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122 results on '"Robbe L"'

1. Environmental dynamics shape perceptual decision bias

Author

Julie A. Charlton, Wiktor F. Młynarski, Yoon H. Bai, Ann M. Hermundstad, and Robbe L. T. Goris

Subjects
Biology (General), QH301-705.5
Abstract

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer’s continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals. Author summary The brain relies on prior knowledge to make perceptual inferences when sensory information is ambiguous. However, when the environmental context changes, the appropriate prior knowledge often changes with it. Here, we develop a Bayesian observer model to investigate how to make optimal perceptual inferences when sensory information and environmental context are both uncertain. The behavioral signature of this strategy is a context-appropriate decision bias whose strength grows with the reliability of the context cue, the stability of the environment, and with the number of decisions since the most recent change in context. We identified exactly this pattern in the behavior of human subjects performing a dynamic orientation discrimination task. Together, our results suggest that in dynamic environments, our perceptual interpretations of ambiguous sensory measurements depend on our underlying belief about the likelihood of change.

Published
2023

2. Primary visual cortex straightens natural video trajectories

Author

Olivier J. Hénaff, Yoon Bai, Julie A. Charlton, Ian Nauhaus, Eero P. Simoncelli, and Robbe L. T. Goris

Subjects
Science
Abstract

Many behaviours depend on predictions about the environment. Here the authors find neural populations in primary visual cortex to straighten the temporal trajectories of natural video clips, facilitating the extrapolation of past observations.

Published
2021
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3. Representation of visual uncertainty through neural gain variability

Author

Olivier J. Hénaff, Zoe M. Boundy-Singer, Kristof Meding, Corey M. Ziemba, and Robbe L. T. Goris

Subjects
Science
Abstract

How does the brain represent sensory uncertainty? The authors find that neural gain variability tracks stimulus uncertainty across the visual hierarchy and explain their findings with a simple generalization of canonical models of neural computation.

Published
2020
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6. Attention stabilizes the shared gain of V4 populations

Author

Neil C Rabinowitz, Robbe L Goris, Marlene Cohen, and Eero P Simoncelli

Subjects
computation, sensory, vision, statistic, attention, Medicine, Science, Biology (General), QH301-705.5
Abstract

Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior.

Published
2015
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7. Invariance in visual object recognition requires training: a computational argument

Author

Robbe L. T Goris and Hans P Op De Beeck

Subjects
inferior temporal cortex, invariance, object recognition, population coding, training, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Visual object recognition is remarkably accurate and robust, yet its neurophysiological underpinnings are poorly understood. Single cells in brain regions thought to underlie object recognition code for many stimulus aspects, which poses a limit on their invariance. Combining the responses of multiple non-invariant neurons via weighted linear summation, i.e. population-coding, has been suggested to offer an optimal decoding strategy able to achieve invariant object recognition. However, because object identification is essentially parameter optimization in this model, the characteristics of the identification task trained to perform are critically important. If this task does not require invariance, a neural population-code is inherently more selective but less tolerant than the single-neurons constituting the population. Nevertheless, tolerance can be learned –provided that it is trained for–, at the cost of selectivity. We argue that this model is the appropriate null-hypothesis to compare behavioural results with and conclude that it may explain several experimental findings.

Published
2010
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8. Neural representations that support invariant object recognition

Author

Robbe L. T Goris and Hans P Op De Beeck

Subjects
inferior temporal cortex, object recognition, population coding, multidimensional tuning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Neural mechanisms underlying invariant behaviour such as object recognition are not well understood. For brain regions critical for object recognition, such as inferior temporal cortex (ITC), there is now ample evidence indicating that single cells code for many stimulus aspects, implying that only a moderate degree of invariance is present. However, recent theoretical and empirical work seems to suggest that integrating responses of multiple non-invariant units may produce invariant representations at population level. We provide an explicit test for the hypothesis that a linear read-out mechanism of a pool of units resembling ITC neurons may achieve invariant performance in an identification task. A linear classifier was trained to decode a particular value in a 2-D stimulus space using as input the response pattern across a population of units. Only one dimension was relevant for the task, and the stimulus location on the irrelevant dimension was kept constant during training. In a series of identification tests, the stimulus location on the relevant and irrelevant dimension was manipulated, yielding estimates for both the level of sensitivity and tolerance reached by the network. We studied the effects of several single-cell characteristics as well as population characteristics typically considered in the literature, but found little support for the hypothesis. While the classifier averages out effects of idiosyncratic tuning properties and interunit variability, its invariance is very much determined by the (hypothetical) 'average' neuron. Consequently, even at population level there exists a fundamental trade-off between selectivity and tolerance, and invariant behaviour does not emerge spontaneously.

Published
2009
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10. A model of sensory neural responses in the presence of unknown modulatory inputs

Author

Rabinowitz, Neil C., Goris, Robbe L. T., Ballé, Johannes, and Simoncelli, Eero P.

Subjects
Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Abstract

Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signals can confound or bias the inferences that one derives from spiking responses. Recent work demonstrates that for sensory neurons, these effects can be captured by a modulated Poisson model, whose rate is the product of a stimulus-driven response function and an unknown modulatory signal. Here, we extend this model, by incorporating explicit modulatory elements that are known (specifically, spike-history dependence, as in previous models), and by constraining the remaining latent modulatory signals to be smooth in time. We develop inference procedures for fitting the entire model, including hyperparameters, via evidence optimization, and apply these to simulated data, and to responses of ferret auditory midbrain and cortical neurons to complex sounds. We show that integrating out the latent modulators yields better (or more readily-interpretable) receptive field estimates than a standard Poisson model. Conversely, integrating out the stimulus dependence yields estimates of the slowly-varying latent modulators., Comment: 9 pages, 5 figures. minor changes since v1: added extra references, connections to previous models, links to GLMs, complexity measures

Published
2015

12. Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys.

Author

Ziemba, Corey M., Goris, Robbe L. T., Stine, Gabriel M., Perez, Richard K., Simoncelli, Eero P., and Movshon, J. Anthony

Subjects
*MACAQUES, *MONKEYS, *NEURONS, *DECISION making, *STIMULUS & response (Psychology)
Abstract

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaqueV1 and V2 and simultaneouslymeasured behavioral judgments of texture. We generated stimuli along a continuumbetween naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior comparedwith V1. The firing of both V1 andV2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the secondmonkey. We conclude that neural responses supporting texture perception likely continue to develop downstreamof V2. Further, combined with neural data recordedwhile the same twomonkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and therefore unlikely to directly reflect the formation of perceptual decisions. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Representation of visual uncertainty through neural gain variability

Author

Kristof Meding, Robbe L. T. Goris, Zoe M. Boundy-Singer, Olivier J. Hénaff, and Corey M. Ziemba

Subjects
Male, 0301 basic medicine, Visual perception, Computer science, media_common.quotation_subject, Science, Models, Neurological, Population, Computer Science::Neural and Evolutionary Computation, General Physics and Astronomy, Sensory system, Stimulus (physiology), Macaque, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Perception, biology.animal, medicine, Biological neural network, Animals, lcsh:Science, education, Visual Cortex, media_common, Neurons, education.field_of_study, Multidisciplinary, biology, Quantitative Biology::Neurons and Cognition, business.industry, Uncertainty, Pattern recognition, General Chemistry, Macaca mulatta, Neural encoding, 030104 developmental biology, Visual cortex, medicine.anatomical_structure, Visual Perception, lcsh:Q, Artificial intelligence, Visual system, business, 030217 neurology & neurosurgery
Abstract

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a model of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test this model, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is specific to the features encoded by these neurons and largely invariant to the source of uncertainty. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and illustrate how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity., How does the brain represent sensory uncertainty? The authors find that neural gain variability tracks stimulus uncertainty across the visual hierarchy and explain their findings with a simple generalization of canonical models of neural computation.

Published
2020

18. Perceptual decisions exhibit hallmarks of dynamic Bayesian inference

Author

Julie A. Charlton, Wiktor F. Młynarski, Yoon H. Bai, Ann M. Hermundstad, and Robbe L. T. Goris

Abstract

To interpret the sensory environment, the brain combines ambiguous sensory measurements with context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages the statistical structure of the task to maximize decision accuracy and show that its decisions are biased by task context. The magnitude of this decision bias is not a fixed property of the sensory measurement but depends on the observer’s belief about the current context. The model therefore predicts that decision bias will grow with the reliability of the context cue, the stability of the environment, and with the number of trials since the last context switch. Analysis of human choice data validates all three predictions, providing evidence that the brain continuously updates probabilistic representations of the environment to best interpret an uncertain, ever-changing world.SIGNIFICANCEThe brain relies on prior knowledge to make perceptual inferences when sensory information is ambiguous. However, when the environmental context changes, the appropriate prior knowledge often changes with it. Here, we develop a Bayesian observer model to investigate how to make optimal perceptual inferences when sensory information and environmental context are both uncertain. The behavioral signature of this strategy is a context-appropriate decision bias whose strength grows with the reliability of the context cue, the stability of the environment, and with the number of decisions since the most recent change in context. We identified exactly this pattern in the behavior of human subjects performing a dynamic orientation discrimination task. Together, our results suggest that the brain continuously updates probabilistic representations of the environment to make perceptual decisions in the face of uncertainty over both sensory and contextual information.

Published
2022
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20. Confidence reflects a noisy decision reliability estimate

Author

Zoe M, Boundy-Singer, Corey M, Ziemba, and Robbe L T, Goris

Abstract

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgements. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subject's estimate of the reliability of their decision. The quality of this estimate is limited by the subject's uncertainty about the uncertainty of the variable that informs their decision ('meta-uncertainty'). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, investigated in six previous studies. We find meta-uncertainty varies across subjects, is stable over time, generalizes across some domains and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

Published
2022

21. Confidence as a noisy decision reliability estimate

Author

Zoe M. Boundy-Singer, Corey M. Ziemba, and Robbe L. T. Goris

Abstract

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgments. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subject’s estimate of the reliability of their decision. The quality of this estimate is limited by the subject’s uncertainty about the uncertainty of the variable that informs their decision (“meta-uncertainty”). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, revealing that meta-uncertainty varies across subjects, is stable over time, generalizes across some domains, and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

Published
2021
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22. Perceptual straightening of natural videos

Author

Robbe L. T. Goris, Olivier J. Hénaff, and Eero P. Simoncelli

Subjects
0301 basic medicine, Pixel, Computer science, business.industry, General Neuroscience, media_common.quotation_subject, Pattern recognition, Space (commercial competition), Curvature, Domain (software engineering), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Perception, Trajectory, Contrast (vision), Natural (music), Artificial intelligence, business, Neuroscience, 030217 neurology & neurosurgery, media_common
Abstract

Many behaviors rely on predictions derived from recent visual input, but the temporal evolution of those inputs is generally complex and difficult to extrapolate. We propose that the visual system transforms these inputs to follow straighter temporal trajectories. To test this ‘temporal straightening’ hypothesis, we develop a methodology for estimating the curvature of an internal trajectory from human perceptual judgments. We use this to test three distinct predictions: natural sequences that are highly curved in the space of pixel intensities should be substantially straighter perceptually; in contrast, artificial sequences that are straight in the intensity domain should be more curved perceptually; finally, naturalistic sequences that are straight in the intensity domain should be relatively less curved. Perceptual data validate all three predictions, as do population models of the early visual system, providing evidence that the visual system specifically straightens natural videos, offering a solution for tasks that rely on prediction. The brain predicts future sensory input. The authors hypothesize that the visual system achieves this by straightening the temporal trajectories of natural videos, and they provide evidence using human perceptual experiments and computational modeling.

Published
2019
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23. ESICM LIVES 2016: part one: Milan, Italy. 1-5 October 2016

Author

Bos, L., Schouten, L., van Vught, L., Wiewel, M., Ong, D., Cremer, O., Artigas, A., Martin-Loeches, I., Hoogendijk, A., van der Poll, T., Horn, J., Juffermans, N., Schultz, M., de Prost, N., Pham, T., Carteaux, G., Dessap, A. Mekontso, Brun-Buisson, C., Fan, E., Bellani, G., Laffey, J., Mercat, A., Brochard, L., Maitre, B., Howells, P. A., Thickett, D. R., Knox, C., Park, D. P., Gao, F., Tucker, O., Whitehouse, T., McAuley, D. F., Perkins, G. D., Pham, T., Laffey, J., Bellani, G., Fan, E., Pisani, L., Roozeman, J. P., Simonis, F. D., Giangregorio, A., Schouten, L. R., Van der Hoeven, S. M., Horn, J., Neto, A. Serpa, Festic, E., Dondorp, A. M., Grasso, S., Bos, L. D., Schultz, M. J., Koster-Brouwer, M., Verboom, D., Scicluna, B., van de Groep, K., Frencken, J., Schultz, M., van der Poll, T., Bonten, M., Cremer, O., Ko, J. I., Kim, K. S., Suh, G. J., Kwon, W. Y., Kim, K., Shin, J. H., Ranzani, O. T., Prina, E., Menendez, R., Ceccato, A., Mendez, R., Cilloniz, C., Gabarrus, A., Ferrer, M., Torres, A., Urbano, A., Zhang, L. A., Swigon, D., Pike, F., Parker, R. S., Clermont, G., Scheer, C., Kuhn, S. O., Modler, A., Vollmer, M., Fuchs, C., Hahnenkamp, K., Rehberg, S., Gründling, M., Taggu, A., Darang, N., Öveges, N., László, I., Tánczos, K., Németh, M., Lebák, G., Tudor, B., Érces, D., Kaszaki, J., Huber, W., Trásy, D., Molnár, Z., Ferrara, G., Edul, V. S. Kanoore, Canales, H. S., Martins, E., Canullán, C., Murias, G., Pozo, M. O., Eguillor, J. F. Caminos, Buscetti, M. G., Ince, C., Dubin, A., Aya, H. D., Rhodes, A., Fletcher, N., Grounds, R. M., Cecconi, M., Jacquet-Lagrèze, M., Riche, M., Schweizer, R., Portran, P., Fornier, W., Lilot, M., Neidecker, J., Fellahi, J. L., Escoresca-Ortega, A., Gutiérrez-Pizarraya, A., Charris-Castro, L., Corcia-Palomo, Y., Fernandez-Delgado, E., Garnacho-Montero, J., Roger, C., Muller, L., Elotmani, L., Lipman, J., Lefrant, J. Y., Roberts, J. A., Muñoz-Bermúdez, R., Samper, M., Climent, C., Vasco, F., Sara, V., Luque, S., Campillo, N., Cerrato, S. Grau, Masclans, J. R., Alvarez-Lerma, F., Brugger, S. Carvalho, Jimenez, G. Jimenez, Torner, M. Miralbés, Cabello, J. Trujillano, Garrido, B. Balsera, Casals, X. Nuvials, Gaite, F. Barcenilla, Vidal, M. Vallverdú, Martínez, M. Palomar, Gusarov, V., Shilkin, D., Dementienko, M., Nesterova, E., Lashenkova, N., Kuzovlev, A., Zamyatin, M., Demoule, A., Carreira, S., Lavault, S., Palancca, O., Morawiec, E., Mayaux, J., Arnulf, I., Similowski, T., Rasmussen, B. S., Maltesen, R. G., Hanifa, M., Pedersen, S., Kristensen, S. R., Wimmer, R., Panigada, M., Bassi, G. Li, Ranzani, O. T., Kolobow, T., Zanella, A., Cressoni, M., Berra, L., Parrini, V., Kandil, H., Salati, G., Livigni, S., Amatu, A., Andreotti, A., Tagliaferri, F., Moise, G., Mercurio, G., Costa, A., Vezzani, A., Lindau, S., Babel, J., Cavana, M., Consonni, D., Pesenti, A., Gattinoni, L., Torres, A., Mansouri, P., Zand, F., Zahed, L., Dehghanrad, F., Bahrani, M., Ghorbani, M., Cambiaghi, B., Moerer, O., Mauri, T., Kunze-Szikszay, N., Ritter, C., Pesenti, A., Quintel, M., Vilander, L. M., Kaunisto, M. A., Vaara, S. T., Pettilä, V., Mulier, J. L. G. Haitsma, Rozemeijer, S., Spoelstra-de Man, A. M. E., Elbers, P. E., Tuinman, P. R., de Waard, M. C., Oudemans-van Straaten, H. M., Liberatore, A. M. A., Souza, R. B., Martins, A. M. C. R. P. F., Vieira, J. C. F., Koh, I. H. J., Martínez, M. Galindo, Sánchez, R. Jiménez, Gascón, L. Martínez, Mulero, M. D. Rodríguez, Freire, A. Ortín, Muñoz, A. Ojados, Acebes, S. Rebollo, Martínez, Á. Fernández, Aliaga, S. Moreno, Para, L. Herrera, Payá, J. Murcia, Mulero, F. Rodríguez, Guerci, P., Ince, Y., Heeman, P., Ergin, B., Ince, C., Uz, Z., Massey, M., Ince, Y., Papatella, R., Bulent, E., Guerci, P., Toraman, F., Ince, C., Longbottom, E. R., Torrance, H. D., Owen, H. C., Hinds, C. J., Pearse, R. M., O’Dywer, M. J., Trogrlic, Z., van der Jagt, M., Lingsma, H., Ponssen, H. H., Schoonderbeek, J. F., Schreiner, F., Verbrugge, S. J., Duran, S., van Achterberg, T., Bakker, J., Gommers, D. A. M. P. J., Ista, E., Krajčová, A., Waldauf, P., Duška, F., Shah, A., Roy, N., McKechnie, S., Doree, C., Fisher, S., Stanworth, S. J., Jensen, J. F., Overgaard, D., Bestle, M. H., Christensen, D. F., Egerod, I., Pivkina, A., Gusarov, V., Zhivotneva, I., Pasko, N., Zamyatin, M., Jensen, J. F., Egerod, I., Bestle, M. H., Christensen, D. F., Alklit, A., Hansen, R. L., Knudsen, H., Grode, L. B., Overgaard, D., Hravnak, M., Chen, L., Dubrawski, A., Clermont, G., Pinsky, M. R., Parry, S. M., Knight, L. D., Connolly, B. C., Baldwin, C. E., Puthucheary, Z. A., Denehy, L., Hart, N., Morris, P. E., Mortimore, J., Granger, C. L., Jensen, H. I., Piers, R., Van den Bulcke, B., Malmgren, J., Metaxa, V., Reyners, A. K., Darmon, M., Rusinova, K., Talmor, D., Meert, A. P., Cancelliere, L., Zubek, L., Maia, P., Michalsen, A., Decruyenaere, J., Kompanje, E., Vanheule, S., Azoulay, E., Vansteelandt, S., Benoit, D., Van den Bulcke, B., Piers, R., Jensen, H. I., Malmgren, J., Metaxa, V., Reyners, A. K., Darmon, M., Rusinova, K., Talmor, D., Meert, A. P., Cancelliere, L., Zubek, L., Maia, P., Michalsen, A., Decruyenaere, J., Kompanje, E., Vanheule, S., Azoulay, E., Vansteelandt, S., Benoit, D., Ryan, C., Dawson, D., Ball, J., Noone, K., Aisling, B., Prudden, S., Ntantana, A., Matamis, D., Savvidou, S., Giannakou, M., Gouva, M., Nakos, G., Koulouras, V., Aron, J., Lumley, G., Milliken, D., Dhadwal, K., McGrath, B. A., Lynch, S. J., Bovento, B., Sharpe, G., Grainger, E., Pieri-Davies, S., Wallace, S., McGrath, B., Lynch, S. J., Bovento, B., Grainger, E., Pieri-Davies, S., Sharpe, G., Wallace, S., Jung, M., Cho, J., Park, H., Suh, G., Kousha, O., Paddle, J., Gripenberg, L. Gamrin, Rehal, M. Sundström, Wernerman, J., Rooyackers, O., de Grooth, H. J., Choo, W. P., Spoelstra-de Man, A. M., Swart, E. L., Oudemans-van Straaten, H. M., Talan, L., Güven, G., Altıntas, N. D., Padar, M., Uusvel, G., Starkopf, L., Starkopf, J., Blaser, A. Reintam, Kalaiselvan, M. S., Arunkumar, A. S., Renuka, M. K., Shivkumar, R. L., Volbeda, M., ten Kate, D., Hoekstra, M., van der Maaten, J. M., Nijsten, M. W., Komaromi, A., Rooyackers, O., Wernerman, J., Norberg, Å., Smedberg, M., Mori, M., Pettersson, L., Norberg, Å., Rooyackers, O., Wernerman, J., Theodorakopoulou, M., Christodoulopoulou, T., Diamantakis, A., Frantzeskaki, F., Kontogiorgi, M., Chrysanthopoulou, E., Lygnos, M., Diakaki, C., Armaganidis, A., Gundogan, K., Dogan, E., Coskun, R., Muhtaroglu, S., Sungur, M., Ziegler, T., Guven, M., Kleyman, A., Khaliq, W., Andreas, D., Singer, M., Meierhans, R., Schuepbach, R., De Brito-Ashurst, I., Zand, F., Sabetian, G., Nikandish, R., Hagar, F., Masjedi, M., Maghsudi, B., Vazin, A., Ghorbani, M., Asadpour, E., Kao, K. C., Chiu, L. C., Hung, C. Y., Chang, C. H., Li, S. H., Hu, H. C., El Maraghi, S., Ali, M., Rageb, D., Helmy, M., Marin-Corral, J., Vilà, C., Masclans, J. R., Vàzquez, A., Martín-Loeches, I., Díaz, E., Yébenes, J. C., Rodriguez, A., Álvarez-Lerma, F., Varga, N., Cortina-Gutiérrez, A., Dono, L., Martínez-Martínez, M., Maldonado, C., Papiol, E., Pérez-Carrasco, M., Ferrer, R., Nweze, K., Morton, B., Welters, I., Houard, M., Voisin, B., Ledoux, G., Six, S., Jaillette, E., Nseir, S., Romdhani, S., Bouneb, R., Loghmari, D., Aicha, N. Ben, Ayachi, J., Meddeb, K., Chouchène, I., Khedher, A., Boussarsar, M., Chan, K. S., Yu, W. L., Marin-Corral, J., Vilà, C., Masclans, J. R., Nolla, J., Vidaur, L., Bonastre, J., Suberbiola, B., Guerrero, J. E., Rodriguez, A., Coll, N. Ramon, Jiménez, G. Jiménez, Brugger, S. Carvalho, Calero, J. Codina, Garrido, B. Balsera, García, M., Martínez, M. Palomar, Vidal, M. Vallverdú, de la Torre, M. C., Vendrell, E., Palomera, E., Güell, E., Yébenes, J. C., Serra-Prat, M., Bermejo-Martín, J. F., Almirall, J., Tomas, E., Escoval, A., Froe, F., Pereira, M. H. Vitoria, Velez, N., Viegas, E., Filipe, E., Groves, C., Reay, M., Chiu, L. C., Hu, H. C., Hung, C. Y., Chang, C. H., Li, S. H., Kao, K. C., Ballin, A., Facchin, F., Sartori, G., Zarantonello, F., Campello, E., Radu, C. M., Rossi, S., Ori, C., Simioni, P., Umei, N., Shingo, I., Santos, A. C., Candeias, C., Moniz, I., Marçal, R., e Silva, Z. Costa, Ribeiro, J. M., Georger, J. F., Ponthus, J. P., Tchir, M., Amilien, V., Ayoub, M., Barsam, E., Martucci, G., Panarello, G., Tuzzolino, F., Capitanio, G., Ferrazza, V., Carollo, T., Giovanni, L., Arcadipane, A., Sánchez, M. López, González-Gay, M. A., Díaz, F. J. Llorca, López, M. I. 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L., Mebazaa, A., Hartmann, J., Harm, S., and Weber, V.

Published
2016
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27. Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons

Author

Luke Shaw, Robbe L. T. Goris, Allison J. Murphy, J. Michael Hasse, and Farran Briggs

Subjects
0301 basic medicine, Functional role, genetic structures, Cognitive Neuroscience, Thalamus, Models, Neurological, Optogenetics, Biology, Lateral geniculate nucleus, Article, Feedback, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, medicine, Animals, Visual Pathways, Neurons, Ferrets, Geniculate Bodies, Sensory Systems, Information coding, 030104 developmental biology, Visual cortex, medicine.anatomical_structure, nervous system, Receptive field, sense organs, Neuroscience, psychological phenomena and processes, 030217 neurology & neurosurgery, Photic Stimulation
Abstract

In spite of their anatomical robustness, it has been difficult to establish the functional role of corticogeniculate circuits connecting primary visual cortex with the lateral geniculate nucleus of the thalamus (LGN) in the feedback direction. Growing evidence suggests that corticogeniculate feedback does not directly shape the spatial receptive field properties of LGN neurons, but rather regulates the timing and precision of LGN responses and the information coding capacity of LGN neurons. We propose that corticogeniculate feedback specifically stabilizes the response gain of LGN neurons, thereby increasing their information coding capacity. Inspired by early work by McClurkin and colleagues (1994), we manipulated the activity of corticogeniculate neurons to test this hypothesis. We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. Together, these findings suggest that the influence of corticogeniculate feedback on LGN response precision and information coding capacity could be mediated through reductions in gain variability.

Published
2020

31. Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Author

J. Anthony Movshon, Robbe L. T. Goris, Gabriel M. Stine, Eero P. Simoncelli, and Corey M. Ziemba

Subjects
Male, 0301 basic medicine, Surround suppression, Nerve net, media_common.quotation_subject, Sensory system, Stimulus (physiology), Choice Behavior, Judgment, 03 medical and health sciences, 0302 clinical medicine, Species Specificity, Orientation, Perception, Task Performance and Analysis, medicine, Animals, Humans, Research Articles, Visual Cortex, media_common, Communication, business.industry, General Neuroscience, Adequate stimulus, Macaca mulatta, 030104 developmental biology, medicine.anatomical_structure, Visual cortex, nervous system, Visual Perception, Neuron, Nerve Net, business, Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract

Responses of individual task-relevant sensory neurons can predict monkeys' trial-by-trial choices in perceptual decision-making tasks. Choice-correlated activity has been interpreted as evidence that the responses of these neurons are causally linked to perceptual judgments. To further test this hypothesis, we studied responses of orientation-selective neurons in V1 and V2 while two macaque monkeys performed a fine orientation discrimination task. Although both animals exhibited a high level of neuronal and behavioral sensitivity, only one exhibited choice-correlated activity. Surprisingly, this correlation was negative: when a neuron fired more vigorously, the animal was less likely to choose the orientation preferred by that neuron. Moreover, choice-correlated activity emerged late in the trial, earlier in V2 than in V1, and was correlated with anticipatory signals. Together, these results suggest that choice-correlated activity in task-relevant sensory neurons can reflect postdecision modulatory signals.SIGNIFICANCE STATEMENTWhen observers perform a difficult sensory discrimination, repeated presentations of the same stimulus can elicit different perceptual judgments. This behavioral variability often correlates with variability in the activity of sensory neurons driven by the stimulus. Traditionally, this correlation has been interpreted as suggesting a causal link between the activity of sensory neurons and perceptual judgments. More recently, it has been argued that the correlation instead may originate in recurrent input from other brain areas involved in the interpretation of sensory signals. Here, we call both hypotheses into question. We show that choice-related activity in sensory neurons can be highly variable across observers and can reflect modulatory processes that are dissociated from perceptual decision-making.

Published
2017
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32. Representation of uncertainty in macaque visual cortex

Author

Zoe M. Boundy-Singer, Robbe L. T. Goris, Olivier J. Hénaff, Corey M. Ziemba, and Kristof Meding

Subjects
education.field_of_study, biology, Computer science, media_common.quotation_subject, Population, Sensory system, Stimulus (physiology), Macaque, Visual cortex, medicine.anatomical_structure, Perception, biology.animal, medicine, Biological neural network, education, Neuroscience, media_common
Abstract

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a view of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test our theory, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is invariant to the source of uncertainty, and that it is specific to the features encoded by these neurons. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and derive how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity.

Published
2019
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33. Compound stimuli reveal the structure of visual motion selectivity in macaque MT neurons

Author

Andrew D Zaharia, J. Anthony Movshon, Robbe L. T. Goris, and Eero P. Simoncelli

Subjects
Physics, 0303 health sciences, Quantitative Biology::Neurons and Cognition, Orientation (computer vision), Motion (geometry), Sensory system, Invariant (physics), Texture (music), 03 medical and health sciences, 0302 clinical medicine, Visual cortex, medicine.anatomical_structure, Frequency domain, medicine, Spatial frequency, Biological system, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Motion selectivity in primary visual cortex (V1) is approximately separable in orientation, spatial frequency, and temporal frequency (“frequency-separable”). Models for area MT neurons posit that their selectivity arises by combining direction-selective V1 afferents whose tuning is organized around a tilted plane in the frequency domain, specifying a particular direction and speed (“velocity-separable”). This construction explains “pattern direction selective” MT neurons, which are velocity-selective but relatively invariant to spatial structure, including spatial frequency, texture and shape. Surprisingly, when tested with single drifting gratings, most MT neurons’ responses are fit equally well by models with either form of separability. However, responses to plaids (sums of two moving gratings) tend to be better described as velocity-separable, especially for pattern neurons. We conclude that direction selectivity in MT is primarily computed by summing V1 afferents, but pattern-invariant velocity tuning for complex stimuli may arise from local, recurrent interactions.Significance StatementHow do sensory systems build representations of complex features from simpler ones? Visual motion representation in cortex is a well-studied example: the direction and speed of moving objects, regardless of shape or texture, is computed from the local motion of oriented edges. Here we quantify tuning properties based on single-unit recordings in primate area MT, then fit a novel, generalized model of motion computation. The model reveals two core properties of MT neurons — speed tuning and invariance to local edge orientation — result from a single organizing principle: each MT neuron combines afferents that represent edge motions consistent with a common velocity, much as V1 simple cells combine thalamic inputs consistent with a common orientation.

Published
2019
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40. Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons.

Author

Murphy, Allison J., Shaw, Luke, Hasse, J. Michael, Goris, Robbe L. T., and Briggs, Farran

Abstract

In spite of their anatomical robustness, it has been difficult to establish the functional role of corticogeniculate circuits connecting primary visual cortex with the lateral geniculate nucleus of the thalamus (LGN) in the feedback direction. Growing evidence suggests that corticogeniculate feedback does not directly shape the spatial receptive field properties of LGN neurons, but rather regulates the timing and precision of LGN responses and the information coding capacity of LGN neurons. We propose that corticogeniculate feedback specifically stabilizes the response gain of LGN neurons, thereby increasing their information coding capacity. Inspired by early work by McClurkin et al. (1994), we manipulated the activity of corticogeniculate neurons to test this hypothesis. We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. Together, these findings suggest that the influence of corticogeniculate feedback on LGN response precision and information coding capacity could be mediated through reductions in gain variability. [ABSTRACT FROM AUTHOR]

Published
2021
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41. Perceptual straightening of natural videos

Author

Olivier J, Hénaff, Robbe L T, Goris, and Eero P, Simoncelli

Subjects
Adult, Male, Young Adult, Visual Perception, Humans, Female
Abstract

Many behaviors rely on predictions derived from recent visual input, but the temporal evolution of those inputs is generally complex and difficult to extrapolate. We propose that the visual system transforms these inputs to follow straighter temporal trajectories. To test this 'temporal straightening' hypothesis, we develop a methodology for estimating the curvature of an internal trajectory from human perceptual judgments. We use this to test three distinct predictions: natural sequences that are highly curved in the space of pixel intensities should be substantially straighter perceptually; in contrast, artificial sequences that are straight in the intensity domain should be more curved perceptually; finally, naturalistic sequences that are straight in the intensity domain should be relatively less curved. Perceptual data validate all three predictions, as do population models of the early visual system, providing evidence that the visual system specifically straightens natural videos, offering a solution for tasks that rely on prediction.

Published
2017

42. Partitioning neuronal variability

Author

Eero P. Simoncelli, Robbe L. T. Goris, and J. Anthony Movshon

Subjects
Male, Action Potentials, Poisson process, Sensory system, Lateral geniculate nucleus, Macaque, Arousal, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, biology.animal, Biological neural network, Animals, Visual Pathways, Visual Cortex, 030304 developmental biology, Neurons, 0303 health sciences, biology, General Neuroscience, Macaca mulatta, Response Variability, Macaca fascicularis, symbols, Female, Macaca nemestrina, Adaptation, Psychology, Neuroscience, Photic Stimulation, 030217 neurology & neurosurgery
Abstract

Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT, revealing that variability originates in large part from excitability fluctuations that are correlated over time and between neurons, and that increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.

Published
2014
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43. Compound Stimuli Reveal the Structure of Visual Motion Selectivity in Macaque MT Neurons

Author

Robbe L. T. Goris, Eero P. Simoncelli, Andrew D Zaharia, and J. Anthony Movshon

Subjects
Male, Models, Neurological, Motion Perception, Texture (music), Macaque, 03 medical and health sciences, 0302 clinical medicine, Orientation, biology.animal, medicine, Animals, Visual Pathways, Invariant (mathematics), Visual Cortex, 030304 developmental biology, Neurons, Physics, Brain Mapping, 0303 health sciences, Erratum/Corrigendum, Quantitative Biology::Neurons and Cognition, biology, Orientation (computer vision), General Neuroscience, General Medicine, Macaca mulatta, Macaca fascicularis, Visual cortex, medicine.anatomical_structure, Receptive field, Frequency domain, Visual Perception, Female, Spatial frequency, Biological system, Photic Stimulation, 030217 neurology & neurosurgery
Abstract

Motion selectivity in primary visual cortex (V1) is approximately separable in orientation, spatial frequency, and temporal frequency (“frequency-separable”). Models for area MT neurons posit that their selectivity arises by combining direction-selective V1 afferents whose tuning is organized around a tilted plane in the frequency domain, specifying a particular direction and speed (“velocity-separable”). This construction explains “pattern direction-selective” MT neurons, which are velocity-selective but relatively invariant to spatial structure, including spatial frequency, texture and shape. We designed a set of experiments to distinguish frequency-separable and velocity-separable models and executed them with single-unit recordings in macaque V1 and MT. Surprisingly, when tested with single drifting gratings, most MT neurons’ responses are fit equally well by models with either form of separability. However, responses to plaids (sums of two moving gratings) tend to be better described as velocity-separable, especially for pattern neurons. We conclude that direction selectivity in MT is primarily computed by summing V1 afferents, but pattern-invariant velocity tuning for complex stimuli may arise from local, recurrent interactions.

Published
2019
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46. Slow gain fluctuations limit benefits of temporal integration in visual cortex

Author

Robbe L. T. Goris, J. Anthony Movshon, Eero P. Simoncelli, and Corey M. Ziemba

Subjects
computational modeling, Male, 0301 basic medicine, temporal modulation, decoding, Response model, Action Potentials, Sensory system, Poisson process, Stimulus (physiology), Macaque, Article, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Orientation, biology.animal, medicine, Animals, visual cortex, Mathematics, Neurons, temporal integration, biology, Macaca mulatta, Response Variability, Temporal Lobe, Sensory Systems, Form Perception, Ophthalmology, 030104 developmental biology, medicine.anatomical_structure, Visual cortex, Pattern Recognition, Visual, symbols, Neuron, Macaca nemestrina, Neuroscience, Photic Stimulation, 030217 neurology & neurosurgery
Abstract

Sensory neurons represent stimulus information with sequences of action potentials that differ across repeated measurements. This variability limits the information that can be extracted from momentary observations of a neuron's response. It is often assumed that integrating responses over time mitigates this limitation. However, temporal response correlations can reduce the benefits of temporal integration. We examined responses of individual orientation-selective neurons in the primary visual cortex of two macaque monkeys performing an orientation-discrimination task. The signal-to-noise ratio of temporally integrated responses increased for durations up to a few hundred milliseconds but saturated for longer durations. This was true even when cells exhibited little or no adaptation in their response levels. These observations are well explained by a statistical response model in which spikes arise from a Poisson process whose stimulus-dependent rate is modulated by slow, stimulus-independent fluctuations in gain. The response variability arising from the Poisson process is reduced by temporal integration, but the slow modulatory nature of variability due to gain fluctuations is not. Slow gain fluctuations therefore impose a fundamental limit on the benefits of temporal integration.

Published
2018
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50. A neural population model for visual pattern detection

Author

Johan Wagemans, T. Putzeys, Felix A. Wichmann, and Robbe L. T. Goris

Subjects
Cognitive science, education.field_of_study, Visual perception, Signal Detection, Psychological, media_common.quotation_subject, Population, Neurophysiology, Range (mathematics), Vision science, Visual cortex, medicine.anatomical_structure, Pattern Recognition, Visual, Perception, medicine, Humans, Neural Networks, Computer, Nerve Net, education, Set (psychology), Psychology, General Psychology, media_common
Abstract

Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear and independent visual channels whose output is linked to choice behavior in detection tasks via simple read-out mechanisms. This model has proven remarkably successful in accounting for threshold vision. It is fundamentally at odds, however, with current knowledge about the neurophysiological underpinnings of pattern vision. In addition, the principles put forward in the model fail to generalize to suprathreshold vision or perceptual tasks other than detection. We propose an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically inspired model of population activity in primary visual cortex. We demonstrate that this theory explains a broad range of classic detection results. With a single set of parameters, our model can account for several summation, adaptation, and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection.

Published
2013

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