Computational neuroscience articles within Nature Communications

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  • Article
    | Open Access

    The intrinsic dynamics of neurons, in particular the generation action potentials, can impact neural network states and processes of encoding information. The authors demonstrate how the elevation of temperature induces a type of action potential dynamics that favors synchronization patterns in neural networks.

    • Janina Hesse
    • , Jan-Hendrik Schleimer
    •  & Susanne Schreiber

  • 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

    Most neuroimaging studies are associated with a broad range analytic and methodological choices that the researcher needs to make, but every choice might lead to different answers, and evaluating all possible analytic choices is computationally challenging. Here, authors present a framework that maps the space of analysis by creating a low-dimensional space and using a Bayesian optimization to navigate it.

    • Jessica Dafflon
    • , Pedro F. Da Costa
    •  & Robert Leech

  • Article
    | Open Access

    The authors investigate the influence of brain injury (strokes) on the criticality of neural dynamics using directly measured connectomes and whole-brain models. They show that lesions engender a sub-critical state that recovers over time in parallel with behavior.

    • Rodrigo P. Rocha
    • , Loren Koçillari
    •  & Maurizio Corbetta

  • Article
    | Open Access

    It remains unclear how predictions of future threat affect memory recall, specifically in the case of post-traumatic stress disorder (PTSD). Here, the authors combined computational modeling and brain connectivity analyses to show that individuals with PTSD have exaggerated predictive control and reduced reactive control in a memory suppression task.

    • Giovanni Leone
    • , Charlotte Postel
    •  & Pierre Gagnepain

  • Article
    | Open Access

    Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.

    • Nanyi Fei
    • , Zhiwu Lu
    •  & Ji-Rong Wen

  • Article
    | Open Access

    The study of learning algorithms in the neocortex requires comprehensive knowledge of synaptic plasticity between its diverse cell types, which is currently lacking. Chindemi et al. describe a modeling approach to fill these gaps in experimental literature, and predict the features of synaptic plasticity in vivo.

    • Giuseppe Chindemi
    • , Marwan Abdellah
    •  & Eilif B. Muller

  • Article
    | Open Access

    Functional connectivity analyses have used both edge and node-centric approaches. Here the authors describe a mathematical framework for an edge-centric analysis of a neuroimaging time series and re-examine the main findings in the previous literature on the topic.

    • Leonardo Novelli
    •  & Adeel Razi

  • Article
    | Open Access

    Neurons in the early visual system respond preferentially to the onset or offset of light. Here the authors show that ON/OFF responses cluster in the mouse primary visual cortex, shaping the receptive fields of cortical cells.

    • Elaine Tring
    • , Konnie K. Duan
    •  & Dario L. Ringach

  • Article
    | Open Access

    Najafian et al. introduce a developmental theory of map formation in the cerebral cortex. The theory proposes that increases in the density of thalamic afferents sampling sensory space make cortical maps to segregate more stimulus dimensions.

    • Sohrab Najafian
    • , Erin Koch
    •  & Jose-Manuel Alonso

  • Article
    | Open Access

    Brain-inspired neural generative models can be designed to learn complex probability distributions from data. Here the authors propose a neural generative computational framework, inspired by the theory of predictive processing in the brain, that facilitates parallel computing for complex tasks.

    • Alexander Ororbia
    •  & Daniel Kifer

  • Article
    | Open Access

    How regional anatomy shapes function is not well understood. Here, the authors evaluate the performance of 40 communication models in predicting functional connectivity, and find regional heterogeneity in terms of fit and optimal model, and that regional coupling varies over the human lifespan.

    • Farnaz Zamani Esfahlani
    • , Joshua Faskowitz
    •  & Richard F. Betzel

  • Article
    | Open Access

    It remains unclear how the hippocampal region integrates position and self-motion information to update spatial representations. Here, the authors report grid and head direction cells as well as cells encoding self-motion parameters such as angular head velocity and speed, and find conjunctive representations of these different parameters.

    • Davide Spalla
    • , Alessandro Treves
    •  & Charlotte N. Boccara

  • Article
    | Open Access

    Tasks involving continual learning and adaptation to real-time scenarios remain challenging for artificial neural networks in contrast to real brain. The authors propose here a brain-inspired optimizer based on mechanisms of synaptic integration and strength regulation for improved performance of both artificial and spiking neural networks.

    • Giorgia Dellaferrera
    • , Stanisław Woźniak
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    Behavioral variation is thought to result from noise in sensory representations or final motor commands. In this study, the authors investigate variability in eye movements and model that variability as resulting from noisy sensorimotor transformations occurring in the middle temporal visual area.

    • Seth W. Egger
    •  & Stephen G. Lisberger

  • Article
    | Open Access

    Between saccades, our eyes undergo random movements called fixational drift, but what drives this motion has remained elusive. In this paper, the authors demonstrate that a central neural circuit within the oculomotor system drives fixational drift.

    • Nadav Ben-Shushan
    • , Nimrod Shaham
    •  & Yoram Burak

  • Article
    | Open Access

    Systems consolidation refers to the reorganization of memory engrams across brain regions. The authors present a biologically-plausible computational model that shows that hippocampal-thalamic-cortical activity is crucial for systems consolidation, making testable predictions for experimental neuroscience.

    • Douglas Feitosa Tomé
    • , Sadra Sadeh
    •  & Claudia Clopath

  • Article
    | Open Access

    The variability in synaptic connectivity observed at the cerebellar granule cell - Purkinje cell connection in mice accounts for motor behavior traits at the individual level, suggesting that cerebellar networks encode internal models underlying individual-specific motor adaptation.

    • Ludovic Spaeth
    • , Jyotika Bahuguna
    •  & Philippe Isope

  • Article
    | Open Access

    Mouse visual cortex is a dense, interconnected network of distinct areas. D’Souza et al. identify an anatomical index to quantify the hierarchical nature of pathways, and highlight the hierarchical and nonhierarchical features of the network.

    • Rinaldo D. D’Souza
    • , Quanxin Wang
    •  & Andreas Burkhalter

  • Article
    | Open Access

    It is unknown whether object category learning can be formed purely through domain general learning of natural image structure. Here the authors show that human visual brain responses to objects are well-captured by self-supervised deep neural network models trained without labels, supporting a domain-general account.

    • Talia Konkle
    •  & George A. Alvarez

  • Article
    | Open Access

    Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. Here, using a hierarchical deep neural network model of the ventral visual stream, the authors suggest that face selectivity arises in the complete absence of training.

    • Seungdae Baek
    • , Min Song
    •  & Se-Bum Paik

  • Article
    | Open Access

    The neural and computational mechanisms underpinning pitch perception remain unclear. Here, the authors trained deep neural networks to estimate the fundamental frequency of sounds and found that human pitch perception depends on precise spike timing in the auditory nerve, but is also adapted to the statistical tendencies of natural sounds.

    • Mark R. Saddler
    • , Ray Gonzalez
    •  & Josh H. McDermott

  • Article
    | Open Access

    Models of perceptual decision making typically take into account either reactive responses to external stimuli or proactive aspects to decision making. Here the authors found that rat perceptual responses are generated by a combination of the standard evidence accumulation process with a fixed decision boundary, and a separate stochastic boundary collapse triggered by a parallel proactive process.

    • Lluís Hernández-Navarro
    • , Ainhoa Hermoso-Mendizabal
    •  & Alexandre Hyafil

  • Article
    | Open Access

    Chronic nicotine exposure impacts various components of decision-making processes, such as exploratory behaviors. Here, the authors identify the cellular mechanism and show that chronic nicotine exposure increases the tonic activity of VTA dopaminergic neurons and reduces exploration in mice.

    • Malou Dongelmans
    • , Romain Durand-de Cuttoli
    •  & Philippe Faure

  • Article
    | Open Access

    Associations have been established between brain folding and white matter connectivity. Here the authors show that axon elongation, in response to mechanical stresses during cortical expansion and folding, may be sufficient to induce tissue remodeling consistent with white matter organization.

    • Kara E. Garcia
    • , Xiaojie Wang
    •  & Christopher D. Kroenke

  • 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

    Human learning depends on opposing effects of two noise factors: volatility and stochasticity. Here the authors present a model of learning that shows how and why joint estimation of these factors is important for understanding healthy and pathological learning.

    • Payam Piray
    •  & Nathaniel D. Daw

  • Article
    | Open Access

    How visual social information informs movement is unclear. Here, the authors characterise the algorithm zebrafish use to transform visual inputs from neighbours into movement decisions during collective swimming behavior. The authors can also predict the neural circuits involved in transforming the visual input into movement decisions.

    • Roy Harpaz
    • , Minh Nguyet Nguyen
    •  & Florian Engert

  • Article
    | Open Access

    Spontaneous fluctuations in brain activity exhibit complex spatiotemporal patterns across animal species. Here the authors show that sensory-motor regions and spatial heterogeneity in excitation-inhibition balance might shape multi-stability in brain dynamics.

    • Xiaolu Kong
    • , Ru Kong
    •  & B. T. Thomas Yeo

  • Article
    | Open Access

    Priors learnt from lifetime experiences influence perception. The authors show that when perception is congruent with a long-term prior, there is increased top-down input in the ventral visual stream, whereas bottom-up input is enhanced when perception is incongruent with prior.

    • Richard Hardstone
    • , Michael Zhu
    •  & Biyu J. He

  • Article
    | Open Access

    Spontaneous traveling cortical waves shape neural responses. Using a large-scale computational model, the authors show that transmission delays shape locally asynchronous spiking dynamics into traveling waves without inducing correlations and boost responses to external input, as observed in vivo.

    • Zachary W. Davis
    • , Gabriel B. Benigno
    •  & Lyle Muller

  • Article
    | Open Access

    Identical physical inputs can evoke non-identical percepts. Here, the authors investigate the sources of such variability and find that rats and humans, trained to judge tactile vibration strength, express a robust sequential effect that could be modeled as the trial-by-trial incorporation of sensory history.

    • I. Hachen
    • , S. Reinartz
    •  & M. E. Diamond

  • Article
    | Open Access

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

    • Olivier J. Hénaff
    • , Yoon Bai
    •  & Robbe L. T. Goris

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