1
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Malkin J, O'Donnell C, Houghton CJ, Aitchison L. Signatures of Bayesian inference emerge from energy-efficient synapses. eLife 2024; 12:RP92595. [PMID: 39106188 DOI: 10.7554/elife.92595] [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] [Indexed: 08/09/2024] Open
Abstract
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.
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Affiliation(s)
- James Malkin
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
| | - Cian O'Donnell
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
- Intelligent Systems Research Centre, School of Computing, Engineering, and Intelligent Systems, Ulster University, Derry/Londonderry, United Kingdom
| | - Conor J Houghton
- Faculty of Engineering, University of Bristol, Bristol, United Kingdom
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2
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Englitz B, Akram S, Elhilali M, Shamma S. Decoding contextual influences on auditory perception from primary auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.24.573229. [PMID: 38187523 PMCID: PMC10769425 DOI: 10.1101/2023.12.24.573229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-class-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch-class representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch-class and pitch-class-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.
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3
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Blanco Malerba S, Micheli A, Woodford M, Azeredo da Silveira R. Jointly efficient encoding and decoding in neural populations. PLoS Comput Biol 2024; 20:e1012240. [PMID: 38985828 PMCID: PMC11262701 DOI: 10.1371/journal.pcbi.1012240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/22/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data.
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Affiliation(s)
- Simone Blanco Malerba
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Aurora Micheli
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Michael Woodford
- Department of Economics, Columbia University, New York, New York, United States of America
| | - Rava Azeredo da Silveira
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Faculty of Science, University of Basel, Basel, Switzerland
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4
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Laamerad P, Liu LD, Pack CC. Decision-related activity and movement selection in primate visual cortex. SCIENCE ADVANCES 2024; 10:eadk7214. [PMID: 38809984 PMCID: PMC11135405 DOI: 10.1126/sciadv.adk7214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 04/24/2024] [Indexed: 05/31/2024]
Abstract
Fluctuations in the activity of sensory neurons often predict perceptual decisions. This connection can be quantified with a metric called choice probability (CP), and there is a longstanding debate about whether CP reflects a causal influence on decisions or an echo of decision-making activity elsewhere in the brain. Here, we show that CP can reflect a third variable, namely, the movement used to indicate the decision. In a standard visual motion discrimination task, neurons in the middle temporal (MT) area of primate cortex responded more strongly during trials that involved a saccade toward their receptive fields. This variability accounted for much of the CP observed across the neuronal population, and it arose through training. Moreover, pharmacological inactivation of MT biased behavioral responses away from the corresponding visual field locations. These results demonstrate that training on a task with fixed sensorimotor contingencies introduces movement-related activity in sensory brain regions and that this plasticity can shape the neural circuitry of perceptual decision-making.
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Affiliation(s)
- Pooya Laamerad
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Liu D. Liu
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
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5
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Fu J, Shrinivasan S, Baroni L, Ding Z, Fahey PG, Pierzchlewicz P, Ponder K, Froebe R, Ntanavara L, Muhammad T, Willeke KF, Wang E, Ding Z, Tran DT, Papadopoulos S, Patel S, Reimer J, Ecker AS, Pitkow X, Antolik J, Sinz FH, Haefner RM, Tolias AS, Franke K. Pattern completion and disruption characterize contextual modulation in the visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.13.532473. [PMID: 36993321 PMCID: PMC10054952 DOI: 10.1101/2023.03.13.532473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli - where the center and surround share the same orientation - are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in vivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.
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6
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Ichikawa K, Kaneko K. Bayesian inference is facilitated by modular neural networks with different time scales. PLoS Comput Biol 2024; 20:e1011897. [PMID: 38478575 PMCID: PMC10962854 DOI: 10.1371/journal.pcbi.1011897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/25/2024] [Accepted: 02/06/2024] [Indexed: 03/26/2024] Open
Abstract
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. Our findings reveal that networks with modular structures, composed of fast and slow modules, are adept at representing this prior distribution, enabling more accurate Bayesian inferences. Specifically, the modular network that consists of a main module connected with input and output layers and a sub-module with slower neural activity connected only with the main module outperformed networks with uniform time scales. Prior information was represented specifically by the slow sub-module, which could integrate observed signals over an appropriate period and represent input means and variances. Accordingly, the neural network could effectively predict the time-varying inputs. Furthermore, by training the time scales of neurons starting from networks with uniform time scales and without modular structure, the above slow-fast modular network structure and the division of roles in which prior knowledge is selectively represented in the slow sub-modules spontaneously emerged. These results explain how the prior distribution for Bayesian inference is represented in the brain, provide insight into the relevance of modular structure with time scale hierarchy to information processing, and elucidate the significance of brain areas with slower time scales.
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Affiliation(s)
- Kohei Ichikawa
- Department of Basic Science, Graduate School of Arts and Sciences, University of Tokyo, Meguro-ku, Tokyo, Japan
| | - Kunihiko Kaneko
- Research Center for Complex Systems Biology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej, Copenhagen, Denmark
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7
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Ziemba CM, Goris RLT, Stine GM, Perez RK, Simoncelli EP, Movshon JA. Neuronal and behavioral responses to naturalistic texture images in macaque monkeys. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581645. [PMID: 38464304 PMCID: PMC10925125 DOI: 10.1101/2024.02.22.581645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
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 macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between 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 compared with V1. The firing of both V1 and V2 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 second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys 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 thus unlikely to reflect a critical aspect of the formation of perceptual decisions. Significance statement As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.
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8
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Lange RD, Shivkumar S, Chattoraj A, Haefner RM. Bayesian encoding and decoding as distinct perspectives on neural coding. Nat Neurosci 2023; 26:2063-2072. [PMID: 37996525 PMCID: PMC11003438 DOI: 10.1038/s41593-023-01458-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 09/08/2023] [Indexed: 11/25/2023]
Abstract
The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.
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Affiliation(s)
- Richard D Lange
- Department of Neurobiology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA.
| | - Sabyasachi Shivkumar
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ankani Chattoraj
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
| | - Ralf M Haefner
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
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9
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Haimerl C, Ruff DA, Cohen MR, Savin C, Simoncelli EP. Targeted V1 comodulation supports task-adaptive sensory decisions. Nat Commun 2023; 14:7879. [PMID: 38036519 PMCID: PMC10689451 DOI: 10.1038/s41467-023-43432-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and flexible, task-specific readout. The former has been studied extensively, but the latter remains poorly understood. We introduce a theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We show that responses of neurons in area V1 of monkeys performing a visual discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in task-informative neurons and can be used to decode from neural activity after few training trials, consistent with observed behavior. In a simulated hierarchical neural network model, such labels are learned quickly and can be used to adapt downstream readout, even after several intervening processing stages. Consistently, we find the modulatory signal estimated in V1 is also present in the activity of simultaneously recorded MT units, and is again strongest in task-informative neurons. These results support the idea that co-modulation facilitates task-adaptive hierarchical information routing.
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Affiliation(s)
- Caroline Haimerl
- Center for Neural Science, New York University, New York, NY, 10003, USA.
- Champalimaud Centre for the Unknown, Lisbon, Portugal.
| | - Douglas A Ruff
- Department of Neurobiology, University of Chicago, Chicago, IL, 60637, US
| | - Marlene R Cohen
- Department of Neurobiology, University of Chicago, Chicago, IL, 60637, US
| | - Cristina Savin
- Center for Neural Science, New York University, New York, NY, 10003, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
| | - Eero P Simoncelli
- Center for Neural Science, New York University, New York, NY, 10003, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
- Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
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10
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Zhang WH, Wu S, Josić K, Doiron B. Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons. Nat Commun 2023; 14:7074. [PMID: 37925497 PMCID: PMC10625605 DOI: 10.1038/s41467-023-41743-3] [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: 01/31/2022] [Accepted: 09/15/2023] [Indexed: 11/06/2023] Open
Abstract
Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.
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Affiliation(s)
- Wen-Hao Zhang
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Si Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Center of Quantitative Biology, Peking University, Beijing, 100871, China
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, TX, USA.
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.
| | - Brent Doiron
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA.
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA.
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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11
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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. Nat Neurosci 2023; 26:1970-1980. [PMID: 37798412 DOI: 10.1038/s41593-023-01445-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 08/31/2023] [Indexed: 10/07/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here we provide evidence that the energy landscape around attractor basins in population neural activity in the prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays to reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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Affiliation(s)
- Siyu Wang
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Rossella Falcone
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Leo M. Davidoff Department of Neurological Surgery, Albert Einstein College of Medicine Montefiore Medical Center, Bronx, NY, USA
| | - Barry Richmond
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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12
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Lee HJ, Lee H, Lim CY, Rhim I, Lee SH. Corrective feedback guides human perceptual decision-making by informing about the world state rather than rewarding its choice. PLoS Biol 2023; 21:e3002373. [PMID: 37939126 PMCID: PMC10659185 DOI: 10.1371/journal.pbio.3002373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/20/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs "reinforcement learning," a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these "feedback-as-reward" and "feedback-as-evidence" hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.
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Affiliation(s)
- Hyang-Jung Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea
| | - Heeseung Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea
| | - Chae Young Lim
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Issac Rhim
- Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America
| | - Sang-Hun Lee
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea
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13
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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14
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Walker EY, Pohl S, Denison RN, Barack DL, Lee J, Block N, Ma WJ, Meyniel F. Studying the neural representations of uncertainty. Nat Neurosci 2023; 26:1857-1867. [PMID: 37814025 DOI: 10.1038/s41593-023-01444-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 08/30/2023] [Indexed: 10/11/2023]
Abstract
The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.
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Affiliation(s)
- Edgar Y Walker
- Department of Physiology and Biophysics, Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - Stephan Pohl
- Department of Philosophy, New York University, New York, NY, USA
| | - Rachel N Denison
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - David L Barack
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Department of Philosophy, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Lee
- Center for Neural Science, New York University, New York, NY, USA
| | - Ned Block
- Department of Philosophy, New York University, New York, NY, USA
| | - Wei Ji Ma
- Center for Neural Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.
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15
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Hajnal MA, Tran D, Einstein M, Martelo MV, Safaryan K, Polack PO, Golshani P, Orbán G. Continuous multiplexed population representations of task context in the mouse primary visual cortex. Nat Commun 2023; 14:6687. [PMID: 37865648 PMCID: PMC10590415 DOI: 10.1038/s41467-023-42441-w] [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: 03/10/2023] [Accepted: 10/10/2023] [Indexed: 10/23/2023] Open
Abstract
Effective task execution requires the representation of multiple task-related variables that determine how stimuli lead to correct responses. Even the primary visual cortex (V1) represents other task-related variables such as expectations, choice, and context. However, it is unclear how V1 can flexibly accommodate these variables without interfering with visual representations. We trained mice on a context-switching cross-modal decision task, where performance depends on inferring task context. We found that the context signal that emerged in V1 was behaviorally relevant as it strongly covaried with performance, independent from movement. Importantly, this signal was integrated into V1 representation by multiplexing visual and context signals into orthogonal subspaces. In addition, auditory and choice signals were also multiplexed as these signals were orthogonal to the context representation. Thus, multiplexing allows V1 to integrate visual inputs with other sensory modalities and cognitive variables to avoid interference with the visual representation while ensuring the maintenance of task-relevant variables.
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Affiliation(s)
- Márton Albert Hajnal
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Duy Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Albert Einstein College of Medicine, New York, NY, 10461, USA
| | - Michael Einstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Mauricio Vallejo Martelo
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Karen Safaryan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- West Los Angeles VA Medical Center, CA, 90073, Los Angeles, USA.
| | - Gergő Orbán
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary.
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16
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Sandhaeger F, Omejc N, Pape AA, Siegel M. Abstract perceptual choice signals during action-linked decisions in the human brain. PLoS Biol 2023; 21:e3002324. [PMID: 37816222 PMCID: PMC10564462 DOI: 10.1371/journal.pbio.3002324] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice, and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied a distinct representational space from motor signals. As expected from an internal decision variable, they were informed by the stimuli, and their strength predicted decision confidence and accuracy. Our results demonstrate abstract neural choice signals that generalize to action-linked decisions, suggesting a general role of an abstract choice stage in human decision-making.
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Affiliation(s)
- Florian Sandhaeger
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- MEG Center, University of Tübingen, Tübingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Nina Omejc
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- MEG Center, University of Tübingen, Tübingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Anna-Antonia Pape
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- MEG Center, University of Tübingen, Tübingen, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Markus Siegel
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- MEG Center, University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Tübingen, Germany
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17
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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.558139. [PMID: 37886489 PMCID: PMC10602028 DOI: 10.1101/2023.09.17.558139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here, we provide evidence that the energy landscape around attractor basins in population neural activity in prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays-to-reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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18
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Benjamin AS, Kording KP. A role for cortical interneurons as adversarial discriminators. PLoS Comput Biol 2023; 19:e1011484. [PMID: 37768890 PMCID: PMC10538760 DOI: 10.1371/journal.pcbi.1011484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/31/2023] [Indexed: 09/30/2023] Open
Abstract
The brain learns representations of sensory information from experience, but the algorithms by which it does so remain unknown. One popular theory formalizes representations as inferred factors in a generative model of sensory stimuli, meaning that learning must improve this generative model and inference procedure. This framework underlies many classic computational theories of sensory learning, such as Boltzmann machines, the Wake/Sleep algorithm, and a more recent proposal that the brain learns with an adversarial algorithm that compares waking and dreaming activity. However, in order for such theories to provide insights into the cellular mechanisms of sensory learning, they must be first linked to the cell types in the brain that mediate them. In this study, we examine whether a subtype of cortical interneurons might mediate sensory learning by serving as discriminators, a crucial component in an adversarial algorithm for representation learning. We describe how such interneurons would be characterized by a plasticity rule that switches from Hebbian plasticity during waking states to anti-Hebbian plasticity in dreaming states. Evaluating the computational advantages and disadvantages of this algorithm, we find that it excels at learning representations in networks with recurrent connections but scales poorly with network size. This limitation can be partially addressed if the network also oscillates between evoked activity and generative samples on faster timescales. Consequently, we propose that an adversarial algorithm with interneurons as discriminators is a plausible and testable strategy for sensory learning in biological systems.
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Affiliation(s)
- Ari S. Benjamin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Konrad P. Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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19
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Maes A, Barahona M, Clopath C. Long- and short-term history effects in a spiking network model of statistical learning. Sci Rep 2023; 13:12939. [PMID: 37558704 PMCID: PMC10412617 DOI: 10.1038/s41598-023-39108-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/20/2023] [Indexed: 08/11/2023] Open
Abstract
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.
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Affiliation(s)
- Amadeus Maes
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA.
- Department of Bioengineering, Imperial College London, London, UK.
| | | | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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20
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Alilović J, Lampers E, Slagter HA, van Gaal S. Illusory object recognition is either perceptual or cognitive in origin depending on decision confidence. PLoS Biol 2023; 21:e3002009. [PMID: 36862734 PMCID: PMC10013920 DOI: 10.1371/journal.pbio.3002009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/14/2023] [Accepted: 01/20/2023] [Indexed: 03/03/2023] Open
Abstract
We occasionally misinterpret ambiguous sensory input or report a stimulus when none is presented. It is unknown whether such errors have a sensory origin and reflect true perceptual illusions, or whether they have a more cognitive origin (e.g., are due to guessing), or both. When participants performed an error-prone and challenging face/house discrimination task, multivariate electroencephalography (EEG) analyses revealed that during decision errors (e.g., mistaking a face for a house), sensory stages of visual information processing initially represent the presented stimulus category. Crucially however, when participants were confident in their erroneous decision, so when the illusion was strongest, this neural representation flipped later in time and reflected the incorrectly reported percept. This flip in neural pattern was absent for decisions that were made with low confidence. This work demonstrates that decision confidence arbitrates between perceptual decision errors, which reflect true illusions of perception, and cognitive decision errors, which do not.
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Affiliation(s)
- Josipa Alilović
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Eline Lampers
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - Heleen A. Slagter
- Department of Applied and Experimental Psychology, Vrije Universiteit Amsterdam, the Netherlands
- Institute for Brain and Behavior, Vrije Universiteit Amsterdam, the Netherlands
| | - Simon van Gaal
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
- * E-mail:
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21
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van den Brink RL, Hagena K, Wilming N, Murphy PR, Büchel C, Donner TH. Flexible sensory-motor mapping rules manifest in correlated variability of stimulus and action codes across the brain. Neuron 2023; 111:571-584.e9. [PMID: 36476977 DOI: 10.1016/j.neuron.2022.11.009] [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: 04/12/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Humans and non-human primates can flexibly switch between different arbitrary mappings from sensation to action to solve a cognitive task. It has remained unknown how the brain implements such flexible sensory-motor mapping rules. Here, we uncovered a dynamic reconfiguration of task-specific correlated variability between sensory and motor brain regions. Human participants switched between two rules for reporting visual orientation judgments during fMRI recordings. Rule switches were either signaled explicitly or inferred by the participants from ambiguous cues. We used behavioral modeling to reconstruct the time course of their belief about the active rule. In both contexts, the patterns of correlations between ongoing fluctuations in stimulus- and action-selective activity across visual- and action-related brain regions tracked participants' belief about the active rule. The rule-specific correlation patterns broke down around the time of behavioral errors. We conclude that internal beliefs about task state are instantiated in brain-wide, selective patterns of correlated variability.
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Affiliation(s)
- Ruud L van den Brink
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Keno Hagena
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Niklas Wilming
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Peter R Murphy
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland; Department of Psychology, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Christian Büchel
- Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Tobias H Donner
- Computational Cognitive Neuroscience Section, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
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22
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The molecular memory code and synaptic plasticity: A synthesis. Biosystems 2023; 224:104825. [PMID: 36610586 DOI: 10.1016/j.biosystems.2022.104825] [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: 10/14/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023]
Abstract
The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.
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23
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Nurminen L, Bijanzadeh M, Angelucci A. Size tuning of neural response variability in laminar circuits of macaque primary visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.17.524397. [PMID: 36711786 PMCID: PMC9882156 DOI: 10.1101/2023.01.17.524397] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A defining feature of the cortex is its laminar organization, which is likely critical for cortical information processing. For example, visual stimuli of different size evoke distinct patterns of laminar activity. Visual information processing is also influenced by the response variability of individual neurons and the degree to which this variability is correlated among neurons. To elucidate laminar processing, we studied how neural response variability across the layers of macaque primary visual cortex is modulated by visual stimulus size. Our laminar recordings revealed that single neuron response variability and the shared variability among neurons are tuned for stimulus size, and this size-tuning is layer-dependent. In all layers, stimulation of the receptive field (RF) reduced single neuron variability, and the shared variability among neurons, relative to their pre-stimulus values. As the stimulus was enlarged beyond the RF, both single neuron and shared variability increased in supragranular layers, but either did not change or decreased in other layers. Surprisingly, we also found that small visual stimuli could increase variability relative to baseline values. Our results suggest multiple circuits and mechanisms as the source of variability in different layers and call for the development of new models of neural response variability.
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Affiliation(s)
- Lauri Nurminen
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
- Present address: College of Optometry, University of Houston, 4401 Martin Luther King Boulevard, Houston, TX 77204-2020, USA
| | - Maryam Bijanzadeh
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
| | - Alessandra Angelucci
- Department of Ophthalmology and Visual Science, Moran Eye Institute, University of Utah, 65 Mario Capecchi Drive, Salt Lake City, UT 84132, USA
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24
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Orlandi JG, Abdolrahmani M, Aoki R, Lyamzin DR, Benucci A. Distributed context-dependent choice information in mouse posterior cortex. Nat Commun 2023; 14:192. [PMID: 36635318 PMCID: PMC9837177 DOI: 10.1038/s41467-023-35824-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.
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Affiliation(s)
- Javier G Orlandi
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.,Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
| | | | - Ryo Aoki
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Dmitry R Lyamzin
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Andrea Benucci
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan. .,University of Tokyo, Graduate School of Information Science and Technology, Department of Mathematical Informatics, 1-1-1 Yayoi, Bunkyo City, Tokyo, 113-0032, Japan.
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25
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Efficient coding theory of dynamic attentional modulation. PLoS Biol 2022; 20:e3001889. [PMID: 36542662 PMCID: PMC9831638 DOI: 10.1371/journal.pbio.3001889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2023] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments.
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26
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Ashinoff BK, Buck J, Woodford M, Horga G. The effects of base rate neglect on sequential belief updating and real-world beliefs. PLoS Comput Biol 2022; 18:e1010796. [PMID: 36548395 PMCID: PMC9831339 DOI: 10.1371/journal.pcbi.1010796] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/10/2023] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel 'urn-and-beads' task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect-including noisier prior representation-correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias.
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Affiliation(s)
- Brandon K. Ashinoff
- Department of Psychiatry, Columbia University, New York, NY, United States of America
- New York State Psychiatric Institute (NYSPI), New York, NY, United States of America
| | - Justin Buck
- Department of Psychiatry, Columbia University, New York, NY, United States of America
- New York State Psychiatric Institute (NYSPI), New York, NY, United States of America
- Department of Neuroscience, Columbia University, New York, NY, United States of America
| | - Michael Woodford
- Department of Economics, Columbia University, New York, NY, United States of America
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, United States of America
- New York State Psychiatric Institute (NYSPI), New York, NY, United States of America
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27
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Bosten JM, Coen-Cagli R, Franklin A, Solomon SG, Webster MA. Calibrating Vision: Concepts and Questions. Vision Res 2022; 201:108131. [PMID: 37139435 PMCID: PMC10151026 DOI: 10.1016/j.visres.2022.108131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.
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Affiliation(s)
| | - Ruben Coen-Cagli
- Department of Systems Computational Biology, and Dominick P. Purpura Department of Neuroscience, and Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx NY
| | | | - Samuel G Solomon
- Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, UK
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28
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Peters MA. Towards characterizing the canonical computations generating phenomenal experience. Neurosci Biobehav Rev 2022; 142:104903. [DOI: 10.1016/j.neubiorev.2022.104903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/01/2022] [Indexed: 10/31/2022]
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29
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A neural correlate of perceptual segmentation in macaque middle temporal cortical area. Nat Commun 2022; 13:4967. [PMID: 36002445 PMCID: PMC9402536 DOI: 10.1038/s41467-022-32555-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 08/04/2022] [Indexed: 11/09/2022] Open
Abstract
High-resolution vision requires fine retinal sampling followed by integration to recover object properties. Importantly, accuracy is lost if local samples from different objects are intermixed. Thus, segmentation, grouping of image regions for separate processing, is crucial for perception. Previous work has used bi-stable plaid patterns, which can be perceived as either a single or multiple moving surfaces, to study this process. Here, we report a relationship between activity in a mid-level site in the primate visual pathways and segmentation judgments. Specifically, we find that direction selective middle temporal neurons are sensitive to texturing cues used to bias the perception of bi-stable plaids and exhibit a significant trial-by-trial correlation with subjective perception of a constant stimulus. This correlation is greater in units that signal global motion in patterns with multiple local orientations. Thus, we conclude the middle temporal area contains a signal for segmenting complex scenes into constituent objects and surfaces.
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30
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Qi Y, Gong P. Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits. Nat Commun 2022; 13:4572. [PMID: 35931698 PMCID: PMC9356069 DOI: 10.1038/s41467-022-32279-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions.
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Affiliation(s)
- Yang Qi
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia.
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Di Luzio P, Tarasi L, Silvanto J, Avenanti A, Romei V. Human perceptual and metacognitive decision-making rely on distinct brain networks. PLoS Biol 2022; 20:e3001750. [PMID: 35944012 PMCID: PMC9362930 DOI: 10.1371/journal.pbio.3001750] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 07/11/2022] [Indexed: 11/18/2022] Open
Abstract
Perceptual decisions depend on the ability to exploit available sensory information in order to select the most adaptive option from a set of alternatives. Such decisions depend on the perceptual sensitivity of the organism, which is generally accompanied by a corresponding level of certainty about the choice made. Here, by use of corticocortical paired associative transcranial magnetic stimulation protocol (ccPAS) aimed at inducing plastic changes, we shaped perceptual sensitivity and metacognitive ability in a motion discrimination task depending on the targeted network, demonstrating their functional dissociation. Neurostimulation aimed at boosting V5/MT+-to-V1/V2 back-projections enhanced motion sensitivity without impacting metacognition, whereas boosting IPS/LIP-to-V1/V2 back-projections increased metacognitive efficiency without impacting motion sensitivity. This double-dissociation provides causal evidence of distinct networks for perceptual sensitivity and metacognitive ability in humans.
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Affiliation(s)
- Paolo Di Luzio
- Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Cesena, Italy
| | - Luca Tarasi
- Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Cesena, Italy
| | - Juha Silvanto
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Alessio Avenanti
- Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Cesena, Italy
- Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica del Maule, Talca, Chile
| | - Vincenzo Romei
- Center for Studies and Research in Cognitive Neuroscience, University of Bologna, Cesena, Italy
- IRCCS Fondazione Santa Lucia, Rome, Italy
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32
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Rethinking delusions: A selective review of delusion research through a computational lens. Schizophr Res 2022; 245:23-41. [PMID: 33676820 PMCID: PMC8413395 DOI: 10.1016/j.schres.2021.01.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 02/06/2023]
Abstract
Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited 'jumping-to-conclusion' bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.
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Uran C, Peter A, Lazar A, Barnes W, Klon-Lipok J, Shapcott KA, Roese R, Fries P, Singer W, Vinck M. Predictive coding of natural images by V1 firing rates and rhythmic synchronization. Neuron 2022; 110:1240-1257.e8. [PMID: 35120628 PMCID: PMC8992798 DOI: 10.1016/j.neuron.2022.01.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 11/22/2021] [Accepted: 01/04/2022] [Indexed: 01/12/2023]
Abstract
Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for larger stimuli. Consequently, γ-synchronization was induced by natural images that are highly compressible and low-dimensional. Natural stimuli with low predictability induced prominent, late-onset beta (β)-synchronization, likely reflecting cortical feedback. Our findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.
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Affiliation(s)
- Cem Uran
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 AJ Nijmegen, the Netherlands.
| | - Alina Peter
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Andreea Lazar
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - William Barnes
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Max Planck Institute for Brain Research, 60438 Frankfurt, Germany
| | - Johanna Klon-Lipok
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Max Planck Institute for Brain Research, 60438 Frankfurt, Germany
| | - Katharine A Shapcott
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Frankfurt Institute for Advanced Studies, 60438 Frankfurt, Germany
| | - Rasmus Roese
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Pascal Fries
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Institute for Brain, Cognition and Behaviour, Department of Biophysics, Radboud University Nijmegen, 6525 AJ Nijmegen, the Netherlands
| | - Wolf Singer
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Max Planck Institute for Brain Research, 60438 Frankfurt, Germany; Frankfurt Institute for Advanced Studies, 60438 Frankfurt, Germany
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 AJ Nijmegen, the Netherlands.
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34
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Lange RD, Haefner RM. Task-induced neural covariability as a signature of approximate Bayesian learning and inference. PLoS Comput Biol 2022; 18:e1009557. [PMID: 35259152 PMCID: PMC8963539 DOI: 10.1371/journal.pcbi.1009557] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 03/29/2022] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function. Perceptual decision-making has classically been studied in the context of feedforward encoding/ decoding models. Here, we derive predictions for the responses of sensory neurons under the assumption that the brain performs hierarchical Bayesian inference, including feedback signals that communicate task-specific prior expectations. Interestingly, those predictions stand in contrast to some of the conclusions drawn in the classic framework. In particular, we find that Bayesian learning predicts the increase of a type of correlated variability called “differential correlations” over the course of learning. Differential correlations limit information, and hence are seen as harmful in feedforward models. Since our results are also specific to the statistics of a given task, and since they hold under a wide class of theories about how Bayesian probabilities may be represented by neural responses, they constitute a strong test of the Bayesian Brain hypothesis. Our results can explain the task-dependence of correlated variability in prior studies and suggest a reason why these kinds of correlations are surprisingly common in empirical data. Interpreted in a probabilistic framework, correlated variability provides a window into an observer’s task-related beliefs.
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Affiliation(s)
- Richard D. Lange
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Center for Visual Science, University of Rochester, Rochester, New York, United States of America
- * E-mail: (RDL); (RMH)
| | - Ralf M. Haefner
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Center for Visual Science, University of Rochester, Rochester, New York, United States of America
- * E-mail: (RDL); (RMH)
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35
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Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat Commun 2022; 13:1099. [PMID: 35232956 PMCID: PMC8888615 DOI: 10.1038/s41467-022-28552-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/19/2022] [Indexed: 12/19/2022] Open
Abstract
Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.
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36
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Zhang Q, Cramer SR, Ma Z, Turner KL, Gheres KW, Liu Y, Drew PJ, Zhang N. Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability. Cereb Cortex 2022; 32:5311-5329. [PMID: 35179203 PMCID: PMC9712744 DOI: 10.1093/cercor/bhac016] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/09/2022] [Accepted: 01/11/2022] [Indexed: 12/27/2022] Open
Abstract
A notorious issue of task-based functional magnetic resonance imaging (fMRI) is its large cross-trial variability. To quantitatively characterize this variability, the blood oxygenation level-dependent (BOLD) signal can be modeled as a linear summation of a stimulation-relevant and an ongoing (i.e. stimulation-irrelevant) component. However, systematic investigation on the spatiotemporal features of the ongoing BOLD component and how these features affect the BOLD response is still lacking. Here we measured fMRI responses to light onsets and light offsets in awake rats. The neuronal response was simultaneously recorded with calcium-based fiber photometry. We established that between-region BOLD signals were highly correlated brain-wide at zero time lag, including regions that did not respond to visual stimulation, suggesting that the ongoing activity co-fluctuates across the brain. Removing this ongoing activity reduced cross-trial variability of the BOLD response by ~30% and increased its coherence with the Ca2+ signal. Additionally, the negative ongoing BOLD activity sometimes dominated over the stimulation-driven response and contributed to the post-stimulation BOLD undershoot. These results suggest that brain-wide ongoing activity is responsible for significant cross-trial BOLD variability, and this component can be reliably quantified and removed to improve the reliability of fMRI response. Importantly, this method can be generalized to virtually all fMRI experiments without changing stimulation paradigms.
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Affiliation(s)
- Qingqing Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States
| | - Samuel R Cramer
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States,The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, United States
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States
| | - Kevin L Turner
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States
| | - Kyle W Gheres
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, PA 16802, United States
| | - Yikang Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States
| | - Patrick J Drew
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, United States,Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802, United States,The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, United States,Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, PA 16802, United States,Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, United States,Department of Neurosurgery, The Pennsylvania State University, Hershey, PA 17033, United States
| | - Nanyin Zhang
- Corresponding author: Biomedical Engineering and Electrical Engineering, Lloyd & Dorothy Foehr Huck Chair in Brain Imaging, The Huck Institutes of Life Sciences, The Pennsylvania State University, W-341 Millennium Science Complex, University Park, PA 16802, United States.
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37
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Echeveste R, Ferrante E, Milone DH, Samengo I. Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference. Netw Neurosci 2022; 6:196-212. [PMID: 36605888 PMCID: PMC9810278 DOI: 10.1162/netn_a_00219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/01/2021] [Indexed: 01/09/2023] Open
Abstract
Theories for autism spectrum disorder (ASD) have been formulated at different levels, ranging from physiological observations to perceptual and behavioral descriptions. Understanding the physiological underpinnings of perceptual traits in ASD remains a significant challenge in the field. Here we show how a recurrent neural circuit model that was optimized to perform sampling-based inference and displays characteristic features of cortical dynamics can help bridge this gap. The model was able to establish a mechanistic link between two descriptive levels for ASD: a physiological level, in terms of inhibitory dysfunction, neural variability, and oscillations, and a perceptual level, in terms of hypopriors in Bayesian computations. We took two parallel paths-inducing hypopriors in the probabilistic model, and an inhibitory dysfunction in the network model-which lead to consistent results in terms of the represented posteriors, providing support for the view that both descriptions might constitute two sides of the same coin.
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Affiliation(s)
- Rodrigo Echeveste
- Research Institute for Signals, Systems, and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Santa Fe, Argentina,* Corresponding Author:
| | - Enzo Ferrante
- Research Institute for Signals, Systems, and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Santa Fe, Argentina
| | - Diego H. Milone
- Research Institute for Signals, Systems, and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Santa Fe, Argentina
| | - Inés Samengo
- Medical Physics Department and Balseiro Institute (CNEA-UNCUYO/CONICET), Bariloche, Argentina
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38
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Ichikawa K, Kataoka A. Dynamical Mechanism of Sampling-Based Probabilistic Inference under Probabilistic Population Codes. Neural Comput 2022; 34:804-827. [PMID: 35026031 DOI: 10.1162/neco_a_01477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/04/2021] [Indexed: 11/04/2022]
Abstract
Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.
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Affiliation(s)
- Kohei Ichikawa
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-0041, Japan, and ACES, Bunkyo-ku, Tokyo-to 223-0034, Japan
| | - Asaki Kataoka
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-0041, Japan, and ACES, Bunkyo-ku, Tokyo-to 223-0034, Japan
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Abstract
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.
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40
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Tarasi L, Trajkovic J, Diciotti S, di Pellegrino G, Ferri F, Ursino M, Romei V. Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model. Neurosci Biobehav Rev 2021; 132:1-22. [PMID: 34774901 DOI: 10.1016/j.neubiorev.2021.11.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 12/14/2022]
Abstract
The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.
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Affiliation(s)
- Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy.
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Giuseppe di Pellegrino
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy; IRCCS Fondazione Santa Lucia, 00179 Rome, Italy.
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41
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Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Comput Biol 2021; 17:e1009517. [PMID: 34843452 PMCID: PMC8659691 DOI: 10.1371/journal.pcbi.1009517] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 12/09/2021] [Accepted: 10/01/2021] [Indexed: 11/18/2022] Open
Abstract
Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer's temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer's existing belief-a type of 'confirmation bias'. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.
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Affiliation(s)
- Richard D. Lange
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Computer Science, University of Rochester, Rochester, New York, United States of America
| | - Ankani Chattoraj
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
| | - Jeffrey M. Beck
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
| | - Jacob L. Yates
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
| | - Ralf M. Haefner
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Computer Science, University of Rochester, Rochester, New York, United States of America
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Schug S, Benzing F, Steger A. Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma. eLife 2021; 10:e69884. [PMID: 34661525 PMCID: PMC8716105 DOI: 10.7554/elife.69884] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/18/2021] [Indexed: 12/30/2022] Open
Abstract
When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.
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Affiliation(s)
- Simon Schug
- Institute of Neuroinformatics, University of Zurich & ETH ZurichZurichSwitzerland
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Wischnewski M, Peelen MV. Causal neural mechanisms of context-based object recognition. eLife 2021; 10:69736. [PMID: 34374647 PMCID: PMC8354632 DOI: 10.7554/elife.69736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/26/2021] [Indexed: 12/05/2022] Open
Abstract
Objects can be recognized based on their intrinsic features, including shape, color, and texture. In daily life, however, such features are often not clearly visible, for example when objects appear in the periphery, in clutter, or at a distance. Interestingly, object recognition can still be highly accurate under these conditions when objects are seen within their typical scene context. What are the neural mechanisms of context-based object recognition? According to parallel processing accounts, context-based object recognition is supported by the parallel processing of object and scene information in separate pathways. Output of these pathways is then combined in downstream regions, leading to contextual benefits in object recognition. Alternatively, according to feedback accounts, context-based object recognition is supported by (direct or indirect) feedback from scene-selective to object-selective regions. Here, in three pre-registered transcranial magnetic stimulation (TMS) experiments, we tested a key prediction of the feedback hypothesis: that scene-selective cortex causally and selectively supports context-based object recognition before object-selective cortex does. Early visual cortex (EVC), object-selective lateral occipital cortex (LOC), and scene-selective occipital place area (OPA) were stimulated at three time points relative to stimulus onset while participants categorized degraded objects in scenes and intact objects in isolation, in different trials. Results confirmed our predictions: relative to isolated object recognition, context-based object recognition was selectively and causally supported by OPA at 160–200 ms after onset, followed by LOC at 260–300 ms after onset. These results indicate that context-based expectations facilitate object recognition by disambiguating object representations in the visual cortex.
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Affiliation(s)
- Miles Wischnewski
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.,Department of Biomedical Engineering, University of Minnesota, Minneapolis, United States
| | - Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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Nassar MR, Scott D, Bhandari A. Noise Correlations for Faster and More Robust Learning. J Neurosci 2021; 41:6740-6752. [PMID: 34193556 PMCID: PMC8336712 DOI: 10.1523/jneurosci.3045-20.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/08/2021] [Accepted: 06/10/2021] [Indexed: 11/21/2022] Open
Abstract
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of an overall population signal-to-noise ratio to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning, whereas those across irrelevant feature dimensions slowed it. Our results complement the existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions.SIGNIFICANCE STATEMENT Positive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal-to-noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.
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Affiliation(s)
- Matthew R Nassar
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912-1821
| | - Daniel Scott
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912-1821
| | - Apoorva Bhandari
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02912-1821
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912-1821
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Quinn KR, Seillier L, Butts DA, Nienborg H. Decision-related feedback in visual cortex lacks spatial selectivity. Nat Commun 2021; 12:4473. [PMID: 34294703 PMCID: PMC8298450 DOI: 10.1038/s41467-021-24629-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/22/2021] [Indexed: 11/21/2022] Open
Abstract
Feedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity. Feedback modulates visual neurons, thought to help achieve flexible task performance. Here, the authors show decision-related feedback is not only relayed to task-relevant neurons, suggesting a broader mechanism and supporting a previously hypothesized link to feature-based attention.
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Affiliation(s)
| | | | - Daniel A Butts
- Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Hendrikje Nienborg
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
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46
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Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nat Neurosci 2021; 24:987-997. [PMID: 33903770 DOI: 10.1038/s41593-021-00839-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 03/12/2021] [Indexed: 02/02/2023]
Abstract
Many decisions under uncertainty entail the temporal accumulation of evidence that informs about the state of the environment. When environments are subject to hidden changes in their state, maximizing accuracy and reward requires non-linear accumulation of evidence. How this adaptive, non-linear computation is realized in the brain is unknown. We analyzed human behavior and cortical population activity (measured with magnetoencephalography) recorded during visual evidence accumulation in a changing environment. Behavior and decision-related activity in cortical regions involved in action planning exhibited hallmarks of adaptive evidence accumulation, which could also be implemented by a recurrent cortical microcircuit. Decision dynamics in action-encoding parietal and frontal regions were mirrored in a frequency-specific modulation of the state of the visual cortex that depended on pupil-linked arousal and the expected probability of change. These findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related feedback to the sensory cortex.
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Festa D, Aschner A, Davila A, Kohn A, Coen-Cagli R. Neuronal variability reflects probabilistic inference tuned to natural image statistics. Nat Commun 2021; 12:3635. [PMID: 34131142 PMCID: PMC8206154 DOI: 10.1038/s41467-021-23838-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 05/19/2021] [Indexed: 11/23/2022] Open
Abstract
Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses-their variability-can be explained by a probabilistic representation tuned to naturalistic inputs.
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Affiliation(s)
- Dylan Festa
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Amir Aschner
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Aida Davila
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.
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Stringer C, Michaelos M, Tsyboulski D, Lindo SE, Pachitariu M. High-precision coding in visual cortex. Cell 2021; 184:2767-2778.e15. [PMID: 33857423 DOI: 10.1016/j.cell.2021.03.042] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 03/04/2021] [Accepted: 03/19/2021] [Indexed: 01/18/2023]
Abstract
Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.
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Affiliation(s)
| | | | | | - Sarah E Lindo
- HHMI Janelia Research Campus, Ashburn, VA 20147, USA
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Chicharro D, Panzeri S, Haefner RM. Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife 2021; 10:e54858. [PMID: 33825683 PMCID: PMC8184215 DOI: 10.7554/elife.54858] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/06/2021] [Indexed: 01/16/2023] Open
Abstract
Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.
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Affiliation(s)
- Daniel Chicharro
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di TecnologiaRoveretoItaly
| | - Ralf M Haefner
- Brain and Cognitive Sciences, Center for Visual Science, University of RochesterRochesterUnited States
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50
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Dynamics of Heading and Choice-Related Signals in the Parieto-Insular Vestibular Cortex of Macaque Monkeys. J Neurosci 2021; 41:3254-3265. [PMID: 33622780 DOI: 10.1523/jneurosci.2275-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 01/20/2021] [Accepted: 02/17/2021] [Indexed: 02/06/2023] Open
Abstract
Perceptual decision-making is increasingly being understood to involve an interaction between bottom-up sensory-driven signals and top-down choice-driven signals, but how these signals interact to mediate perception is not well understood. The parieto-insular vestibular cortex (PIVC) is an area with prominent vestibular responsiveness, and previous work has shown that inactivating PIVC impairs vestibular heading judgments. To investigate the nature of PIVC's contribution to heading perception, we recorded extracellularly from PIVC neurons in two male rhesus macaques during a heading discrimination task, and compared findings with data from previous studies of dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas using identical stimuli. By computing partial correlations between neural responses, heading, and choice, we find that PIVC activity reflects a dynamically changing combination of sensory and choice signals. In addition, the sensory and choice signals are more balanced in PIVC, in contrast to the sensory dominance in MSTd and choice dominance in VIP. Interestingly, heading and choice signals in PIVC are negatively correlated during the middle portion of the stimulus epoch, reflecting a mismatch in the polarity of heading and choice signals. We anticipate that these results will help unravel the mechanisms of interaction between bottom-up sensory signals and top-down choice signals in perceptual decision-making, leading to more comprehensive models of self-motion perception.SIGNIFICANCE STATEMENT Vestibular information is important for our perception of self-motion, and various cortical regions in primates show vestibular heading selectivity. Inactivation of the macaque vestibular cortex substantially impairs the precision of vestibular heading discrimination, more so than inactivation of other multisensory areas. Here, we record for the first time from the vestibular cortex while monkeys perform a forced-choice heading discrimination task, and we compare results with data collected previously from other multisensory cortical areas. We find that vestibular cortex activity reflects a dynamically changing combination of sensory and choice signals, with both similarities and notable differences with other multisensory areas.
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