Computational neuroscience articles within Nature Communications

Featured

  • Article
    | Open Access

    How the brain analyzes complex visual scenes within a fraction of a second remains poorly understood. Here, the authors suggest that this might be accomplished through the use of a temporal code by exploiting the sequence order of responses generated in networks of recurrently coupled neurons that harbor the priors of natural image statistics.

    • Yang Yiling
    • , Katharine Shapcott
    •  & Wolf Singer

  • 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

    Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a brain-inspired learning algorithm to uncover dependencies in individual fMRI networks with features of neural activity and predict inter-individual differences in decision-making.

    • Michael Schirner
    • , Gustavo Deco
    •  & Petra Ritter

  • Article
    | Open Access

    How the ‘what’, ‘where’, and ‘when’ of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave like food caching birds in episodic memory tasks.

    • Johanni Brea
    • , Nicola S. Clayton
    •  & Wulfram Gerstner

  • Article
    | Open Access

    Movies are complex, continuous stimuli that are characterized by visual and semantic novelty. Here, by leveraging intracranial recordings from 23 humans, the authors find that responses to novelty across film cuts and saccades are widespread in the brain.

    • Maximilian Nentwich
    • , Marcin Leszczynski
    •  & Lucas C. Parra

  • Article
    | Open Access

    How macroscale connectivity relates to regional micro-architecture is poorly understood. Here, the authors annotate brain networks with microarchitectural attributes, finding that the interplay between connection patterns and biological annotations shape regional functional specialization.

    • Vincent Bazinet
    • , Justine Y. Hansen
    •  & Bratislav Misic

  • Article
    | Open Access

    Moving precisely in natural environments requires adapting to multiple demands arising dynamically. Here, the authors show that the cerebellum’s capacity for multidimensional computations allows it to flexibly control multiple movement parameters guaranteeing movement precision.

    • Akshay Markanday
    • , Sungho Hong
    •  & Peter Thier

  • Article
    | Open Access

    Accurately capturing the tuning variability directly from the noisy neural responses is an important and challenging issue. Here, the authors introduce an unsupervised statistical approach to decomposing tuning variability, leading to a simple and unifying rule of tuning modulation in V1.

    • Rong J. B. Zhu
    •  & Xue-Xin Wei

  • Article
    | Open Access

    Neuroscience has long inspired AI, however the neuroevolutionary search that produces sophisticated behaviors has not been systematized. This paper defines neurodevelopmental ML as a discovery process for structures that promote complex computations.

    • Dániel L. Barabási
    • , Taliesin Beynon
    •  & Nicolas Perez-Nieves

  • Article
    | Open Access

    Not much is known about how intrinsic timescales, which characterize the dynamics of endogenous fluctuations in neural activity, change during cognitive tasks. Here, the authors show that intrinsic timescales of neural activity in the primate visual cortex change during spatial attention. Experimental data were best explained by a network model in which timescales arise from spatially arranged connectivity.

    • Roxana Zeraati
    • , Yan-Liang Shi
    •  & Tatiana A. Engel

  • 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

    The biological plausibility of backpropagation and its relationship with synaptic plasticity remain open questions. The authors propose a meta-learning approach to discover interpretable plasticity rules to train neural networks under biological constraints. The meta-learned rules boost the learning efficiency via bio-inspired synaptic plasticity.

    • Navid Shervani-Tabar
    •  & Robert Rosenbaum

  • Article
    | Open Access

    The cerebral cortex has ongoing electrical activities with rich and complex patterns in space and time. Here, the authors use optical voltage imaging in mice and computational methods, relating these complexities to different levels of wakefulness.

    • Yuqi Liang
    • , Junhao Liang
    •  & Changsong Zhou

  • Article
    | Open Access

    How we juggle morally conflicting outcomes during learning remains unknown. Here, by comparing variants of reinforcement learning models, the authors show that participants differ substantially in their preference, with some choosing actions that benefit themselves while others choose actions that prevent harm.

    • Laura Fornari
    • , Kalliopi Ioumpa
    •  & Valeria Gazzola

  • Article
    | Open Access

    The salience network has been hypothesised to modulate default mode network activity during stimulus-driven cognition. Here, the authors show that in rats, stimulation of the anterior insular cortex, a key node of the salience network, suppresses the default mode network and decouples these networks, providing in vivo evidence of a causal role of the anterior insular cortex in brain network switching.

    • Vinod Menon
    • , Domenic Cerri
    •  & Yen-Yu Ian Shih

  • Article
    | Open Access

    The mechanisms which generate fMRI signal correlations across the brain are not fully understood. Here, the authors record ultrafast fMRI signals in anesthetized female rats to demonstrate intrinsic macroscale oscillatory modes which drive correlated activity between distant regions.

    • Joana Cabral
    • , Francisca F. Fernandes
    •  & Noam Shemesh

  • Article
    | Open Access

    Alternative polyadenylation (APA) contributes to the post-transcriptional regulation of most human genes, yet the effects of APA are largely overlooked by conventional transcriptome-wide association studies (TWAS). Here, the authors conduct an APA-TWAS for 11 brain disorders, identifying hundreds of APA-linked disease susceptibility genes.

    • Ya Cui
    • , Frederick J. Arnold
    •  & Wei Li

  • Article
    | Open Access

    How the brain computes the value of complex stimuli such as visual art remains poorly understood. Here, the authors use computational models and fMRI to show that this process involves an integration over low- and high-level features across visual, parietal, and frontal cortical areas.

    • Kiyohito Iigaya
    • , Sanghyun Yi
    •  & John P. O’Doherty

  • Article
    | Open Access

    In the posterior cortex, which is involved in decision making, the strength and area specificity of choice signals are highly variable. Here the authors show that the representation of choice in the posterior area of the mouse brain is orthogonal to that of sensory and movement-related signals, with modulations determined by task features and cognitive demands.

    • Javier G. Orlandi
    • , Mohammad Abdolrahmani
    •  & Andrea Benucci

  • Article
    | Open Access

    In decision circuits, inhibitory neurons signal animal choices. Here, the authors show that choice-selective inhibition can stabilize the circuit dynamics or promote competition depending on inhibitory output connections, affecting choice behavior.

    • James P. Roach
    • , Anne K. Churchland
    •  & Tatiana A. Engel

  • Article
    | Open Access

    Biologically inspired spiking neural networks are highly promising, but remain simplified omitting relevant biological details. The authors introduce here theoretical and numerical frameworks for incorporating dendritic features in spiking neural networks to improve their flexibility and performance.

    • Michalis Pagkalos
    • , Spyridon Chavlis
    •  & Panayiota Poirazi

  • Article
    | Open Access

    Behavioral feedback is critical for learning, but it is often not available. Here, the authors introduce a deep learning model in which the cerebellum provides the cerebrum with feedback predictions, thereby facilitating learning, reducing dysmetria, and making several experimental predictions.

    • Ellen Boven
    • , Joseph Pemberton
    •  & Rui Ponte Costa

  • Article
    | Open Access

    In animals, sensory systems appear optimized for the statistics of the external world. Here the authors take an artificial psychophysics approach, analysing sensory responses in artificial neural networks, and show why these demonstrate the same phenomenon as natural sensory systems.

    • Ari S. Benjamin
    • , Ling-Qi Zhang
    •  & Konrad P. Kording

  • Article
    | Open Access

    Neural circuit dynamics are thought to drive temporally precise actions. Here, the authors used a theoretical approach to show that synapses endowed with diverse short-term plasticity can act as tunable timers sufficient to generate rich neural dynamics.

    • A. Barri
    • , M. T. Wiechert
    •  & D. A. DiGregorio

  • Article
    | Open Access

    How the human visual system leverages the rich structure in object motion for perception remains unclear. Here, Bill et al. propose a theory of how the brain could infer motion relations in real time and offer a unifying explanation for various perceptual phenomena.

    • Johannes Bill
    • , Samuel J. Gershman
    •  & Jan Drugowitsch

  • Article
    | Open Access

    Pain fluctuates over time in ways that are non-random. Here, the authors show that the human brain can learn to predict these changes in a manner consistent with optimal Bayesian inference by engaging sensorimotor, parietal, and premotor regions.

    • Flavia Mancini
    • , Suyi Zhang
    •  & Ben Seymour

  • 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

    Whether orientation-selectivity is discernable via fMRI remains unclear. Here, by analyzing a public dataset of responses to natural scenes using neurally-inspired image-computable models, the authors isolate and characterize a coarse-scale orientation map and demonstrate that orientation-selective BOLD responses reflect multiple distinct computations at a range of spatial scales.

    • Zvi N. Roth
    • , Kendrick Kay
    •  & Elisha P. Merriam

  • Article
    | Open Access

    The sensory cortices of many mammals consist of modules in the form of cortical columns. By analyzing functional connectivity and neural responses to visual stimuli, the authors show that this organization may extend to the human temporal lobe.

    • Julio I. Chapeton
    • , John H. Wittig Jr
    •  & Kareem A. Zaghloul

  • Article
    | Open Access

    It is unclear how the brain keeps track of the number of times different events are experienced. Here, a neural circuit is proposed for this problem inspired by a classic solution in computer science, and evidence of this circuit is shown in the fruit fly brain.

    • Sanjoy Dasgupta
    • , Daisuke Hattori
    •  & Saket Navlakha

  • 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

    There are several models of how serotonergic psychedelic drugs affect brain activity. Here the authors use network control theory and functional MRI data to provide evidence that serotonin receptor agonists LSD and psilocybin flatten the brain’s dynamic landscape, allowing for facile state transitions and more temporally diverse brain activity.

    • S. Parker Singleton
    • , Andrea I. Luppi
    •  & Amy Kuceyeski

  • Article
    | Open Access

    Most humans procrastinate to some extent, despite adverse consequences. Here, the authors show that how much an individual procrastinates, both in the lab and at home, relates to brain signals that reflect temporal discounting of effort cost.

    • Raphaël Le Bouc
    •  & Mathias Pessiglione

  • Article
    | Open Access

    Ganglion cells classically respond to either light increase (ON) or decrease (OFF). Here, the authors show that during natural scene stimulation, a single ganglion cell can switch between ON and OFF depending on the visual context.

    • Matías A. Goldin
    • , Baptiste Lefebvre
    •  & Olivier Marre

Browse broader subjects

Browse narrower subjects

Browse Computational neuroscience across other nature.com journals