Computational neuroscience articles within Nature Communications

Featured

  • Article
    | Open Access

    Understanding how brain networks evolve in time remains a challenge, with the potential for significant impact to human health and disease. Here, the authors introduce a new methodology to track dynamic functional networks that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time.

    • L.-E. Martinet
    • , M. A. Kramer
    •  & E. D. Kolaczyk

  • Article
    | Open Access

    Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs.

    • André G. Mendonça
    • , Jan Drugowitsch
    •  & Zachary F. Mainen

  • Article
    | Open Access

    Although the feeling of being stressed is ubiquitous and clinically significant, the underlying neural mechanisms are unclear. Using a novel predictive modeling approach, the authors show that functional hippocampal networks specifically and consistently predict the feeling of stress.

    • Elizabeth V. Goldfarb
    • , Monica D. Rosenberg
    •  & Rajita Sinha

  • Article
    | Open Access

    Path integration abilities, important for spatial navigation, vary widely across individuals and deteriorate in old age. This work shows that path integration errors in general, as well as age-related path integration deficits, are mainly caused by accumulating noise in people’s velocity estimation.

    • Matthias Stangl
    • , Ingmar Kanitscheider
    •  & Thomas Wolbers

  • Article
    | Open Access

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

    • Olivier J. Hénaff
    • , Zoe M. Boundy-Singer
    •  & Robbe L. T. Goris

  • 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

    The cognitive computational mechanisms underlying the antidepressant treatment response of SSRIs is not well understood. Here the authors show that SSRI treatment in healthy subjects for a week manifests as an amplification of the perception of positive outcomes when learning occurs in a positive mood setting.

    • Jochen Michely
    • , Eran Eldar
    •  & Raymond J. Dolan

  • Article
    | Open Access

    Theta oscillations have been implicated in hippocampal processing but mechanisms constraining phase timing of specific cell types are unknown. Here, the authors combine single-cell and multisite recordings with evolutionary computational models to evaluate mechanisms of phase preference of deep and superficial CA1 pyramidal cells.

    • Andrea Navas-Olive
    • , Manuel Valero
    •  & Liset M. de la Prida

  • Article
    | Open Access

    Neurons compute by integrating synaptic inputs across their dendritic arbor. Here, the authors show that distinct cell-types of mouse retinal ganglion cells that receive similar excitatory inputs have different biophysical mechanisms of input integration to generate their unique response tuning.

    • Yanli Ran
    • , Ziwei Huang
    •  & Thomas Euler

  • 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

    Head direction neurons constitute the brain’s compass, and are classically known to indicate head orientation in the horizontal plane. Here, the authors show that head direction neurons form a three-dimensional compass that can also indicate head tilt, and anchors to gravity.

    • Dora E. Angelaki
    • , Julia Ng
    •  & Jean Laurens

  • Article
    | Open Access

    When a cue is provided, people can rapidly attend to a changing scene and remember how it looked right after the cue appeared, but if the scene changes gradually, there is a delay in what we remember. Here the authors model these effects as prolonged attentional engagement.

    • Chloe Callahan-Flintoft
    • , Alex O. Holcombe
    •  & Brad Wyble

  • Article
    | Open Access

    Feelings of confidence reflect the likelihood that decisions are correct. Here the authors show that confidence taps partially dissociable evidence from that used for perceptual decisions, and that, rather than passively monitoring, confidence controls the depth of sensory information processing.

    • Tarryn Balsdon
    • , Valentin Wyart
    •  & Pascal Mamassian

  • Article
    | Open Access

    Adaptive adjustments in learning dynamics are accompanied by dynamic changes in a pattern of whole-brain functional connectivity characterized by integration between fronto-parietal and other networks. These dynamic functional connectivity changes also track individual differences in learning.

    • Chang-Hao Kao
    • , Ankit N. Khambhati
    •  & Joseph W. Kable

  • Article
    | Open Access

    The authors propose a learning rule for a neuron model with dendrite. In their model, somatodendritic interaction implements self-supervised learning applicable to a wide range of sequence learning tasks, including spike pattern detection, chunking temporal input and blind source separation.

    • Toshitake Asabuki
    •  & Tomoki Fukai

  • Article
    | Open Access

    The formation of functional synaptic clusters (FSCs) and their impact on somatic membrane potential (sVm) in vivo are poorly understood. Here, the authors develop a computational approach to show that FSCs have to form via local rather than global plasticity and be moderately large to impact sVm.

    • Balázs B. Ujfalussy
    •  & Judit K. Makara

  • 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 authors use a combination of perceptual decision making in rats and computational modeling to explore the interplay of priors and sensory cues. They find that rats can learn to either alternate or repeat their actions based on reward likelihood and the influence of bias on their actions disappears after making an error.

    • Ainhoa Hermoso-Mendizabal
    • , Alexandre Hyafil
    •  & Jaime de la Rocha

  • Article
    | Open Access

    That attention is a rhythmic process has received abundant evidence. Here, the authors reveal the natural sampling rate of auditory and visual periodic temporal attention. Both are antagonistically modulated by overt motor activity, a result generalised in a dynamical model of coupled oscillators.

    • Arnaud Zalta
    • , Spase Petkoski
    •  & Benjamin Morillon

  • Article
    | Open Access

    Understanding the underlying mechanisms behind the successes of deep networks remains a challenge. Here, the author demonstrates an implicit regularization in training deep networks, showing that the control of complexity in the training is hidden within the optimization technique of gradient descent.

    • Tomaso Poggio
    • , Qianli Liao
    •  & Andrzej Banburski

  • 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

    Humans are normally not aware that their eyes are always in motion, even when attempting to maintain steady gaze on a point. Here the authors show that these small eye movements are finely controlled and contribute more than two lines in a standard eye-chart test of visual acuity.

    • Janis Intoy
    •  & Michele Rucci

  • 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

    Auditory contrast gain control helps us perceive sounds as constant despite changes in the environment or background noise. Here, the authors show that neurons in the auditory thalamus and midbrain of mice display independent contrast gain control, not just the cortex as previously thought.

    • Michael Lohse
    • , Victoria M. Bajo
    •  & Ben D. B. Willmore

  • Article
    | Open Access

    Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics and dendritic computations.

    • Oren Amsalem
    • , Guy Eyal
    •  & Idan Segev

  • Article
    | Open Access

    Dopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. Here, the authors show that when given during an associative learning task, optogenetic activation of dopamine neurons causes associative, rather than value, learning.

    • Melissa J. Sharpe
    • , Hannah M. Batchelor
    •  & Geoffrey Schoenbaum

  • Article
    | Open Access

    Pavlovian and instrumentally driven actions often conflict when determining the best outcome. Here, the authors present an arbitration theory supported by human behavioral data where Pavlovian predictors drive action selection in an uncontrollable environment, while more flexible instrumental prediction dominates under conditions of high controllability.

    • Hayley M. Dorfman
    •  & Samuel J. Gershman

  • Article
    | Open Access

    The brain dynamically arbitrates between two model-based and model-free reinforcement learning (RL). Here, the authors show that participants tended to increase model-based control in response to increasing task complexity, but resorted to model-free when both uncertainty and task complexity were high.

    • Dongjae Kim
    • , Geon Yeong Park
    •  & Sang Wan Lee

  • Article
    | Open Access

    In vivo laser ablation of dendrites in single L2/3 pyramidal neurons reveals that neuronal orientation tuning in V1 is robust to loss of dendritic input. Orientation tuning functions remain unchanged following apical dendrite ablation and change only slightly upon loss of two primary basal dendrites.

    • Jiyoung Park
    • , Athanasia Papoutsi
    •  & Stelios M. Smirnakis

  • 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

    People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better courses of action in the future.

    • Chia-Chun Hung
    • , Timothy Lillicrap
    •  & Greg Wayne

  • Article
    | Open Access

    Providing efficient and scalable specialized hardware for stochastic neural networks remains a challenge. Here, the authors propose a fast, energy-efficient and scalable stochastic dot-product circuit that may use either of two types of memory devices – metal-oxide memristors and floating-gate memories.

    • M. R. Mahmoodi
    • , M. Prezioso
    •  & D. B. Strukov

  • Article
    | Open Access

    Rewards can improve stimulus processing in early sensory areas but the underlying neural circuit mechanisms are unknown. Here, the authors build a computational model of layer 2/3 primary visual cortex and suggest that plastic inhibitory circuits change first and then increase excitatory representations beyond the presence of rewards.

    • Katharina Anna Wilmes
    •  & Claudia Clopath

  • Article
    | Open Access

    Is there an optimum difficulty level for training? In this paper, the authors show that for the widely-used class of stochastic gradient-descent based learning algorithms, learning is fastest when the accuracy during training is 85%.

    • Robert C. Wilson
    • , Amitai Shenhav
    •  & Jonathan D. Cohen

  • Article
    | Open Access

    Working memory is maintained in the recurrent connectivity of prefrontal neurons; however, distractors lead to a morphing of the population code. Here, the authors show that a low dimensional subspace of activity maintains memory information even with a distractor and can be modeled as a bump attractor.

    • Aishwarya Parthasarathy
    • , Cheng Tang
    •  & Camilo Libedinsky

  • Article
    | Open Access

    The 302-neuron connectome of the nematode C. elegans has been completely mapped, yet the design principles that explain how the connectome structure determines its function are unknown. Here, the authors show that physical principles of symmetry and mathematical tools of symmetry groups can be used to understand C. elegans neural locomotion circuits.

    • Flaviano Morone
    •  & Hernán A. Makse

  • 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

    The brain can often continue to function despite lesions in many areas, but damage to particular locations may have serious effects. Here, the authors use the concept of Ollivier-Ricci curvature to investigate the robustness of brain networks.

    • Hamza Farooq
    • , Yongxin Chen
    •  & Christophe Lenglet

  • Article
    | Open Access

    In order to perceive moving or changing objects, sensory information must be integrated over time. Here, using a visual sequential metacontrast paradigm, the authors show that integration occurs only when subsequent stimuli are presented within a discrete window of time after the initial stimulus.

    • Leila Drissi-Daoudi
    • , Adrien Doerig
    •  & Michael H. Herzog

  • Article
    | Open Access

    Recording from monkey orbitofrontal cortex, the authors used composite reward bundles and found individual neuron and population responses that were suitable for economic choice. The responses followed behavioral indifference curves and predicted behavioral choices consistent with formalisms of Revealed Preference Theory.

    • Alexandre Pastor-Bernier
    • , Arkadiusz Stasiak
    •  & Wolfram Schultz

  • Article
    | Open Access

    Somatosensory hypersensitivity in Fmr-1 knockout mice is thought to arise from an increase in cortical circuit excitability. Here, the authors report that the loss of precision of sensory encoding in the Layer 4 of barrel cortex is the primary developmental circuit alteration that drives the other compensatory circuit dysfunction.

    • Aleksander P. F. Domanski
    • , Sam A. Booker
    •  & Peter C. Kind

  • 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

Browse broader subjects

Browse narrower subjects

Browse Computational neuroscience across other nature.com journals