Neural decoding articles within Nature Communications

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  • Article
    | Open Access

    It is unclear how reward and risk are temporally organized in the human brain. Here, the authors demonstrate both sequential and parallel encoding of decision variables, and the role of anterior insula in reward- and risk-prediction error.

    • Vincent Man
    • , Jeffrey Cockburn
    •  & John P. O’Doherty

  • Article
    | Open Access

    Animals respond rapidly and precisely to a variety of sensory stimuli, but the neural mechanisms supporting this flexibility are not fully understood. Here the authors describe a model of adaptive sensory processing based on functionally-targeted stochastic modulation, and find evidence for this co-variability in macaque V1 and middle temporal area.

    • Caroline Haimerl
    • , Douglas A. Ruff
    •  & Eero P. Simoncelli

  • Article
    | Open Access

    Perception is often modelled using a Bayesian framework, but its neural instantiation remains unclear. Using a novel modelling approach, the authors reveal an empirical encoding scheme for visual orientation sufficient for optimal inference.

    • William J. Harrison
    • , Paul M. Bays
    •  & Reuben Rideaux

  • Article
    | Open Access

    Little is known about how the brain encodes—and ultimately drives—the tongue’s 3D deformation. Here, the authors successfully decoded complex tongue deformation from sensorimotor cortex neurons, suggesting a cortical representation of 3D tongue shape.

    • Jeffrey D. Laurence-Chasen
    • , Callum F. Ross
    •  & Nicholas G. Hatsopoulos

  • Article
    | Open Access

    Natural behaviors induce changes to hidden states of the world that may be vital to track. Here, in monkeys navigating virtually to hidden goals, the authors show that neural interactions in the posterior parietal cortex play a role in tracking displacement from an unobservable goal.

    • Kaushik J. Lakshminarasimhan
    • , Eric Avila
    •  & Dora E. Angelaki

  • Article
    | Open Access

    Speech unfolds faster than the brain completes processing of speech sounds. Here, the authors show that brain activity moves systematically within neural populations of auditory cortex, allowing accurate representation of a speech sound’s identity and its position in the sound sequence.

    • Laura Gwilliams
    • , Jean-Remi King
    •  & David Poeppel

  • Article
    | Open Access

    It is unclear how the activity of individual neurons conform to prospect theory. Here, the authors demonstrate that the activity of single neurons in various reward-related regions in the monkey brain can be described as encoding a multiplicative combination of utility and probability weighting, and that this subjective valuation process is achieved via a distributed coding scheme.

    • Yuri Imaizumi
    • , Agnieszka Tymula
    •  & Hiroshi Yamada

  • Article
    | Open Access

    Activity in the superficial layers of the sensory cortex is believed to be largely driven by incoming sensory stimuli. Here the authors demonstrate how learning changes neural responses to sensations according to both behavioral relevance and timing, suggesting a high degree of non-sensory modulation.

    • Rebecca J. Rabinovich
    • , Daniel D. Kato
    •  & Randy M. Bruno

  • Article
    | Open Access

    The brain adapts dynamically to the statistics of its environment. Here, the authors use psychophysics and model-based representational fMRI and EEG to show that audiovisual recalibration relies on distinct spatial and decisional codes that are expressed with opposite gradients and time courses across the auditory processing hierarchy.

    • Máté Aller
    • , Agoston Mihalik
    •  & Uta Noppeney

  • Article
    | Open Access

    Visual recognition depends on the ability to extract specific shape and colour features from complicated natural scenes. Here, the authors show that neurons along the object-recognition cortical pathway encode information-concentrating features of moderate complexity and of behavioural relevance.

    • Olivia Rose
    • , James Johnson
    •  & Carlos R. Ponce

  • Article
    | Open Access

    Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. Here, the authors present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information which indicates near-optimal nonlinear decoding.

    • Qianli Yang
    • , Edgar Walker
    •  & Xaq Pitkow

  • Article
    | Open Access

    Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium signals to drive an optical brain-computer interface in macaques.

    • Eric M. Trautmann
    • , Daniel J. O’Shea
    •  & Krishna V. Shenoy

  • Article
    | Open Access

    A major challenge across a variety of fields is how to process the vast quantities of data produced by sensors without large computation resources. Here, the authors present a neuromorphic chip which can detect a relevant signature of epileptogenic tissue from intracranial recordings in patients.

    • Mohammadali Sharifshazileh
    • , Karla Burelo
    •  & Giacomo Indiveri

  • Article
    | Open Access

    How thalamic sensory relays participate in plasticity upon associative fear learning and stable long-term sensory coding remains unknown. The authors show that auditory thalamus neurons exhibit heterogeneous plasticity patterns after learning while population level encoding of auditory stimuli remains stable across days.

    • James Alexander Taylor
    • , Masashi Hasegawa
    •  & Jan Gründemann

  • Article
    | Open Access

    Information regarding a sensory stimulus is distributed in activity of neuronal populations. Here the authors show stimulus information scales sub-linearly with the number of neurons in mouse visual cortex due to correlated noise and may saturate in far fewer numbers of neurons than the total in V1.

    • MohammadMehdi Kafashan
    • , Anna W. Jaffe
    •  & Jan Drugowitsch

  • Article
    | Open Access

    Two-photon imaging in macaque V1 captured maps of tuning selectivity for four spatial parameters, all of which correlated with peak spatial frequency. These inter-map relationships reveal a common motif—they are described by uniform spatial pooling from a family of scale invariant Gabor receptive fields.

    • Y. Chen
    • , H. Ko
    •  & I. Nauhaus

  • Article
    | Open Access

    When asked to imagine an event such as a party, individuals will vary in their mental imagery based on their specific experience of parties. Here, the authors show that such signatures of personal experience can be read from brain activity elicited as events are imagined.

    • Andrew James Anderson
    • , Kelsey McDermott
    •  & Feng V. Lin

  • Article
    | Open Access

    To see during day and night, the retina adapts to a trillion-fold change in light intensity. The authors show that an accurate read-out of retinal signals over this intensity range requires that brain circuits account for changing noise correlations across populations of retinal neurons.

    • Kiersten Ruda
    • , Joel Zylberberg
    •  & Greg D. Field

  • Article
    | Open Access

    Humans can unconsciously learn to gamble on rewarding options, but can they do so when it comes to their own mental states? Here, the authors show that participants can learn to use unconscious representations in their own brains to earn rewards, and that metacognition correlates with their learning processes.

    • Aurelio Cortese
    • , Hakwan Lau
    •  & Mitsuo Kawato

  • Article
    | Open Access

    Schulz et al. systematically benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets. Complicated nonlinear intervariable relationships remain largely inaccessible for predicting key phenotypes from typical brain scans.

    • Marc-Andre Schulz
    • , B. T. Thomas Yeo
    •  & Danilo Bzdok

  • Article
    | Open Access

    In value-based decision-making, single prefrontal neurons represent multiple variables at different times in the decision process. Here, the authors show these representations to be separable and stable at the population level, allowing read out of specific variables at behaviorally relevant times.

    • Daniel L. Kimmel
    • , Gamaleldin F. Elsayed
    •  & William T. Newsome

  • Article
    | Open Access

    Deep brain stimulation (DBS) is a symptomatic treatment of Parkinson’s disease (PD) that benefits only a minority of patients. Here, the authors show that activation of cortical somatostatin interneurons alleviates motor symptoms in a mouse model of PD and may constitute a less invasive alternative than DBS.

    • Sébastien Valverde
    • , Marie Vandecasteele
    •  & Laurent Venance

  • Article
    | Open Access

    Memory recollection involves reactivation of neural activity that occurred during the recalled experience. Here, the authors show that neural reactivation can be decomposed into visual-semantic features, is widely synchronized throughout the brain, and predicts memory vividness and accuracy.

    • Michael B. Bone
    • , Fahad Ahmad
    •  & Bradley R. Buchsbaum

  • Article
    | Open Access

    The authors construct quantitative models of human brain activity evoked by 103 cognitive tasks and reveal the organization of diverse cognitive functions in the brain. Their model, which uses latent cognitive features, predicts brain activity and decodes tasks, even under novel task conditions.

    • Tomoya Nakai
    •  & Shinji Nishimoto

  • Article
    | Open Access

    The prefrontal attention spotlight dynamically explores space at 7–12 Hz, enhancing sensory encoding and behavior, in the absence of eye movements. This alpha-clocked sampling of space is under top-down control and implements an alternation in exploration and exploitation of the visual environment.

    • Corentin Gaillard
    • , Sameh Ben Hadj Hassen
    •  & Suliann Ben Hamed

  • Article
    | Open Access

    Neural activity space or manifold that represents object information changes across the layers of a deep neural network. Here the authors present a theoretical account of the relationship between the geometry of the manifolds and the classification capacity of the neural networks.

    • Uri Cohen
    • , SueYeon Chung
    •  & Haim Sompolinsky

  • Article
    | Open Access

    Interference from overlapping memories can cause forgetting. Here, the authors show using fMRI decoding approaches that spontaneous reactivation of older memories during new encoding leads to integration, and less interference, between overlapping items.

    • Avi J. H. Chanales
    • , Nicole M. Dudukovic
    •  & Brice A. Kuhl

  • Article
    | Open Access

    It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.

    • Josef Ladenbauer
    • , Sam McKenzie
    •  & Srdjan Ostojic

  • Article
    | Open Access

    Neuronal tuning is typically measured in response to a priori defined behavioural variables of interest. Here, the authors use an unsupervised learning approach to recover neuronal tuning with respect to the recorded network activity and show that this can reveal the relevant behavioural variables.

    • Alon Rubin
    • , Liron Sheintuch
    •  & Yaniv Ziv

  • Article
    | Open Access

    Current techniques have enabled the simultaneous collection of spike train data from large numbers of neurons. Here, the authors report a method to infer the underlying neural circuit connectivity diagram based on a generalized linear model applied to spike cross-correlations between neurons.

    • Ryota Kobayashi
    • , Shuhei Kurita
    •  & Shigeru Shinomoto

  • Article
    | Open Access

    The effect of spontaneous variations in prestimulus neural activity on subsequent perception is incompletely understood. Here, using MEG, the authors identify two distinct neural processes that can influence object recognition in different ways.

    • Ella Podvalny
    • , Matthew W. Flounders
    •  & Biyu J. He

  • Article
    | Open Access

    In order to make optimal choices, it is adaptive for the brain to build a model of the world to enable predictions about likely later events. Here, the authors show that activity across learning in the orbitofrontal cortex comes to represent expected states, up to 30 s in the future.

    • G. Elliott Wimmer
    •  & Christian Büchel

  • Article
    | Open Access

    People differ in their current levels of understanding of many complex concepts. Here, the authors show using fMRI that brain activity during a task that requires concept knowledge can be used to compute a ‘neural score’ of the participant’s understanding.

    • Joshua S. Cetron
    • , Andrew C. Connolly
    •  & David J. M. Kraemer

  • Article
    | Open Access

    There has been recent controversy over the validity of commonly-used software packages for functional MRI (fMRI) data analysis. Here, the authors compare the performance of three leading packages (AFNI, FSL, SPM) in terms of temporal autocorrelation modeling, a key statistical step in fMRI analysis.

    • Wiktor Olszowy
    • , John Aston
    •  & Guy B. Williams

  • Article
    | Open Access

    Sensory systems produce stable stimulus representations despite constant changes across multiple stimulus dimensions. Here, the authors reveal dynamic neural coding mechanisms by testing how coding of one dimension (orientation) changes with adaptations to other dimensions (luminance and contrast).

    • Masoud Ghodrati
    • , Elizabeth Zavitz
    •  & Nicholas S. C. Price

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