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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:S1364-6613(24)00103-7. [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] [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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2
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Schmehl MN, Caruso VC, Chen Y, Jun NY, Willett SM, Mohl JT, Ruff DA, Cohen M, Ebihara AF, Freiwald WA, Tokdar ST, Groh JM. Multiple objects evoke fluctuating responses in several regions of the visual pathway. eLife 2024; 13:e91129. [PMID: 38489224 PMCID: PMC10942787 DOI: 10.7554/elife.91129] [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: 08/01/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here, we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action.
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Affiliation(s)
- Meredith N Schmehl
- Department of Neurobiology, Duke UniversityDurhamUnited States
- Center for Cognitive Neuroscience, Duke UniversityDurhamUnited States
- Duke Institute for Brain Sciences, Duke UniversityDurhamUnited States
| | - Valeria C Caruso
- Department of Psychiatry, University of MichiganAnn ArborUnited States
| | - Yunran Chen
- Department of Statistical Science, Duke UniversityDurhamUnited States
| | - Na Young Jun
- Department of Neurobiology, Duke UniversityDurhamUnited States
- Duke Institute for Brain Sciences, Duke UniversityDurhamUnited States
| | - Shawn M Willett
- Department of Ophthalmology, University of PittsburghPittsburghUnited States
| | - Jeff T Mohl
- American Medical Group AssociationAlexandriaUnited States
| | - Douglas A Ruff
- Department of Neurobiology, University of ChicagoChicagoUnited States
| | - Marlene Cohen
- Department of Neurobiology, University of ChicagoChicagoUnited States
| | | | | | - Surya T Tokdar
- Duke Institute for Brain Sciences, Duke UniversityDurhamUnited States
- Department of Statistical Science, Duke UniversityDurhamUnited States
| | - Jennifer M Groh
- Department of Neurobiology, Duke UniversityDurhamUnited States
- Center for Cognitive Neuroscience, Duke UniversityDurhamUnited States
- Duke Institute for Brain Sciences, Duke UniversityDurhamUnited States
- Department of Psychology & Neuroscience, Duke UniversityDurhamUnited States
- Department of Computer Science, Duke UniversityDurhamUnited States
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
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3
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Aboutorabi E, Baloni Ray S, Kaping D, Shahbazi F, Treue S, Esghaei M. Phase of neural oscillations as a reference frame for attention-based routing in visual cortex. Prog Neurobiol 2024; 233:102563. [PMID: 38142770 DOI: 10.1016/j.pneurobio.2023.102563] [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: 06/21/2023] [Revised: 12/04/2023] [Accepted: 12/20/2023] [Indexed: 12/26/2023]
Abstract
Selective attention allows the brain to efficiently process the image projected onto the retina, selectively focusing neural processing resources on behaviorally relevant visual information. While previous studies have documented the crucial role of the action potential rate of single neurons in relaying such information, little is known about how the activity of single neurons relative to their neighboring network contributes to the efficient representation of attended stimuli and transmission of this information to downstream areas. Here, we show in the dorsal visual pathway of monkeys (medial superior temporal area) that neurons fire spikes preferentially at a specific phase of the ongoing population beta (∼20 Hz) oscillations of the surrounding local network. This preferred spiking phase shifts towards a later phase when monkeys selectively attend towards (rather than away from) the receptive field of the neuron. This shift of the locking phase is positively correlated with the speed at which animals report a visual change. Furthermore, our computational modeling suggests that neural networks can manipulate the preferred phase of coupling by imposing differential synaptic delays on postsynaptic potentials. This distinction between the locking phase of neurons activated by the spatially attended stimulus vs. that of neurons activated by the unattended stimulus, may enable the neural system to discriminate relevant from irrelevant sensory inputs and consequently filter out distracting stimuli information by aligning the spikes which convey relevant/irrelevant information to distinct phases linked to periods of better/worse perceptual sensitivity for higher cortices. This strategy may be used to reserve the narrow windows of highest perceptual efficacy to the processing of the most behaviorally relevant information, ensuring highly efficient responses to attended sensory events.
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Affiliation(s)
- Ehsan Aboutorabi
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Robarts Research Institute, Western University, London, Ontario, Canada
| | - Sonia Baloni Ray
- Indraprastha Institute of Information Technology, New Delhi, India
| | - Daniel Kaping
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Farhad Shahbazi
- Department of Physics, Isfahan University of Technology, Isfahan, Iran
| | - Stefan Treue
- Cognitive Neuroscience Laboratory, German Primate Center - Leibniz Institute for Primate Research, Goettingen, Germany; Faculty for Biology and Psychology, University of Goettingen, Germany; Leibniz ScienceCampus Primate Cognition, Goettingen, Germany
| | - Moein Esghaei
- Cognitive Neuroscience Laboratory, German Primate Center - Leibniz Institute for Primate Research, Goettingen, Germany; Westa Higher Education Center, Karaj, Iran.
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4
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Schmehl MN, Caruso VC, Chen Y, Jun NY, Willett SM, Mohl JT, Ruff DA, Cohen M, Ebihara AF, Freiwald W, Tokdar ST, Groh JM. Multiple objects evoke fluctuating responses in several regions of the visual pathway. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549668. [PMID: 37502939 PMCID: PMC10370052 DOI: 10.1101/2023.07.19.549668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (for example, visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time. However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action. Impact Statement We find neural fluctuations in multiple areas along the visual cortical hierarchy that could allow the brain to represent distinct co-occurring visual stimuli.
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5
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Zajzon B, Dahmen D, Morrison A, Duarte R. Signal denoising through topographic modularity of neural circuits. eLife 2023; 12:77009. [PMID: 36700545 PMCID: PMC9981157 DOI: 10.7554/elife.77009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 01/25/2023] [Indexed: 01/27/2023] Open
Abstract
Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally relevant operating regimes, and provide an in-depth theoretical analysis unraveling the dynamical principles underlying the mechanism.
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Affiliation(s)
- Barna Zajzon
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research CentreJülichGermany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen UniversityAachenGermany
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research CentreJülichGermany
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research CentreJülichGermany
- Department of Computer Science 3 - Software Engineering, RWTH Aachen UniversityAachenGermany
| | - Renato Duarte
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-BRAIN Institute I, Jülich Research CentreJülichGermany
- Donders Institute for Brain, Cognition and Behavior, Radboud University NijmegenNijmegenNetherlands
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6
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Bill J, Gershman SJ, Drugowitsch J. Visual motion perception as online hierarchical inference. Nat Commun 2022; 13:7403. [PMID: 36456546 PMCID: PMC9715570 DOI: 10.1038/s41467-022-34805-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/07/2022] [Indexed: 12/03/2022] Open
Abstract
Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.
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Affiliation(s)
- Johannes Bill
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA. .,Department of Psychology, Harvard University, Cambridge, MA, USA.
| | - Samuel J Gershman
- Department of Psychology, Harvard University, Cambridge, MA, USA.,Center for Brain Science, Harvard University, Cambridge, MA, USA.,Center for Brains, Minds, and Machines, MIT, Cambridge, MA, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.,Center for Brain Science, Harvard University, Cambridge, MA, USA
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7
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Testing models at the neural level reveals how the brain computes subjective value. Proc Natl Acad Sci U S A 2021; 118:2106237118. [PMID: 34686596 PMCID: PMC8639327 DOI: 10.1073/pnas.2106237118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2021] [Indexed: 12/03/2022] Open
Abstract
In recent years, models have played an increasingly important role for understanding the brain in cognitive, behavioral, and systems neuroscience. Decision neuroscience in particular has benefitted greatly from the application of economic models of choice preferences to neural data. However, an often-overlooked aspect is that many models of preferences have a generic problem—they make extremely similar behavioral predictions. Here, we demonstrate that to understand the mechanisms of valuation in the brain, it is useful to compare models of choice preferences not only at the behavioral but also at the neural level. Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.
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8
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Human visual motion perception shows hallmarks of Bayesian structural inference. Sci Rep 2021; 11:3714. [PMID: 33580096 PMCID: PMC7881251 DOI: 10.1038/s41598-021-82175-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 01/13/2021] [Indexed: 11/08/2022] Open
Abstract
Motion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene's motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task's Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence-especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.
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9
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Søltoft-Jensen A, Heide-Jørgensen MP, Ditlevsen S. Modelling the sound production of narwhals using a point process framework with memory effects. Ann Appl Stat 2020. [DOI: 10.1214/20-aoas1379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Mohl JT, Caruso VC, Tokdar ST, Groh JM. Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals. NEURONS, BEHAVIOR, DATA ANALYSIS AND THEORY 2020; 3:https://nbdt.scholasticahq.com/article/11880-sensitivity-and-specificity-of-a-bayesian-single-trial-analysis-for-time-varying-neural-signals. [PMID: 34505116 PMCID: PMC8425354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
We recently reported the existence of fluctuations in neural signals that may permit neurons to code multiple simultaneous stimuli sequentially across time [1]. This required deploying a novel statistical approach to permit investigation of neural activity at the scale of individual trials. Here we present tests using synthetic data to assess the sensitivity and specificity of this analysis. We fabricated datasets to match each of several potential response patterns derived from single-stimulus response distributions. In particular, we simulated dual stimulus trial spike counts that reflected fluctuating mixtures of the single stimulus spike counts, stable intermediate averages, single stimulus winner-take-all, or response distributions that were outside the range defined by the single stimulus responses (such as summation or suppression). We then assessed how well the analysis recovered the correct response pattern as a function of the number of simulated trials and the difference between the simulated responses to each "stimulus" alone. We found excellent recovery of the mixture, intermediate, and outside categories (>97% correct), and good recovery of the single/winner-take-all category (>90% correct) when the number of trials was >20 and the single-stimulus response rates were 50Hz and 20Hz respectively. Both larger numbers of trials and greater separation between the single stimulus firing rates improved categorization accuracy. These results provide a benchmark, and guidelines for data collection, for use of this method to investigate coding of multiple items at the individual-trial time scale.
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Affiliation(s)
- Jeff T. Mohl
- Duke Institute for Brain Sciences, Duke University, Durham, NC, 27708, USA,Center for Cognitive Neuroscience, Duke University,Department of Neurobiology, Duke University
| | - Valeria C. Caruso
- Duke Institute for Brain Sciences, Duke University, Durham, NC, 27708, USA,Center for Cognitive Neuroscience, Duke University,Department of Neurobiology, Duke University,Department of Psychology and Neuroscience, Duke University,Center for Human Growth and Development, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Surya T. Tokdar
- Duke Institute for Brain Sciences, Duke University, Durham, NC, 27708, USA,Department of Statistical Science, Duke University
| | - Jennifer M. Groh
- Duke Institute for Brain Sciences, Duke University, Durham, NC, 27708, USA,Center for Cognitive Neuroscience, Duke University,Department of Neurobiology, Duke University,Department of Psychology and Neuroscience, Duke University
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11
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Li K, Kadohisa M, Kusunoki M, Duncan J, Bundesen C, Ditlevsen S. Distinguishing between parallel and serial processing in visual attention from neurobiological data. ROYAL SOCIETY OPEN SCIENCE 2020; 7:191553. [PMID: 32218974 PMCID: PMC7029944 DOI: 10.1098/rsos.191553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 11/22/2019] [Indexed: 06/10/2023]
Abstract
Serial and parallel processing in visual search have been long debated in psychology, but the processing mechanism remains an open issue. Serial processing allows only one object at a time to be processed, whereas parallel processing assumes that various objects are processed simultaneously. Here, we present novel neural models for the two types of processing mechanisms based on analysis of simultaneously recorded spike trains using electrophysiological data from prefrontal cortex of rhesus monkeys while processing task-relevant visual displays. We combine mathematical models describing neuronal attention and point process models for spike trains. The same model can explain both serial and parallel processing by adopting different parameter regimes. We present statistical methods to distinguish between serial and parallel processing based on both maximum likelihood estimates and decoding the momentary focus of attention when two stimuli are presented simultaneously. Results show that both processing mechanisms are in play for the simultaneously recorded neurons, but neurons tend to follow parallel processing in the beginning after the onset of the stimulus pair, whereas they tend to serial processing later on.
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Affiliation(s)
- Kang Li
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Mikiko Kadohisa
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Makoto Kusunoki
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - John Duncan
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Claus Bundesen
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Susanne Ditlevsen
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
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12
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Kozyrev V, Daliri MR, Schwedhelm P, Treue S. Strategic deployment of feature-based attentional gain in primate visual cortex. PLoS Biol 2019; 17:e3000387. [PMID: 31386656 PMCID: PMC6684042 DOI: 10.1371/journal.pbio.3000387] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 07/02/2019] [Indexed: 11/18/2022] Open
Abstract
Attending to visual stimuli enhances the gain of those neurons in primate visual cortex that preferentially respond to the matching locations and features (on-target gain). Although this is well suited to enhance the neuronal representation of attended stimuli, it is nonoptimal under difficult discrimination conditions, as in the presence of similar distractors. In such cases, directing attention to neighboring neuronal populations (off-target gain) has been shown to be the most efficient strategy, but although such a strategic deployment of attention has been shown behaviorally, its underlying neural mechanisms are unknown. Here, we investigated how attention affects the population responses of neurons in the middle temporal (MT) visual area of rhesus monkeys to bidirectional movement inside the neurons' receptive field (RF). The monkeys were trained to focus their attention onto the fixation spot or to detect a direction or speed change in one of the motion directions (the "target"), ignoring the distractor motion. Population activity profiles were determined by systematically varying the patterns' directions while maintaining a constant angle between them. As expected, the response profiles show a peak for each of the 2 motion directions. Switching spatial attention from the fixation spot into the RF enhanced the peak representing the attended stimulus and suppressed the distractor representation. Importantly, the population data show a direction-dependent attentional modulation that does not peak at the target feature but rather along the slopes of the activity profile representing the target direction. Our results show that attentional gains are strategically deployed to optimize the discriminability of target stimuli, in line with an optimal gain mechanism proposed by Navalpakkam and Itti.
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Affiliation(s)
- Vladislav Kozyrev
- Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Goettingen, Germany.,Bernstein Center for Computational Neuroscience, Goettingen, Germany.,Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP), University Medical Center Goettingen, Germany.,Department of Cognitive Neurology, University Medical Center Goettingen, Germany
| | - Mohammad Reza Daliri
- Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Goettingen, Germany.,Bernstein Center for Computational Neuroscience, Goettingen, Germany.,Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, Iran.,Cognitive Neurobiology Lab., School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Niavaran, Tehran, Iran
| | - Philipp Schwedhelm
- Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Goettingen, Germany.,Center for Mind and Brain Sciences, University of Trento, Italy.,Institute of Molecular and Clinical Ophthalmology Basel (IOB), Switzerland.,Functional Imaging Laboratory, German Primate Center-Leibniz Institute for Primate Research, Goettingen, Germany
| | - Stefan Treue
- Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Goettingen, Germany.,Bernstein Center for Computational Neuroscience, Goettingen, Germany.,Leibniz ScienceCampus PrimateCognition, Goettingen, Germany.,Faculty of Biology and Psychology, University of Goettingen, Germany
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13
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Routing information flow by separate neural synchrony frequencies allows for "functionally labeled lines" in higher primate cortex. Proc Natl Acad Sci U S A 2019; 116:12506-12515. [PMID: 31147468 PMCID: PMC6589668 DOI: 10.1073/pnas.1819827116] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Dynamical coordination of the neural activity between individual neurons is known to have a key role in the efficient transfer of sensory information to associative areas. Here, we report a role of interneuronal synchrony within the high-gamma (180 to 220 Hz) frequency range of activity in macaque area MT (a visual area in the dorsal visual pathway) in determining behavioral performance. This is, however, in contrast to previous reports for the ventral visual pathway (such as area V4), where only gamma range (40 to 70 Hz) was observed to play a role. We propose that such a difference between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas. Efficient transfer of sensory information to higher (motor or associative) areas in primate visual cortical areas is crucial for transforming sensory input into behavioral actions. Dynamically increasing the level of coordination between single neurons has been suggested as an important contributor to this efficiency. We propose that differences between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas, ensuring a proper routing of the information flow. Here we determined the level of coordination between neurons in area MT in macaque visual cortex in a visual attention task via the strength of synchronization between the neurons’ spike timing relative to the phase of oscillatory activities in local field potentials. In contrast to reports on the ventral visual pathway, we observed the synchrony of spikes only in the range of high gamma (180 to 220 Hz), rather than gamma (40 to 70 Hz) (as reported previously) to predict the animal’s reaction speed. This supports a mechanistic role of the phase of high-gamma oscillatory activity in dynamically modulating the efficiency of neuronal information transfer. In addition, for inputs to higher cortical areas converging from the dorsal and ventral pathway, the distinct frequency bands of these inputs can be leveraged to preserve the identity of the input source. In this way source-specific oscillatory activity in primate cortex can serve to establish and maintain “functionally labeled lines” for dynamically adjusting cortical information transfer and multiplexing converging sensory signals.
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14
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Li K, Ditlevsen S. Neural decoding with visual attention using sequential Monte Carlo for leaky integrate-and-fire neurons. PLoS One 2019; 14:e0216322. [PMID: 31086375 PMCID: PMC6516730 DOI: 10.1371/journal.pone.0216322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 04/18/2019] [Indexed: 11/18/2022] Open
Abstract
How the brain makes sense of a complicated environment is an important question, and a first step is to be able to reconstruct the stimulus that give rise to an observed brain response. Neural coding relates neurobiological observations to external stimuli using computational methods. Encoding refers to how a stimulus affects the neuronal output, and entails constructing a neural model and parameter estimation. Decoding refers to reconstruction of the stimulus that led to a given neuronal output. Existing decoding methods rarely explain neuronal responses to complicated stimuli in a principled way. Here we perform neural decoding for a mixture of multiple stimuli using the leaky integrate-and-fire model describing neural spike trains, under the visual attention hypothesis of probability mixing in which the neuron only attends to a single stimulus at any given time. We assume either a parallel or serial processing visual search mechanism when decoding multiple simultaneous neurons. We consider one or multiple stochastic stimuli following Ornstein-Uhlenbeck processes, and dynamic neuronal attention that switches following discrete Markov processes. To decode stimuli in such situations, we develop various sequential Monte Carlo particle methods in different settings. The likelihood of the observed spike trains is obtained through the first-passage time probabilities obtained by solving the Fokker-Planck equations. We show that the stochastic stimuli can be successfully decoded by sequential Monte Carlo, and different particle methods perform differently considering the number of observed spike trains, the number of stimuli, model complexity, etc. The proposed novel decoding methods, which analyze the neural data through psychological visual attention theories, provide new perspectives to understand the brain.
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Affiliation(s)
- Kang Li
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Susanne Ditlevsen
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
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15
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Keemink SW, Tailor DV, van Rossum MCW. Unconscious Biases in Neural Populations Coding Multiple Stimuli. Neural Comput 2018; 30:3168-3188. [PMID: 30216141 DOI: 10.1162/neco_a_01130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Throughout the nervous system, information is commonly coded in activity distributed over populations of neurons. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of the encoded stimulus can be read out without bias. However, in many situations, multiple stimuli are simultaneously present; for example, multiple motion patterns might overlap. Here we find that when multiple stimuli that overlap in their neural representation are simultaneously encoded in the population, biases in the read-out emerge. Although the bias disappears in the absence of noise, the bias is remarkably persistent at low noise levels. The bias can be reduced by competitive encoding schemes or by employing complex decoders. To study the origin of the bias, we develop a novel general framework based on gaussian processes that allows an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli. The results have implications for neural coding and behavioral experiments on, for instance, overlapping motion patterns.
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Affiliation(s)
- Sander W Keemink
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K., and Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Dharmesh V Tailor
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K.
| | - Mark C W van Rossum
- School of Psychology and School of Mathematical Sciences, University of Nottingham, Nottingham NH7 2RD, U.K.
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16
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Single neurons may encode simultaneous stimuli by switching between activity patterns. Nat Commun 2018; 9:2715. [PMID: 30006598 PMCID: PMC6045601 DOI: 10.1038/s41467-018-05121-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 06/11/2018] [Indexed: 11/08/2022] Open
Abstract
How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple stimuli by interleaving signals across time. We record single units in an auditory region, the inferior colliculus, while monkeys localize 1 or 2 simultaneous sounds. During dual-sound trials, we find that some neurons fluctuate between firing rates observed for each single sound, either on a whole-trial or on a sub-trial timescale. These fluctuations are correlated in pairs of neurons, can be predicted by the state of local field potentials prior to sound onset, and, in one monkey, can predict which sound will be reported first. We find corroborating evidence of fluctuating activity patterns in a separate dataset involving responses of inferotemporal cortex neurons to multiple visual stimuli. Alternation between activity patterns corresponding to each of multiple items may therefore be a general strategy to enhance the brain processing capacity, potentially linking such disparate phenomena as variable neural firing, neural oscillations, and limits in attentional/memory capacity.
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17
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Abstract
The relationship between visual attentional selection of items in particular spatial locations and selection by nonspatial criteria was investigated in a partial report experiment with report of letters (as many as possible) from brief postmasked exposures of circular arrays of letters and digits. The data were fitted by mathematical models based on Bundesen's (Psychological Review, 97, 523-547, 1990) theory of visual attention (TVA). Both attentional weights of targets (letters) and attentional weights of distractors (digits) showed strong variations across the eight possible target locations, but for each of the ten participants, the ratio of the weight of a distractor at a given location to the weight of a target at the same location was approximately constant. The results were accommodated by revising the weight equation of TVA such that the attentional weight of an object equals a product of a spatial weight component (weight due to being at a particular location) and a nonspatial weight component (weight due to having particular features other than locations), the two components scaling the effects of each other multiplicatively.
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18
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Li K, Kozyrev V, Kyllingsbæk S, Treue S, Ditlevsen S, Bundesen C. Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field. Front Comput Neurosci 2016; 10:141. [PMID: 28082892 PMCID: PMC5187355 DOI: 10.3389/fncom.2016.00141] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Accepted: 12/12/2016] [Indexed: 11/26/2022] Open
Abstract
A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior.
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Affiliation(s)
- Kang Li
- Department of Mathematical Sciences, University of CopenhagenCopenhagen, Denmark; Department of Psychology, University of CopenhagenCopenhagen, Denmark
| | - Vladislav Kozyrev
- Cognitive Neuroscience Laboratory, German Primate CenterGoettingen, Germany; Bernstein Center for Computational NeuroscienceGoettingen, Germany; Chair Theory of Cognitive Systems, Institute for Neuroinformatics, Ruhr University BochumBochum, Germany; Visual Cognition Lab, Department of Medicine/Physiology, University of FribourgFribourg, Switzerland
| | - Søren Kyllingsbæk
- Department of Psychology, University of Copenhagen Copenhagen, Denmark
| | - Stefan Treue
- Cognitive Neuroscience Laboratory, German Primate CenterGoettingen, Germany; Bernstein Center for Computational NeuroscienceGoettingen, Germany; Faculty for Biology and Psychology, Goettingen UniversityGeottingen, Germany
| | - Susanne Ditlevsen
- Department of Mathematical Sciences, University of Copenhagen Copenhagen, Denmark
| | - Claus Bundesen
- Department of Psychology, University of Copenhagen Copenhagen, Denmark
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19
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Li K, Bundesen C, Ditlevsen S. Responses of Leaky Integrate-and-Fire Neurons to a Plurality of Stimuli in Their Receptive Fields. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2016; 6:8. [PMID: 27215548 PMCID: PMC4877359 DOI: 10.1186/s13408-016-0040-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Accepted: 04/30/2016] [Indexed: 06/05/2023]
Abstract
A fundamental question concerning the way the visual world is represented in our brain is how a cortical cell responds when its classical receptive field contains a plurality of stimuli. Two opposing models have been proposed. In the response-averaging model, the neuron responds with a weighted average of all individual stimuli. By contrast, in the probability-mixing model, the cell responds to a plurality of stimuli as if only one of the stimuli were present. Here we apply the probability-mixing and the response-averaging model to leaky integrate-and-fire neurons, to describe neuronal behavior based on observed spike trains. We first estimate the parameters of either model using numerical methods, and then test which model is most likely to have generated the observed data. Results show that the parameters can be successfully estimated and the two models are distinguishable using model selection.
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Affiliation(s)
- Kang Li
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark
- Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, Copenhagen, 1353, Denmark
| | - Claus Bundesen
- Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, Copenhagen, 1353, Denmark
| | - Susanne Ditlevsen
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark.
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