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Article
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Phase information is conserved in sparse, synchronous population-rate-codes via phase-to-rate recoding
How neural codes are passed from one brain area to the next remains poorly understood. Here, the authors show how neuronal feedback inhibition converts incoming temporal information into sparse rate information in a biophysical network model of the dentate gyrus.
- Daniel Müller-Komorowska
- , Baris Kuru
- & Oliver Braganza
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Article
| Open Access
Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex
The relationship between structural and functional coupling varies across the brain, but the biological underpinnings are not known. Here, the authors show that structure-function coupling is related to myelination and excitation-inhibition balance.
- Panagiotis Fotiadis
- , Matthew Cieslak
- & Dani S. Bassett
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Article
| Open Access
PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging
The role of dopamine in foraging behaviour in humans is not well understood. Here, the authors show using PET imaging, that striatal dopamine receptor availability, and dopamine function in the anterior cingulate cortex and mesolimbic areas are related to the decision to explore new environments.
- Angela M. Ianni
- , Daniel P. Eisenberg
- & Karen F. Berman
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Article
| Open Access
Neurophysiological signatures of cortical micro-architecture
How neurophysiological dynamics are organized across the cortex and their relationship with cortical micro-architecture is not well understood. Here, the authors find the dominant axis of neurophysiological dynamics reflects characteristics of the power spectrum and the linear correlation structure of the signal, and that spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features.
- Golia Shafiei
- , Ben D. Fulcher
- & Bratislav Misic
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Article
| Open Access
On the visual analytic intelligence of neural networks
Visual oddity tasks delve into the visual analytic intelligence of humans, which remained challenging for artificial neural networks. The authors propose here a model with biologically inspired neural dynamics and synthetic saccadic eye movements with improved efficiency and accuracy in solving the visual oddity tasks.
- Stanisław Woźniak
- , Hlynur Jónsson
- & Evangelos Eleftheriou
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Article
| Open Access
Network controllability of structural connectomes in the neonatal brain
Network controllability represents the ease with which the brain switches between mental states and can be inferred from white matter connectivity. Here, the authors show network controllability emerges in infants as early as the third trimester, and that preterm birth disrupts the energy required to drive state transitions.
- Huili Sun
- , Rongtao Jiang
- & Dustin Scheinost
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Article
| Open Access
A GPU-based computational framework that bridges neuron simulation and artificial intelligence
High computational cost severely limit the applications of biophysically detailed multi-compartment models. Here, the authors present DeepDendrite, a GPU-optimized tool that drastically accelerates detailed neuron simulations for neuroscience and AI, enabling exploration of intricate neuronal processes and dendritic learning mechanisms in these fields.
- Yichen Zhang
- , Gan He
- & Tiejun Huang
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Article
| Open Access
Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable
Adolescence is often associated with heightened reward learning and impulsivity. Here the authors show in 742 people aged 9-18 that reward learning in fact remains stable with age, whilst punishment learning increases and action initiation decreases.
- Ruth Pauli
- , Inti A. Brazil
- & Patricia L. Lockwood
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Article
| Open Access
The spatial and temporal structure of neural activity across the fly brain
Neuropil regions across the fly brain are activated by locomotion. Here, authors show that this movement-related activity involves most neurons in the dorsal fly brain, including genetically defined neurons with known, seemingly unrelated functions.
- Evan S. Schaffer
- , Neeli Mishra
- & Richard Axel
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Article
| Open Access
Prior information differentially affects discrimination decisions and subjective confidence reports
Both decisions and the confidence accompanying them are influenced not only by incoming information, but also prior expectations. Here, the authors show that confidence in decisions is affected by prior information more than the decisions themselves.
- Marika Constant
- , Michael Pereira
- & Elisa Filevich
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Article
| Open Access
Beta traveling waves in monkey frontal and parietal areas encode recent reward history
Here, the authors show that beta oscillations in the frontal and parietal lobes of monkeys propagate as traveling waves. The strength of these signals increases after rewards, suggesting a role for traveling waves in memory for recent events.
- Erfan Zabeh
- , Nicholas C. Foley
- & Jacqueline P. Gottlieb
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Article
| Open Access
Locating causal hubs of memory consolidation in spontaneous brain network in male mice
How long-lasting memory is formed remains incompletely understood. Here, using fMRI and hub silencing, the authors discovered causal network hubs that are instrumental in consolidating memory and contributing to network reorganization.
- Zengmin Li
- , Dilsher Athwal
- & Kai-Hsiang Chuang
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Article
| Open Access
Neural tuning instantiates prior expectations in the human visual system
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
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Article
| Open Access
Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks
The conditions under which networks of neurons exhibit critical dynamics remains unclear. Here, the authors investigate how simple neural cultures reorganize activity when embodied in a gameplay environment and find that network wide neural criticality arises in nuanced ways.
- Forough Habibollahi
- , Brett J. Kagan
- & Chris French
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Article
| Open Access
Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule
Prediction of future inputs is a key computational task for the brain. Here, the authors proposed a predictive learning rule in neurons that leads to anticipation and recall of inputs, and that reproduces experimentally observed STDP phenomena.
- Matteo Saponati
- & Martin Vinck
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Comment
| Open Access
The brain’s unique take on algorithms
The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective.
- James B. Aimone
- & Ojas Parekh
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Perspective
| Open Access
Toward a formal theory for computing machines made out of whatever physics offers
Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.
- Herbert Jaeger
- , Beatriz Noheda
- & Wilfred G. van der Wiel
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| Open Access
Brain mitochondrial diversity and network organization predict anxiety-like behavior in male mice
Brain mitochondria play crucial roles that influence cognition, yet their diversity is often overlooked. This study in mice identifies distinct mitochondrial phenotypes distributed as large-scale networks, accounting for a large portion of animal-to-animal behavioural variation.
- Ayelet M. Rosenberg
- , Manish Saggar
- & Martin Picard
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Article
| Open Access
Dynamics of cortical contrast adaptation predict perception of signals in noise
The auditory system adapts to properties of sounds reaching the ear, but it is unclear whether this affects the way sounds are perceived. Here, the authors found that auditory responses in the brain predict changes in the perception of sounds, suggesting that adaptation shapes the way we hear.
- Christopher F. Angeloni
- , Wiktor Młynarski
- & Maria N. Geffen
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Article
| Open Access
Mechanisms underlying pathological cortical bursts during metabolic depletion
Disruption to the brain’s oxygen supply triggers pathological dynamics and brain injuries. Here, the authors develop a model of coupled metabolic-neuronal activity that generates burst suppression patterns similar to those of infants after birth asphyxia.
- Shrey Dutta
- , Kartik K. Iyer
- & James A. Roberts
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Article
| Open Access
Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data
The brain has been proposed to operate near a critical transition between order and disorder, controlled by a balance between inhibition and excitation. Here, the authors show that individual variability in long-range synchronization between brain regions can be explained by an individual’s proximity to this phase transition.
- Marco Fuscà
- , Felix Siebenhühner
- & Satu Palva
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| Open Access
Experimental validation of the free-energy principle with in vitro neural networks
Empirical applications of the free-energy principle entail a commitment to a particular process theory. Here, the authors reverse engineered generative models from neural responses of in vitro networks and demonstrated that the free-energy principle could predict how neural networks reorganized in response to external stimulation.
- Takuya Isomura
- , Kiyoshi Kotani
- & Karl J. Friston
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| Open Access
Neural manifolds for odor-driven innate and acquired appetitive preferences
It remains unclear how odorants with diverse appetitive preferences are encoded by an ensemble of neurons. Here, the authors show that such odorants can be succinctly described using low-dimensional neural representations or ‘neural manifolds.’
- Rishabh Chandak
- & Baranidharan Raman
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Article
| Open Access
Representations in human primary visual cortex drift over time
It is unclear whether human visual cortex exhibits representational drift. Here, the authors test the stability of visual representations and find that responsivity drifts over time, yet dissimilarities remain stable, suggesting a neural mechanism to overcome cumulative changes.
- Zvi N. Roth
- & Elisha P. Merriam
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Article
| Open Access
Phonemic segmentation of narrative speech in human cerebral cortex
The neural dynamics underlying speech comprehension are not well understood. Here, the authors show that phonemic-to-lexical processing is localized to a large region of the temporal cortex, and that segmentation of the speech stream occurs mostly at the level of diphones.
- Xue L. Gong
- , Alexander G. Huth
- & Frédéric E. Theunissen
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Article
| Open Access
The default network dominates neural responses to evolving movie stories
How brain networks process dynamic naturalistic stimuli is not well understood. Here, the authors use machine learning algorithms to show that brain states in the default network capture the semantic aspects of an unfolding narrative during movie watching.
- Enning Yang
- , Filip Milisav
- & Danilo Bzdok
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Article
| Open Access
Trait anxiety is associated with hidden state inference during aversive reversal learning
Here, the authors show that anxiety-related alterations of aversive learning can be understood in terms of a computational model in which anxious humans mentally represent more hidden states as causes of different levels of threats.
- Ondrej Zika
- , Katja Wiech
- & Nicolas W. Schuck
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Article
| Open Access
Cell-type-specific plasticity of inhibitory interneurons in the rehabilitation of auditory cortex after peripheral damage
Peripheral sensory organ damage leads to compensatory cortical plasticity. Here, the authors show that after noise trauma, auditory cortical neurons display cell-type-specific plasticity in their sound-evoked and intrinsic properties.
- Manoj Kumar
- , Gregory Handy
- & Thanos Tzounopoulos
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Article
| Open Access
Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others
Inferring other people’s intentions from their actions is essential for successful social engagement. Here, the authors show that in social contexts, dopamine D2 receptors are important in regulating uncertainty-driven belief updating.
- Nace Mikus
- , Christoph Eisenegger
- & Michael Naef
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Article
| Open Access
Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance
Metacognition is fundamental for regulating learning speeds and memory retention. Here, the authors demonstrate that reinforcement learning mediates this process in implicit motor learning, maximizing rewards and minimizing punishments.
- Taisei Sugiyama
- , Nicolas Schweighofer
- & Jun Izawa
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Article
| Open Access
Natural statistics support a rational account of confidence biases
Human decision confidence displays a number of biases and has been shown to dissociate from decision accuracy. Here, by using neural network and Bayesian models, the authors show that these effects can be explained by the statistics of sensory inputs.
- Taylor W. Webb
- , Kiyofumi Miyoshi
- & Hakwan Lau
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| Open Access
Detailed characterization of neural selectivity in free viewing primates
Studying visual processing during natural eye movements in untrained animals is challenging. Here, the authors provide a method for accurately measuring the retinal input to study visual processing and neural selectivity during natural oculomotor behavior in non-human primates.
- Jacob L. Yates
- , Shanna H. Coop
- & Jude F. Mitchell
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| Open Access
A dorsomedial prefrontal cortex-based dynamic functional connectivity model of rumination
Rumination, which is the tendency to dwell on negative internal states repetitively, is a well-known cognitive style associated with depression. The authors developed a predictive model of rumination and observed that the dorsomedial prefrontal cortex plays an important role in rumination.
- Jungwoo Kim
- , Jessica R. Andrews-Hanna
- & Choong-Wan Woo
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Article
| Open Access
Waves traveling over a map of visual space can ignite short-term predictions of sensory input
Waves of neural activity travel across single regions in the visual cortex, but their computational role is unclear. Here, the authors present a neural network model demonstrating that waves traveling over retinotopic maps can enable short-term predictions of future inputs.
- Gabriel B. Benigno
- , Roberto C. Budzinski
- & Lyle Muller
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Article
| Open Access
Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations
Whether or not deep neural networks require hierarchical representations to predict brain activity is not known. Here, the authors show that a multi-branch deep neural network can predict neural activity independently in visual areas in the absence of hierarchical representations.
- Ghislain St-Yves
- , Emily J. Allen
- & Thomas Naselaris
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Article
| Open Access
Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex
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
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Article
| Open Access
Robust cortical encoding of 3D tongue shape during feeding in macaques
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
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| Open Access
Learning how network structure shapes decision-making for bio-inspired computing
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
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Article
| Open Access
Computational models of episodic-like memory in food-caching birds
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
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| Open Access
Semantic novelty modulates neural responses to visual change across the human brain
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
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Article
| Open Access
Assortative mixing in micro-architecturally annotated brain connectomes
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
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Article
| Open Access
Distributing task-related neural activity across a cortical network through task-independent connections
Large scale neural recordings show that task-related activity is observed across neural circuits. Here, the authors have identified a network mechanism that promotes distributed activity in the cortex during decision-making via task-independent synapses.
- Christopher M. Kim
- , Arseny Finkelstein
- & Ran Darshan
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Article
| Open Access
Multidimensional cerebellar computations for flexible kinematic control of movements
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
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Article
| Open Access
Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex
Understanding functional role of different neuronal cell types is challenging. Here the authors associate multi-modal in vitro cell properties with in vivo physiology of mouse visual cortex.
- Yina Wei
- , Anirban Nandi
- & Costas A. Anastassiou
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Article
| Open Access
Unsupervised approach to decomposing neural tuning variability
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
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Article
| Open Access
Complex computation from developmental priors
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
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Article
| Open Access
Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity
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
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Article
| Open Access
Dynamical latent state computation in the male macaque posterior parietal cortex
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
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Article
| Open Access
Meta-learning biologically plausible plasticity rules with random feedback pathways
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
Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex