
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Bach JH, Kollmeier B, Anemüller J. Matching Pursuit Analysis of Auditory Receptive Fields' Spectro-Temporal Properties. Front Syst Neurosci 2017; 11:4. [PMID: 28232791 PMCID: PMC5299023 DOI: 10.3389/fnsys.2017.00004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 01/23/2017] [Indexed: 11/13/2022] Open
Abstract
Gabor filters have long been proposed as models for spectro-temporal receptive fields (STRFs), with their specific spectral and temporal rate of modulation qualitatively replicating characteristics of STRF filters estimated from responses to auditory stimuli in physiological data. The present study builds on the Gabor-STRF model by proposing a methodology to quantitatively decompose STRFs into a set of optimally matched Gabor filters through matching pursuit, and by quantitatively evaluating spectral and temporal characteristics of STRFs in terms of the derived optimal Gabor-parameters. To summarize a neuron's spectro-temporal characteristics, we introduce a measure for the “diagonality,” i.e., the extent to which an STRF exhibits spectro-temporal transients which cannot be factorized into a product of a spectral and a temporal modulation. With this methodology, it is shown that approximately half of 52 analyzed zebra finch STRFs can each be well approximated by a single Gabor or a linear combination of two Gabor filters. Moreover, the dominant Gabor functions tend to be oriented either in the spectral or in the temporal direction, with truly “diagonal” Gabor functions rarely being necessary for reconstruction of an STRF's main characteristics. As a toy example for the applicability of STRF and Gabor-STRF filters to auditory detection tasks, we use STRF filters as features in an automatic event detection task and compare them to idealized Gabor filters and mel-frequency cepstral coefficients (MFCCs). STRFs classify a set of six everyday sounds with an accuracy similar to reference Gabor features (94% recognition rate). Spectro-temporal STRF and Gabor features outperform reference spectral MFCCs in quiet and in low noise conditions (down to 0 dB signal to noise ratio).
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Affiliation(s)
- Jörg-Hendrik Bach
- Medizinische Physik, Universität OldenburgOldenburg, Germany
- Cluster of Excellence Hearing4all, Universität OldenburgOldenburg, Germany
| | - Birger Kollmeier
- Medizinische Physik, Universität OldenburgOldenburg, Germany
- Cluster of Excellence Hearing4all, Universität OldenburgOldenburg, Germany
| | - Jörn Anemüller
- Medizinische Physik, Universität OldenburgOldenburg, Germany
- Cluster of Excellence Hearing4all, Universität OldenburgOldenburg, Germany
- *Correspondence: Jörn Anemüller
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Meyer AF, Williamson RS, Linden JF, Sahani M. Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation. Front Syst Neurosci 2017; 10:109. [PMID: 28127278 PMCID: PMC5226961 DOI: 10.3389/fnsys.2016.00109] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 12/19/2016] [Indexed: 11/13/2022] Open
Abstract
Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available.
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Affiliation(s)
- Arne F Meyer
- Gatsby Computational Neuroscience Unit, University College London London, UK
| | - Ross S Williamson
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear InfirmaryBoston, MA, USA; Department of Otology and Laryngology, Harvard Medical SchoolBoston, MA, USA
| | - Jennifer F Linden
- Ear Institute, University College LondonLondon, UK; Department of Neuroscience, Physiology and Pharmacology, University College LondonLondon, UK
| | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London London, UK
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Yassin L, Pecka M, Kajopoulos J, Gleiss H, Li L, Leibold C, Felmy F. Differences in synaptic and intrinsic properties result in topographic heterogeneity of temporal processing of neurons within the inferior colliculus. Hear Res 2016; 341:79-90. [DOI: 10.1016/j.heares.2016.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 08/15/2016] [Accepted: 08/16/2016] [Indexed: 10/21/2022]
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Westö J, May PJC. Capturing contextual effects in spectro-temporal receptive fields. Hear Res 2016; 339:195-210. [PMID: 27473504 DOI: 10.1016/j.heares.2016.07.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 06/16/2016] [Accepted: 07/24/2016] [Indexed: 11/25/2022]
Abstract
Spectro-temporal receptive fields (STRFs) are thought to provide descriptive images of the computations performed by neurons along the auditory pathway. However, their validity can be questioned because they rely on a set of assumptions that are probably not fulfilled by real neurons exhibiting contextual effects, that is, nonlinear interactions in the time or frequency dimension that cannot be described with a linear filter. We used a novel approach to investigate how a variety of contextual effects, due to facilitating nonlinear interactions and synaptic depression, affect different STRF models, and if these effects can be captured with a context field (CF). Contextual effects were incorporated in simulated networks of spiking neurons, allowing one to define the true STRFs of the neurons. This, in turn, made it possible to evaluate the performance of each STRF model by comparing the estimations with the true STRFs. We found that currently used STRF models are particularly poor at estimating inhibitory regions. Specifically, contextual effects make estimated STRFs dependent on stimulus density in a contrasting fashion: inhibitory regions are underestimated at lower densities while artificial inhibitory regions emerge at higher densities. The CF was found to provide a solution to this dilemma, but only when it is used together with a generalized linear model. Our results therefore highlight the limitations of the traditional STRF approach and provide useful recipes for how different STRF models and stimuli can be used to arrive at reliable quantifications of neural computations in the presence of contextual effects. The results therefore push the purpose of STRF analysis from simply finding an optimal stimulus toward describing context-dependent computations of neurons along the auditory pathway.
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Affiliation(s)
- Johan Westö
- Department of Neuroscience and Biomedical Engineering, Aalto University, FI-00076 Espoo, Finland.
| | - Patrick J C May
- Special Laboratory Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany.
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Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations. J Neurosci Methods 2015; 246:119-33. [PMID: 25744059 DOI: 10.1016/j.jneumeth.2015.02.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Revised: 01/22/2015] [Accepted: 02/11/2015] [Indexed: 11/23/2022]
Abstract
BACKGROUND The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. NEW METHOD Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. RESULTS AND COMPARISON WITH EXISTING METHOD Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. CONCLUSIONS The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves.
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