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Wang S, Jiang Q, Liu H, Yu C, Li P, Pan G, Xu K, Xiao R, Hao Y, Wang C, Song J. Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings. Proc Natl Acad Sci U S A 2024; 121:e2403380121. [PMID: 39331412 DOI: 10.1073/pnas.2403380121] [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: 02/17/2024] [Accepted: 09/03/2024] [Indexed: 09/28/2024] Open
Abstract
Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.
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Affiliation(s)
- Suhao Wang
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou 310027, China
| | - Qianqian Jiang
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Hang Liu
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Chaonan Yu
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
| | - Pengxian Li
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Gang Pan
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Kedi Xu
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Key Laboratory of Biomedical Engineering of Education Ministry, and Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, China
| | - Rui Xiao
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Yaoyao Hao
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Key Laboratory of Biomedical Engineering of Education Ministry, and Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, China
| | | | - Jizhou Song
- Department of Engineering Mechanics, Soft Matter Research Center, and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
- Nanhu Brain-Computer Interface Institute, Hangzhou 311100, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- Huanjiang Lab, Zhuji 311800, China
- Department of Rehabilitation Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
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2
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Li Y, Zhu X, Qi Y, Wang Y. Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals. eLife 2024; 12:RP87881. [PMID: 39120996 PMCID: PMC11315449 DOI: 10.7554/elife.87881] [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: 08/11/2024] Open
Abstract
In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.
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Affiliation(s)
- Yangang Li
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Xinyun Zhu
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Yu Qi
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
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3
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Cowley BR, Calhoun AJ, Rangarajan N, Ireland E, Turner MH, Pillow JW, Murthy M. Mapping model units to visual neurons reveals population code for social behaviour. Nature 2024; 629:1100-1108. [PMID: 38778103 PMCID: PMC11136655 DOI: 10.1038/s41586-024-07451-8] [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: 07/08/2022] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1-5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8-11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Adam J Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Elise Ireland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Maxwell H Turner
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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4
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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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5
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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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6
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Metzen MG, Chacron MJ. Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior. iScience 2023; 26:107139. [PMID: 37416462 PMCID: PMC10320509 DOI: 10.1016/j.isci.2023.107139] [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: 03/10/2023] [Revised: 04/25/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
The functional role of heterogeneous spiking responses of otherwise similarly tuned neurons to stimulation, which has been observed ubiquitously, remains unclear to date. Here, we demonstrate that such response heterogeneity serves a beneficial function that is used by downstream brain areas to generate behavioral responses that follows the detailed timecourse of the stimulus. Multi-unit recordings from sensory pyramidal cells within the electrosensory system of Apteronotus leptorhynchus were performed and revealed highly heterogeneous responses that were similar for all cell types. By comparing the coding properties of a given neural population before and after inactivation of descending pathways, we found that heterogeneities were beneficial as decoding was then more robust to the addition of noise. Taken together, our results not only reveal that descending pathways actively promote response heterogeneity within a given cell type, but also uncover a beneficial function for such heterogeneity that is used by the brain to generate behavior.
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Affiliation(s)
- Michael G. Metzen
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Maurice J. Chacron
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
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7
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Lamberti M, Tripathi S, van Putten MJAM, Marzen S, le Feber J. Prediction in cultured cortical neural networks. PNAS NEXUS 2023; 2:pgad188. [PMID: 37383023 PMCID: PMC10299080 DOI: 10.1093/pnasnexus/pgad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/18/2023] [Accepted: 05/25/2023] [Indexed: 06/30/2023]
Abstract
Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short- and long-term memory. To answer these questions, we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long-term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20 h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.
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Affiliation(s)
- Martina Lamberti
- Department of Clinical Neurophysiology, University of Twente, PO Box 217 7500AE, Enschede, The Netherlands
| | - Shiven Tripathi
- Department of Electrical Engineering, Indian Institute of Technology, Kanpur 208016, India
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, University of Twente, PO Box 217 7500AE, Enschede, The Netherlands
| | - Sarah Marzen
- W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA
| | - Joost le Feber
- Department of Clinical Neurophysiology, University of Twente, PO Box 217 7500AE, Enschede, The Netherlands
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8
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Haggard M, Chacron MJ. Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish. PLoS Comput Biol 2023; 19:e1010938. [PMID: 36867650 PMCID: PMC10016687 DOI: 10.1371/journal.pcbi.1010938] [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: 10/12/2022] [Revised: 03/15/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.
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Affiliation(s)
- Myriah Haggard
- Quantitative Life Sciences, McGill University, Montreal, Canada
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9
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Kuc R. Brain-inspired sensorimotor echolocation system for confident landmark recognition. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2022; 152:1272. [PMID: 36182295 DOI: 10.1121/10.0013833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/11/2022] [Indexed: 06/16/2023]
Abstract
A landmark is a familiar target in terms of the echoes that it can produce and is important for echolocation-based navigation by bats, robots, and blind humans. A brain-inspired system (BIS) achieves confident recognition, defined as classification to an arbitrarily small error probability (PE), by employing a voting process with an echo sequence. The BIS contains sensory neurons implemented with binary single-layer perceptrons trained to classify echo spectrograms with PE and generate excitatory and inhibitory votes in face neurons until a landmark-specific face neuron achieves recognition by reaching a confidence vote level (CVL). A discrete random step process models the vote count to show the recognition probability can achieve any desired accuracy by decreasing PE or increasing CVL. A hierarchical approach first classifies surface reflector and volume scatterer target categories and then uses that result to classify two subcategories that form four landmarks. The BIS models blind human echolocation to recognize four human-made and foliage landmarks by acquiring suitably sized and dense audible echo sequences. The sensorimotor BIS employs landmark-specific CVL values and a 2.7° view increment to acquire echo sequences that achieve zero-error recognition of each landmark independent of the initial view.
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Affiliation(s)
- Roman Kuc
- Department of Electrical Engineering and Wu Tsai Institute, Yale University, New Haven, Connecticut 06511, USA
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10
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Heller CR, David SV. Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data. PLoS One 2022; 17:e0271136. [PMID: 35862300 PMCID: PMC9302847 DOI: 10.1371/journal.pone.0271136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 06/23/2022] [Indexed: 11/30/2022] Open
Abstract
Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.
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Affiliation(s)
- Charles R. Heller
- Neuroscience Graduate Program, Oregon Health and Science University, Portland, Oregon, United States of America
- Oregon Hearing Research Center, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Stephen V. David
- Oregon Hearing Research Center, Oregon Health and Science University, Portland, Oregon, United States of America
- * E-mail:
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11
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Alefantis P, Lakshminarasimhan K, Avila E, Noel JP, Pitkow X, Angelaki DE. Sensory Evidence Accumulation Using Optic Flow in a Naturalistic Navigation Task. J Neurosci 2022; 42:5451-5462. [PMID: 35641186 PMCID: PMC9270913 DOI: 10.1523/jneurosci.2203-21.2022] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 04/01/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022] Open
Abstract
Sensory evidence accumulation is considered a hallmark of decision-making in noisy environments. Integration of sensory inputs has been traditionally studied using passive stimuli, segregating perception from action. Lessons learned from this approach, however, may not generalize to ethological behaviors like navigation, where there is an active interplay between perception and action. We designed a sensory-based sequential decision task in virtual reality in which humans and monkeys navigated to a memorized location by integrating optic flow generated by their own joystick movements. A major challenge in such closed-loop tasks is that subjects' actions will determine future sensory input, causing ambiguity about whether they rely on sensory input rather than expectations based solely on a learned model of the dynamics. To test whether subjects integrated optic flow over time, we used three independent experimental manipulations, unpredictable optic flow perturbations, which pushed subjects off their trajectory; gain manipulation of the joystick controller, which changed the consequences of actions; and manipulation of the optic flow density, which changed the information borne by sensory evidence. Our results suggest that both macaques (male) and humans (female/male) relied heavily on optic flow, thereby demonstrating a critical role for sensory evidence accumulation during naturalistic action-perception closed-loop tasks.SIGNIFICANCE STATEMENT The temporal integration of evidence is a fundamental component of mammalian intelligence. Yet, it has traditionally been studied using experimental paradigms that fail to capture the closed-loop interaction between actions and sensations inherent in real-world continuous behaviors. These conventional paradigms use binary decision tasks and passive stimuli with statistics that remain stationary over time. Instead, we developed a naturalistic visuomotor visual navigation paradigm that mimics the causal structure of real-world sensorimotor interactions and probed the extent to which participants integrate sensory evidence by adding task manipulations that reveal complementary aspects of the computation.
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Affiliation(s)
- Panos Alefantis
- Center for Neural Science, New York University, New York, New York 10003
| | | | - Eric Avila
- Center for Neural Science, New York University, New York, New York 10003
| | - Jean-Paul Noel
- Center for Neural Science, New York University, New York, New York 10003
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005-1892
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030
| | - Dora E Angelaki
- Center for Neural Science, New York University, New York, New York 10003
- Tandon School of Engineering, New York University, New York, New York 11201
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12
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Ni AM, Huang C, Doiron B, Cohen MR. A general decoding strategy explains the relationship between behavior and correlated variability. eLife 2022; 11:67258. [PMID: 35660134 PMCID: PMC9170243 DOI: 10.7554/elife.67258] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Abstract
Improvements in perception are frequently accompanied by decreases in correlated variability in sensory cortex. This relationship is puzzling because overall changes in correlated variability should minimally affect optimal information coding. We hypothesize that this relationship arises because instead of using optimal strategies for decoding the specific stimuli at hand, observers prioritize generality: a single set of neuronal weights to decode any stimuli. We tested this using a combination of multineuron recordings in the visual cortex of behaving rhesus monkeys and a cortical circuit model. We found that general decoders optimized for broad rather than narrow sets of visual stimuli better matched the animals’ decoding strategy, and that their performance was more related to the magnitude of correlated variability. In conclusion, the inverse relationship between perceptual performance and correlated variability can be explained by observers using a general decoding strategy, capable of decoding neuronal responses to the variety of stimuli encountered in natural vision.
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Affiliation(s)
- Amy M Ni
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
| | - Chengcheng Huang
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Brent Doiron
- Center for the Neural Basis of Cognition, Pittsburgh, United States.,Department of Mathematics, University of Pittsburgh, Pittsburgh, United States
| | - Marlene R Cohen
- Department of Neuroscience,University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, Pittsburgh, United States
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13
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Lin CHS, Garrido MI. Towards a cross-level understanding of Bayesian inference in the brain. Neurosci Biobehav Rev 2022; 137:104649. [PMID: 35395333 DOI: 10.1016/j.neubiorev.2022.104649] [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: 10/17/2021] [Revised: 02/28/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr's cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.
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Affiliation(s)
- Chin-Hsuan Sophie Lin
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia; Australian Research Council for Integrative Brain Function, Australia.
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia; Australian Research Council for Integrative Brain Function, Australia
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14
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Kim HR, Angelaki DE, DeAngelis GC. A neural mechanism for detecting object motion during self-motion. eLife 2022; 11:74971. [PMID: 35642599 PMCID: PMC9159750 DOI: 10.7554/elife.74971] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 05/17/2022] [Indexed: 11/17/2022] Open
Abstract
Detection of objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque middle temporal (MT) area with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion, and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.
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Affiliation(s)
- HyungGoo R Kim
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.,Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, United States.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Dora E Angelaki
- Center for Neural Science, New York University, New York, United States
| | - Gregory C DeAngelis
- Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, United States
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15
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Anbuhl KL, Yao JD, Hotz RA, Mowery TM, Sanes DH. Auditory processing remains sensitive to environmental experience during adolescence in a rodent model. Nat Commun 2022; 13:2872. [PMID: 35610222 PMCID: PMC9130260 DOI: 10.1038/s41467-022-30455-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/02/2022] [Indexed: 11/09/2022] Open
Abstract
Elevated neural plasticity during development contributes to dramatic improvements in perceptual, motor, and cognitive skills. However, malleable neural circuits are vulnerable to environmental influences that may disrupt behavioral maturation. While these risks are well-established prior to sexual maturity (i.e., critical periods), the degree of neural vulnerability during adolescence remains uncertain. Here, we induce transient hearing loss (HL) spanning adolescence in gerbils, and ask whether behavioral and neural maturation are disrupted. We find that adolescent HL causes a significant perceptual deficit that can be attributed to degraded auditory cortex processing, as assessed with wireless single neuron recordings and within-session population-level analyses. Finally, auditory cortex brain slices from adolescent HL animals reveal synaptic deficits that are distinct from those typically observed after critical period deprivation. Taken together, these results show that diminished adolescent sensory experience can cause long-lasting behavioral deficits that originate, in part, from a dysfunctional cortical circuit.
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Affiliation(s)
- Kelsey L Anbuhl
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA.
| | - Justin D Yao
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA
| | - Robert A Hotz
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA
| | - Todd M Mowery
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA
- Department of Otolaryngology, Rutgers University, New Brunswick, NJ, USA
| | - Dan H Sanes
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA.
- Department of Psychology, New York University, New York, NY, USA.
- Department of Biology, New York University, New York, NY, USA.
- Neuroscience Institute at NYU Langone School of Medicine, New York, NY, USA.
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16
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McFadyen JR, Heider B, Karkhanis AN, Cloherty SL, Muñoz F, Siegel RM, Morris AP. Robust Coding of Eye Position in Posterior Parietal Cortex despite Context-Dependent Tuning. J Neurosci 2022; 42:4116-4130. [PMID: 35410881 PMCID: PMC9121829 DOI: 10.1523/jneurosci.0674-21.2022] [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: 03/30/2021] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/21/2022] Open
Abstract
Neurons in posterior parietal cortex (PPC) encode many aspects of the sensory world (e.g., scene structure), the posture of the body, and plans for action. For a downstream computation, however, only some of these dimensions are relevant; the rest are "nuisance variables" because their influence on neural activity changes with sensory and behavioral context, potentially corrupting the read-out of relevant information. Here we show that a key postural variable for vision (eye position) is represented robustly in male macaque PPC across a range of contexts, although the tuning of single neurons depended strongly on context. Contexts were defined by different stages of a visually guided reaching task, including (1) a visually sparse epoch, (2) a visually rich epoch, (3) a "go" epoch in which the reach was cued, and (4) during the reach itself. Eye position was constant within trials but varied across trials in a 3 × 3 grid spanning 24° × 24°. Using demixed principal component analysis of neural spike-counts, we found that the subspace of the population response encoding eye position is orthogonal to that encoding task context. Accordingly, a context-naive (fixed-parameter) decoder was nevertheless able to estimate eye position reliably across contexts. Errors were small given the sample size (∼1.78°) and would likely be even smaller with larger populations. Moreover, they were comparable to that of decoders that were optimized for each context. Our results suggest that population codes in PPC shield encoded signals from crosstalk to support robust sensorimotor transformations across contexts.SIGNIFICANCE STATEMENT Neurons in posterior parietal cortex (PPC) which are sensitive to gaze direction are thought to play a key role in spatial perception and behavior (e.g., reaching, navigation), and provide a potential substrate for brain-controlled prosthetics. Many, however, change their tuning under different sensory and behavioral contexts, raising the prospect that they provide unreliable representations of egocentric space. Here, we analyze the structure of encoding dimensions for gaze direction and context in PPC during different stages of a visually guided reaching task. We use demixed dimensionality reduction and decoding techniques to show that the coding of gaze direction in PPC is mostly invariant to context. This suggests that PPC can provide reliable spatial information across sensory and behavioral contexts.
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Affiliation(s)
- Jamie R McFadyen
- Neuroscience Program, Biomedicine Discovery Institute, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Barbara Heider
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Anushree N Karkhanis
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Shaun L Cloherty
- School of Engineering, RMIT University, Melbourne, VIC, 3001, Australia
| | - Fabian Muñoz
- Department of Neuroscience, Columbia University, New York, NY, 10027
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Ralph M Siegel
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102
| | - Adam P Morris
- Neuroscience Program, Biomedicine Discovery Institute, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
- Monash Data Futures Institute, Monash University, Clayton, VIC, 3800, Australia
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17
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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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18
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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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19
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Steymans I, Pujol-Lereis LM, Brembs B, Gorostiza EA. Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLoS One 2021; 16:e0256560. [PMID: 34437617 PMCID: PMC8389364 DOI: 10.1371/journal.pone.0256560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/09/2021] [Indexed: 11/22/2022] Open
Abstract
Our own unique character traits make our behavior consistent and define our individuality. Yet, this consistency does not entail that we behave repetitively like machines. Like humans, animals also combine personality traits with spontaneity to produce adaptive behavior: consistent, but not fully predictable. Here, we study an iconically rigid behavioral trait, insect phototaxis, that nevertheless also contains both components of individuality and spontaneity. In a light/dark T-maze, approximately 70% of a group of Drosophila fruit flies choose the bright arm of the T-Maze, while the remaining 30% walk into the dark. Taking the photopositive and the photonegative subgroups and re-testing them reveals the spontaneous component: a similar 70–30 distribution emerges in each of the two subgroups. Increasing the number of choices to ten choices, reveals the individuality component: flies with an extremely negative series of first choices were more likely to show photonegative behavior in subsequent choices and vice versa. General behavioral traits, independent of light/dark preference, contributed to the development of this individuality. The interaction of individuality and spontaneity together explains why group averages, even for such seemingly stereotypical behaviors, are poor predictors of individual choices.
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Affiliation(s)
- Isabelle Steymans
- Institut für Zoologie - Neurogenetik, Universität Regensburg, Regensburg, Germany
| | - Luciana M. Pujol-Lereis
- Laboratory of Amyloidosis and Neurodegeneration, Fundación Instituto Leloir, IIBBA, CONICET, Buenos Aires, Argentina
| | - Björn Brembs
- Institut für Zoologie - Neurogenetik, Universität Regensburg, Regensburg, Germany
- * E-mail: (EAG); (BB)
| | - E. Axel Gorostiza
- Institut für Zoologie - Neurogenetik, Universität Regensburg, Regensburg, Germany
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE) CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
- * E-mail: (EAG); (BB)
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20
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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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21
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Valente M, Pica G, Bondanelli G, Moroni M, Runyan CA, Morcos AS, Harvey CD, Panzeri S. Correlations enhance the behavioral readout of neural population activity in association cortex. Nat Neurosci 2021; 24:975-986. [PMID: 33986549 PMCID: PMC8559600 DOI: 10.1038/s41593-021-00845-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 03/24/2021] [Indexed: 02/03/2023]
Abstract
Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.
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Affiliation(s)
- Martina Valente
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Centro Interdisciplinare Mente e Cervello (CIMeC), University of Trento, Rovereto, Italy
| | - Giuseppe Pica
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Giulio Bondanelli
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Monica Moroni
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
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22
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Wang Z, Chacron MJ. Synergistic population coding of natural communication stimuli by hindbrain electrosensory neurons. Sci Rep 2021; 11:10840. [PMID: 34035395 PMCID: PMC8149419 DOI: 10.1038/s41598-021-90413-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 05/11/2021] [Indexed: 01/11/2023] Open
Abstract
Understanding how neural populations encode natural stimuli with complex spatiotemporal structure to give rise to perception remains a central problem in neuroscience. Here we investigated population coding of natural communication stimuli by hindbrain neurons within the electrosensory system of weakly electric fish Apteronotus leptorhynchus. Overall, we found that simultaneously recorded neural activities were correlated: signal but not noise correlations were variable depending on the stimulus waveform as well as the distance between neurons. Combining the neural activities using an equal-weight sum gave rise to discrimination performance between different stimulus waveforms that was limited by redundancy introduced by noise correlations. However, using an evolutionary algorithm to assign different weights to individual neurons before combining their activities (i.e., a weighted sum) gave rise to increased discrimination performance by revealing synergistic interactions between neural activities. Our results thus demonstrate that correlations between the neural activities of hindbrain electrosensory neurons can enhance information about the structure of natural communication stimuli that allow for reliable discrimination between different waveforms by downstream brain areas.
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Affiliation(s)
- Ziqi Wang
- Department of Physiology, McGill University, Montreal, Canada
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23
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Smith JET, Parker AJ. Correlated structure of neuronal firing in macaque visual cortex limits information for binocular depth discrimination. J Neurophysiol 2021; 126:275-303. [PMID: 33978495 PMCID: PMC8325604 DOI: 10.1152/jn.00667.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Variability in cortical neural activity potentially limits sensory discriminations. Theoretical work shows that information required to discriminate two similar stimuli is limited by the correlation structure of cortical variability. We investigated these information-limiting correlations by recording simultaneously from visual cortical areas primary visual cortex (V1) and extrastriate area V4 in macaque monkeys performing a binocular, stereo depth discrimination task. Within both areas, noise correlations on a rapid temporal scale (20–30 ms) were stronger for neuron pairs with similar selectivity for binocular depth, meaning that these correlations potentially limit information for making the discrimination. Between-area correlations (V1 to V4) were different, being weaker for neuron pairs with similar tuning and having a slower temporal scale (100+ ms). Fluctuations in these information-limiting correlations just prior to the detection event were associated with changes in behavioral accuracy. Although these correlations limit the recovery of information about sensory targets, their impact may be curtailed by integrative processing of signals across multiple brain areas. NEW & NOTEWORTHY Correlated noise reduces the stimulus information in visual cortical neurons during experimental performance of binocular depth discriminations. The temporal scale of these correlations is important. Rapid (20–30 ms) correlations reduce information within and between areas V1 and V4, whereas slow (>100 ms) correlations between areas do not. Separate cortical areas appear to act together to maintain signal fidelity. Rapid correlations reduce the neuronal signal difference between stimuli and adversely affect perceptual discrimination.
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Affiliation(s)
- Jackson E T Smith
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Andrew J Parker
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
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24
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Metzen MG, Chacron MJ. Population Coding of Natural Electrosensory Stimuli by Midbrain Neurons. J Neurosci 2021; 41:3822-3841. [PMID: 33687962 PMCID: PMC8084312 DOI: 10.1523/jneurosci.2232-20.2021] [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: 08/25/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022] Open
Abstract
Natural stimuli display spatiotemporal characteristics that typically vary over orders of magnitude, and their encoding by sensory neurons remains poorly understood. We investigated population coding of highly heterogeneous natural electrocommunication stimuli in Apteronotus leptorhynchus of either sex. Neuronal activities were positively correlated with one another in the absence of stimulation, and correlation magnitude decayed with increasing distance between recording sites. Under stimulation, we found that correlations between trial-averaged neuronal responses (i.e., signal correlations) were positive and higher in magnitude for neurons located close to another, but that correlations between the trial-to-trial variability (i.e., noise correlations) were independent of physical distance. Overall, signal and noise correlations were independent of stimulus waveform as well as of one another. To investigate how neuronal populations encoded natural electrocommunication stimuli, we considered a nonlinear decoder for which the activities were combined. Decoding performance was best for a timescale of 6 ms, indicating that midbrain neurons transmit information via precise spike timing. A simple summation of neuronal activities (equally weighted sum) revealed that noise correlations limited decoding performance by introducing redundancy. Using an evolution algorithm to optimize performance when considering instead unequally weighted sums of neuronal activities revealed much greater performance values, indicating that midbrain neuron populations transmit information that reliably enable discrimination between different stimulus waveforms. Interestingly, we found that different weight combinations gave rise to similar discriminability, suggesting robustness. Our results have important implications for understanding how natural stimuli are integrated by downstream brain areas to give rise to behavioral responses.SIGNIFICANCE STATEMENT We show that midbrain electrosensory neurons display correlations between their activities and that these can significantly impact performance of decoders. While noise correlations limited discrimination performance by introducing redundancy, considering unequally weighted sums of neuronal activities gave rise to much improved performance and mitigated the deleterious effects of noise correlations. Further analysis revealed that increased discriminability was achieved by making trial-averaged responses more separable, as well as by reducing trial-to-trial variability by eliminating noise correlations. We further found that multiple combinations of weights could give rise to similar discrimination performances, which suggests that such combinatorial codes could be achieved in the brain. We conclude that the activities of midbrain neuronal populations can be used to reliably discriminate between highly heterogeneous stimulus waveforms.
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Affiliation(s)
- Michael G Metzen
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
| | - Maurice J Chacron
- Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
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25
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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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26
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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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27
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Dehaene GP, Coen-Cagli R, Pouget A. Investigating the representation of uncertainty in neuronal circuits. PLoS Comput Biol 2021; 17:e1008138. [PMID: 33577553 PMCID: PMC7880493 DOI: 10.1371/journal.pcbi.1008138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 07/09/2020] [Indexed: 11/24/2022] Open
Abstract
Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes’ rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes’ rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty. In order to optimize their behavior, animals must continuously represent the uncertainty associated with their beliefs. Understanding the neural code for this uncertainty is a pressing and critical issue in neuroscience. Following a long tradition, some studies have investigated this code by measuring how average statistics of neural responses (like the tuning curves) correlate with uncertainty as stimulus characteristics are varied. We show that this approach can be very misleading. An alternative consists in decoding the neuronal responses to recover the posterior distribution over the encoded sensory variables and using the variance of this distribution as the measure of uncertainty. We demonstrate that this decoding approach can indeed avoid the pitfalls of the traditional approach, while leading to more accurate estimates of uncertainty.
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Affiliation(s)
- Guillaume P Dehaene
- University of Geneva, Département des neurosciences fondamentales, Geneva, Switzerland
| | - Ruben Coen-Cagli
- University of Geneva, Département des neurosciences fondamentales, Geneva, Switzerland.,Albert Einstein College of Medicine, Bronx, Department of Systems & Computational Biology & Department of Neuroscience, New York, United States of America
| | - Alexandre Pouget
- University of Geneva, Département des neurosciences fondamentales, Geneva, Switzerland.,Gatsby Computational Neuroscience Unit, London, United Kingdom
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28
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Kafashan M, Jaffe AW, Chettih SN, Nogueira R, Arandia-Romero I, Harvey CD, Moreno-Bote R, Drugowitsch J. Scaling of sensory information in large neural populations shows signatures of information-limiting correlations. Nat Commun 2021; 12:473. [PMID: 33473113 PMCID: PMC7817840 DOI: 10.1038/s41467-020-20722-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 12/16/2020] [Indexed: 01/29/2023] Open
Abstract
How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.
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Affiliation(s)
| | - Anna W Jaffe
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Iñigo Arandia-Romero
- ISAAC Lab, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
- IAS-Research Center for Life, Mind, and Society, Department of Logic and Philosophy of Science, University of the Basque Country, UPV-EHU, Donostia-San Sebastián, Spain
| | | | - Rubén Moreno-Bote
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Serra Húnter Fellow Programme and ICREA Academia, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA.
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29
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Krishna A, Tanabe S, Kohn A. Decision Signals in the Local Field Potentials of Early and Mid-Level Macaque Visual Cortex. Cereb Cortex 2021; 31:169-183. [PMID: 32852540 PMCID: PMC7727373 DOI: 10.1093/cercor/bhaa218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 06/12/2020] [Accepted: 07/14/2020] [Indexed: 12/28/2022] Open
Abstract
The neural basis of perceptual decision making has typically been studied using measurements of single neuron activity, though decisions are likely based on the activity of large neuronal ensembles. Local field potentials (LFPs) may, in some cases, serve as a useful proxy for population activity and thus be useful for understanding the neural basis of perceptual decision making. However, little is known about whether LFPs in sensory areas include decision-related signals. We therefore analyzed LFPs recorded using two 48-electrode arrays implanted in primary visual cortex (V1) and area V4 of macaque monkeys trained to perform a fine orientation discrimination task. We found significant choice information in low (0-30 Hz) and higher (70-500 Hz) frequency components of the LFP, but little information in gamma frequencies (30-70 Hz). Choice information was more robust in V4 than V1 and stronger in LFPs than in simultaneously measured spiking activity. LFP-based choice information included a global component, common across electrodes within an area. Our findings reveal the presence of robust choice-related signals in the LFPs recorded in V1 and V4 and suggest that LFPs may be a useful complement to spike-based analyses of decision making.
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Affiliation(s)
- Aravind Krishna
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Bioengineering, School of Chemical and Biotechnology, SASTRA University, Thanjavur 613401, India
| | - Seiji Tanabe
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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30
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Kim C, Chacron MJ. Lower Baseline Variability Gives Rise to Lower Detection Thresholds in Midbrain than Hindbrain Electrosensory Neurons. Neuroscience 2020; 448:43-54. [PMID: 32926952 DOI: 10.1016/j.neuroscience.2020.09.011] [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: 04/23/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 10/23/2022]
Abstract
Understanding how the brain decodes sensory information to give rise to behaviour remains an important problem in systems neuroscience. Across various sensory modalities (e.g. auditory, visual), the time-varying contrast of natural stimuli has been shown to carry behaviourally relevant information. However, it is unclear how such information is actually decoded by the brain to evoke perception and behaviour. Here we investigated how midbrain electrosensory neurons respond to weak contrasts in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We found that these neurons displayed lower detection thresholds than their afferent hindbrain electrosensory neurons. Further analysis revealed that the lower detection thresholds of midbrain neurons were not due to increased sensitivity to the stimulus. Rather, these were due to the fact that midbrain neurons displayed lower variability in their firing activities in the absence of stimulation, which is due to lower firing rates. Our results suggest that midbrain neurons play an active role towards enabling the detection of weak stimulus contrasts, which in turn leads to perception and behavioral responses.
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Affiliation(s)
- Chelsea Kim
- Department of Physiology, McGill University, Montreal, QC, Canada
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31
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Keitel A, Gross J, Kayser C. Shared and modality-specific brain regions that mediate auditory and visual word comprehension. eLife 2020; 9:e56972. [PMID: 32831168 PMCID: PMC7470824 DOI: 10.7554/elife.56972] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022] Open
Abstract
Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants' trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld.
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Affiliation(s)
- Anne Keitel
- Psychology, University of DundeeDundeeUnited Kingdom
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
| | - Christoph Kayser
- Department for Cognitive Neuroscience, Faculty of Biology, Bielefeld UniversityBielefeldGermany
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32
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Krug K. Coding Perceptual Decisions: From Single Units to Emergent Signaling Properties in Cortical Circuits. Annu Rev Vis Sci 2020; 6:387-409. [PMID: 32600168 DOI: 10.1146/annurev-vision-030320-041223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.
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Affiliation(s)
- Kristine Krug
- Lehrstuhl für Sensorische Physiologie, Institut für Biologie, Otto-von-Guericke-Universität Magdeburg, 39120 Magdeburg, Germany; .,Leibniz-Institut für Neurobiologie, 39118 Magdeburg, Germany.,Department of Physiology, Anatomy, and Genetics, Oxford University, Oxford OX1 3PT, United Kingdom
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33
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Gupta P, Balasubramaniam N, Chang HY, Tseng FG, Santra TS. A Single-Neuron: Current Trends and Future Prospects. Cells 2020; 9:E1528. [PMID: 32585883 PMCID: PMC7349798 DOI: 10.3390/cells9061528] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/15/2020] [Accepted: 06/19/2020] [Indexed: 12/11/2022] Open
Abstract
The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. Further, the development in the field of artificial intelligence in relation to single-neurons is highlighted. The review concludes with between limitations and future prospects of single-neuron analyses.
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Affiliation(s)
- Pallavi Gupta
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
| | - Nandhini Balasubramaniam
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
| | - Hwan-You Chang
- Department of Medical Science, National Tsing Hua University, Hsinchu 30013, Taiwan;
| | - Fan-Gang Tseng
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan;
| | - Tuhin Subhra Santra
- Department of Engineering Design, Indian Institute of Technology Madras, Tamil Nadu 600036, India; (P.G.); (N.B.)
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34
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The Correlation of Neuronal Signals with Behavior at Different Levels of Visual Cortex and Their Relative Reliability for Behavioral Decisions. J Neurosci 2020; 40:3751-3767. [PMID: 32273483 DOI: 10.1523/jneurosci.2587-19.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/22/2020] [Accepted: 03/12/2020] [Indexed: 11/21/2022] Open
Abstract
Behavior can be guided by neuronal activity in visual, auditory, or somatosensory cerebral cortex, depending on task requirements. In contrast to this flexible access of cortical signals, several observations suggest that behaviors depend more on neurons in later areas of visual cortex than those in earlier areas, although neurons in earlier areas would provide more reliable signals for many tasks. We recorded from neurons in different levels of visual cortex of 2 male rhesus monkeys while the animals did a visual discrimination task and examined trial-to-trial correlations between neuronal and behavioral responses. These correlations became stronger in primary visual cortex as neuronal signals in that area became more reliable relative to the other areas. The results suggest that the mechanisms that read signals from cortex might access any cortical area depending on the relative value of those signals for the task at hand.SIGNIFICANCE STATEMENT Information is encoded by the action potentials of neurons in various cortical areas in a hierarchical manner such that increasingly complex stimulus features are encoded in successive stages. The brain must extract information from the response of appropriate neurons to drive optimal behavior. A widely held view of this decoding process is that the brain relies on the output of later cortical areas to make decisions, although neurons in earlier areas can provide more reliable signals. We examined correlations between perceptual decisions and the responses of neurons in different levels of monkey visual cortex. The results suggest that the brain may access signals in any cortical area depending on the relative value of those signals for the task at hand.
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35
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Zhao Y, Yates JL, Levi AJ, Huk AC, Park IM. Stimulus-choice (mis)alignment in primate area MT. PLoS Comput Biol 2020; 16:e1007614. [PMID: 32421716 PMCID: PMC7259805 DOI: 10.1371/journal.pcbi.1007614] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/29/2020] [Accepted: 04/05/2020] [Indexed: 12/12/2022] Open
Abstract
For stimuli near perceptual threshold, the trial-by-trial activity of single neurons in many sensory areas is correlated with the animal's perceptual report. This phenomenon has often been attributed to feedforward readout of the neural activity by the downstream decision-making circuits. The interpretation of choice-correlated activity is quite ambiguous, but its meaning can be better understood in the light of population-wide correlations among sensory neurons. Using a statistical nonlinear dimensionality reduction technique on single-trial ensemble recordings from the middle temporal (MT) area during perceptual-decision-making, we extracted low-dimensional latent factors that captured the population-wide fluctuations. We dissected the particular contributions of sensory-driven versus choice-correlated activity in the low-dimensional population code. We found that the latent factors strongly encoded the direction of the stimulus in single dimension with a temporal signature similar to that of single MT neurons. If the downstream circuit were optimally utilizing this information, choice-correlated signals should be aligned with this stimulus encoding dimension. Surprisingly, we found that a large component of the choice information resides in the subspace orthogonal to the stimulus representation inconsistent with the optimal readout view. This misaligned choice information allows the feedforward sensory information to coexist with the decision-making process. The time course of these signals suggest that this misaligned contribution likely is feedback from the downstream areas. We hypothesize that this non-corrupting choice-correlated feedback might be related to learning or reinforcing sensory-motor relations in the sensory population.
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Affiliation(s)
- Yuan Zhao
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
| | - Jacob L. Yates
- Brain and Cognitive Science, University of Rochester, Rochester, New York, United States of America
| | - Aaron J. Levi
- Center for Perceptual Systems, Departments of Neuroscience & Psychology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Alexander C. Huk
- Center for Perceptual Systems, Departments of Neuroscience & Psychology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Il Memming Park
- Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America
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36
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Fundamental bounds on the fidelity of sensory cortical coding. Nature 2020; 580:100-105. [PMID: 32238928 DOI: 10.1038/s41586-020-2130-2] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 01/21/2020] [Indexed: 12/13/2022]
Abstract
How the brain processes information accurately despite stochastic neural activity is a longstanding question1. For instance, perception is fundamentally limited by the information that the brain can extract from the noisy dynamics of sensory neurons. Seminal experiments2,3 suggest that correlated noise in sensory cortical neural ensembles is what limits their coding accuracy4-6, although how correlated noise affects neural codes remains debated7-11. Recent theoretical work proposes that how a neural ensemble's sensory tuning properties relate statistically to its correlated noise patterns is a greater determinant of coding accuracy than is absolute noise strength12-14. However, without simultaneous recordings from thousands of cortical neurons with shared sensory inputs, it is unknown whether correlated noise limits coding fidelity. Here we present a 16-beam, two-photon microscope to monitor activity across the mouse primary visual cortex, along with analyses to quantify the information conveyed by large neural ensembles. We found that, in the visual cortex, correlated noise constrained signalling for ensembles with 800-1,300 neurons. Several noise components of the ensemble dynamics grew proportionally to the ensemble size and the encoded visual signals, revealing the predicted information-limiting correlations12-14. Notably, visual signals were perpendicular to the largest noise mode, which therefore did not limit coding fidelity. The information-limiting noise modes were approximately ten times smaller and concordant with mouse visual acuity15. Therefore, cortical design principles appear to enhance coding accuracy by restricting around 90% of noise fluctuations to modes that do not limit signalling fidelity, whereas much weaker correlated noise modes inherently bound sensory discrimination.
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37
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Yates JL, Katz LN, Levi AJ, Pillow JW, Huk AC. A simple linear readout of MT supports motion direction-discrimination performance. J Neurophysiol 2019; 123:682-694. [PMID: 31852399 DOI: 10.1152/jn.00117.2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimination behavior or to approach optimal performance. In this study, we evaluated the performance of various neurally plausible decoders, trained to discriminate motion direction from small ensembles of simultaneously recorded MT neurons. We found that decoding the stimulus without knowledge of the interneuronal correlations was sufficient to match an optimal (correlation aware) decoder. Additionally, a decoder could match the psychophysical performance of the animals with flat integration of up to half the stimulus and inherited temporal dynamics from the time-varying MT responses. These results demonstrate that simple, linear decoders operating on small ensembles of neurons can match both psychophysical performance and optimal sensitivity without taking correlations into account and that such simple read-out mechanisms can exhibit complex temporal properties inherited from the sensory dynamics themselves.NEW & NOTEWORTHY Motion perception depends on the ability to decode the activity of neurons in the middle temporal area. Theoretically optimal decoding requires knowledge of the sensitivity of neurons and interneuronal correlations. We report that a simple correlation-blind decoder performs as well as the optimal decoder for coarse motion discrimination. Additionally, the decoder could match the psychophysical performance with moderate temporal integration and dynamics inherited from sensory responses.
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Affiliation(s)
- Jacob L Yates
- Brain and Cognitive Science, University of Rochester, Rochester, New York.,Center for Perceptual Systems, University of Texas at Austin, Austin, Texas.,Department of Neuroscience, University of Texas at Austin, Austin, Texas
| | - Leor N Katz
- Center for Perceptual Systems, University of Texas at Austin, Austin, Texas.,Department of Neuroscience, University of Texas at Austin, Austin, Texas.,Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Aaron J Levi
- Center for Perceptual Systems, University of Texas at Austin, Austin, Texas.,Department of Neuroscience, University of Texas at Austin, Austin, Texas.,Department of Psychology, University of Texas at Austin, Austin, Texas
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey.,Department of Psychology, Princeton University, Princeton, New Jersey
| | - Alexander C Huk
- Center for Perceptual Systems, University of Texas at Austin, Austin, Texas.,Department of Neuroscience, University of Texas at Austin, Austin, Texas.,Department of Psychology, University of Texas at Austin, Austin, Texas
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38
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Hou H, Zheng Q, Zhao Y, Pouget A, Gu Y. Neural Correlates of Optimal Multisensory Decision Making under Time-Varying Reliabilities with an Invariant Linear Probabilistic Population Code. Neuron 2019; 104:1010-1021.e10. [DOI: 10.1016/j.neuron.2019.08.038] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/21/2019] [Accepted: 08/22/2019] [Indexed: 12/27/2022]
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39
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The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior. J Neurosci 2019; 40:1066-1083. [PMID: 31754013 DOI: 10.1523/jneurosci.0859-19.2019] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 10/04/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022] Open
Abstract
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and shared response variability, and global activity modulations, could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions (population signal) and the inverse population covariability along the modulation axis (projected precision). We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions consistently across 4 monkeys (1 female and 1 male Macaca mulatta; and 2 male Macaca fascicularis). In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. We also show that differential correlations reduce the amount of information encoded in finite populations by reducing projected precision. Our results are consistent with predictions of a model that optimally decodes population responses to produce behavior.SIGNIFICANCE STATEMENT The last two or three decades of research have seen hot debates about what features of population tuning and trial-by-trial variability influence the information carried by a population of neurons, with some camps arguing, for instance, that mean pairwise correlations or global fluctuations are important while other camps report opposite results. In this study, we identify the most important features of neural population responses that determine the amount of encoded information and behavioral performance by combining analytic calculations with a novel nonparametric method that allows us to isolate the effects of different statistical features. We tested our hypothesis on 4 macaques, three decision-making tasks, and two brain areas. The predictions of our theory were in agreement with the experimental data.
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40
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Bányai M, Orbán G. Noise correlations and perceptual inference. Curr Opin Neurobiol 2019; 58:209-217. [PMID: 31593872 DOI: 10.1016/j.conb.2019.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Mihály Bányai
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary.
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41
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Choice (-history) correlations in sensory cortex: cause or consequence? Curr Opin Neurobiol 2019; 58:148-154. [PMID: 31581052 DOI: 10.1016/j.conb.2019.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 08/04/2019] [Accepted: 09/06/2019] [Indexed: 01/27/2023]
Abstract
One challenge in neuroscience, as in other areas of science, is to make inferences about the underlying causal structure from correlational data. Here, we discuss this challenge in the context of choice correlations in sensory neurons, that is, trial-by-trial correlations, unexplained by the stimulus, between the activity of sensory neurons and an animal's perceptual choice. Do these choice-correlations reflect feedforward, feedback signalling, both, or neither? We highlight recent results of correlational and causal examinations of choice and choice-history signals in sensory, and in part sensorimotor, cortex and address formal statistical frameworks to infer causal interactions from data.
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42
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Dendritic Spikes Expand the Range of Well Tolerated Population Noise Structures. J Neurosci 2019; 39:9173-9184. [PMID: 31558617 DOI: 10.1523/jneurosci.0638-19.2019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/08/2019] [Accepted: 09/14/2019] [Indexed: 12/11/2022] Open
Abstract
The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest in identifying noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary denoising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.SIGNIFICANCE STATEMENT Noise, or random variability, is a prominent feature of the neuronal code and poses a fundamental challenge for information processing. To reconcile the surprisingly accurate output of the brain with the inherent noisiness of biological systems, previous work examined signal integration in idealized neurons. The notion that emerged from this body of work is that accurate signal representation relies largely on input averaging in neuronal dendrites. In contrast to the prevailing view, I show that denoising in simulated neurons with realistic morphology and biophysical properties follows a different strategy: dendritic spikes act as classifiers that assist in extracting information from a variety of noise structures that have been considered before to be particularly disruptive for reliable brain function.
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Interneuronal correlations at longer time scales predict decision signals for bistable structure-from-motion perception. Sci Rep 2019; 9:11449. [PMID: 31391489 PMCID: PMC6686021 DOI: 10.1038/s41598-019-47786-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 07/19/2019] [Indexed: 12/25/2022] Open
Abstract
Perceptual decisions are thought to depend on the activation of task-relevant neurons, whose activity is often correlated in time. Here, we examined how the temporal structure of shared variability in neuronal firing relates to perceptual choices. We recorded stimulus-selective neurons from visual area V5/MT while two monkeys (Macaca mulatta) made perceptual decisions about the rotation direction of structure-from-motion cylinders. Interneuronal correlations for a perceptually ambiguous cylinder stimulus were significantly higher than those for unambiguous cylinders or for random 2D motion during passive viewing. Much of the difference arose from correlations at relatively long timescales (hundreds of milliseconds). Choice-related neural activity (quantified as choice probability; CP) for ambiguous cylinders was positively correlated with interneuronal correlations and was specifically associated with their long timescale component. Furthermore, the slope of the long timescale - but not the instantaneous - component of the correlation predicted higher CPs towards the end of the trial i.e. close to the decision. Our results suggest that the perceptual stability of structure-from-motion cylinders may be controlled by enhanced interneuronal correlations on longer timescales. We propose this as a potential signature of top-down influences onto V5/MT processing that shape and stabilize the appearance of 3D-motion percepts.
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Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History. J Neurosci 2019; 39:6714-6727. [PMID: 31235648 DOI: 10.1523/jneurosci.0035-19.2019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/12/2019] [Accepted: 06/18/2019] [Indexed: 01/06/2023] Open
Abstract
Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice- and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.SIGNIFICANCE STATEMENT Decades of research has provided a rich description of how visual information is represented in the visual cortex. Yet how cortical responses relate to visual perception remains poorly understood. Here we relate fluctuations in small neuronal population responses, recorded simultaneously in primary visual cortex (V1) and area V4 of monkeys, to perceptual reports in an orientation discrimination task. Choice-related signals were robust in V4, particularly late in the behavioral trial, but not in V1. Models that include both neuronal responses and choice-history information were able to predict a substantial portion of decisions. Our work shows the power of integrating information across neurons and including decision history in relating neuronal responses to perceptual decisions.
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Zavitz E, Price NSC. Weighting neurons by selectivity produces near-optimal population codes. J Neurophysiol 2019; 121:1924-1937. [PMID: 30917063 DOI: 10.1152/jn.00504.2018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Perception is produced by "reading out" the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons' spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets (Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron's tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron's preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron's tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher's linear discriminant) led to only marginally better performance than decoders based purely on a neuron's preferred direction and selectivity.
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Affiliation(s)
- Elizabeth Zavitz
- Department of Physiology, Monash University , Clayton, Victoria , Australia.,Biomedicine Discovery Institute, Monash University , Clayton, Victoria , Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University , Clayton, Victoria , Australia
| | - Nicholas S C Price
- Department of Physiology, Monash University , Clayton, Victoria , Australia.,Biomedicine Discovery Institute, Monash University , Clayton, Victoria , Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University , Clayton, Victoria , Australia
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Choice-Related Activity during Visual Slant Discrimination in Macaque CIP But Not V3A. eNeuro 2019; 6:eN-NWR-0248-18. [PMID: 30923736 PMCID: PMC6437654 DOI: 10.1523/eneuro.0248-18.2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 02/18/2019] [Accepted: 02/26/2019] [Indexed: 02/02/2023] Open
Abstract
Creating three-dimensional (3D) representations of the world from two-dimensional retinal images is fundamental to visually guided behaviors including reaching and grasping. A critical component of this process is determining the 3D orientation of objects. Previous studies have shown that neurons in the caudal intraparietal area (CIP) of the macaque monkey represent 3D planar surface orientation (i.e., slant and tilt). Here we compare the responses of neurons in areas V3A (which is implicated in 3D visual processing and precedes CIP in the visual hierarchy) and CIP to 3D-oriented planar surfaces. We then examine whether activity in these areas correlates with perception during a fine slant discrimination task in which the monkeys report if the top of a surface is slanted toward or away from them. Although we find that V3A and CIP neurons show similar sensitivity to planar surface orientation, significant choice-related activity during the slant discrimination task is rare in V3A but prominent in CIP. These results implicate both V3A and CIP in the representation of 3D surface orientation, and suggest a functional dissociation between the areas based on slant-related choice signals.
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Hofmann V, Chacron MJ. Population Coding and Correlated Variability in Electrosensory Pathways. Front Integr Neurosci 2018; 12:56. [PMID: 30542271 PMCID: PMC6277784 DOI: 10.3389/fnint.2018.00056] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 10/30/2018] [Indexed: 11/29/2022] Open
Abstract
The fact that perception and behavior depend on the simultaneous and coordinated activity of neural populations is well established. Understanding encoding through neuronal population activity is however complicated by the statistical dependencies between the activities of neurons, which can be present in terms of both their mean (signal correlations) and their response variability (noise correlations). Here, we review the state of knowledge regarding population coding and the influence of correlated variability in the electrosensory pathways of the weakly electric fish Apteronotus leptorhynchus. We summarize known population coding strategies at the peripheral level, which are largely unaffected by noise correlations. We then move on to the hindbrain, where existing data from the electrosensory lateral line lobe (ELL) shows the presence of noise correlations. We summarize the current knowledge regarding the mechanistic origins of noise correlations and known mechanisms of stimulus dependent correlation shaping in ELL. We finish by considering future directions for understanding population coding in the electrosensory pathways of weakly electric fish, highlighting the benefits of this model system for understanding the origins and impact of noise correlations on population coding.
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Affiliation(s)
- Volker Hofmann
- Department of Physiology, McGill University, Montréal, QC, Canada
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Lombardo JA, Macellaio MV, Liu B, Palmer SE, Osborne LC. State dependence of stimulus-induced variability tuning in macaque MT. PLoS Comput Biol 2018; 14:e1006527. [PMID: 30312315 PMCID: PMC6211771 DOI: 10.1371/journal.pcbi.1006527] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 11/01/2018] [Accepted: 09/25/2018] [Indexed: 12/31/2022] Open
Abstract
Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state. The brain controls behavior fluidly in a wide variety of cognitive contexts that alter the precision of neural responses. We examine how neural variability changes versus the mean response as a function of the stimulus and the behavioral state. We show that this scaled variability can have qualitatively different stimulus tuning in different behavioral contexts. In alert primates, scaled variability is tuned to the direction of motion of a visual stimulus and decreases around the preferred direction of each neuron. Under anesthesia, neurons show flat scaled variability tuning and, overall, responses are significantly more variable. We develop a simple model that includes a parameter describing firing rate gain fluctuations that can explain these changes. Our results suggest that tuned decreases in scaled variability during wakefulness may be mediated by an active process that suppresses synchronization and makes information transmission more reliable.
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Affiliation(s)
- Joseph A. Lombardo
- Computational Neuroscience Graduate Program, University of Chicago, Chicago, Illinois, United States of America
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
| | - Matthew V. Macellaio
- Neurobiology Graduate Program, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
| | - Bing Liu
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
| | - Stephanie E. Palmer
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
- Department of Physics, University of Chicago, Chicago, Illinois, United States of America
- * E-mail: (SEP); (LCO)
| | - Leslie C. Osborne
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
- * E-mail: (SEP); (LCO)
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Gu Y. Vestibular signals in primate cortex for self-motion perception. Curr Opin Neurobiol 2018; 52:10-17. [DOI: 10.1016/j.conb.2018.04.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 03/12/2018] [Accepted: 04/07/2018] [Indexed: 10/17/2022]
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Lakshminarasimhan KJ, Pouget A, DeAngelis GC, Angelaki DE, Pitkow X. Inferring decoding strategies for multiple correlated neural populations. PLoS Comput Biol 2018; 14:e1006371. [PMID: 30248091 PMCID: PMC6188888 DOI: 10.1371/journal.pcbi.1006371] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 10/15/2018] [Accepted: 07/17/2018] [Indexed: 12/29/2022] Open
Abstract
Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations. The neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animal’s reported percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models could be distinguished by a proposed experiment that measures correlations while inactivating different brain areas.
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Affiliation(s)
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States of America
| | - Gregory C. DeAngelis
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States of America
| | - Dora E. Angelaki
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
- Department of Mechanical and Aerospace Engineering, New York University, New York, United States of America
- Center for Neural Science, New York University, New York, United States of America
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, United States of America
- * E-mail:
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