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Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks
How internal states such as confidence and motivation influence motor performance remains unclear. Here, the authors explore brain networks associated with these internal states, finding that the Dorsal Attention Network encodes error states and the Default Network reflects perceived uncertainty.
- Macauley Smith Breault
- , Pierre Sacré
- & Sridevi V. Sarma
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Article
| Open Access
Motion direction is represented as a bimodal probability distribution in the human visual cortex
The visual system quickly infers an object’s direction of motion from noisy sensory signals. Here, the authors show that orientation signals are used in this process, leading to bimodal probabilistic representations of motion direction in the human cortex.
- Andrey Chetverikov
- & Janneke F. M. Jehee
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Article
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Online conversion of reconstructed neural morphologies into standardized SWC format
There are a number of programs available for digital reconstruction of neural morphology. Here the authors present a standardized specification of the SWC file format, and introduce xyz2swc, a tool that converts reconstruction formats described in the literature into the SWC standard.
- Ketan Mehta
- , Bengt Ljungquist
- & Giorgio A. Ascoli
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Change detection in the primate auditory cortex through feedback of prediction error signals
The brain can quickly detect sounds that are not predicted. Here, the authors show that propagation of prediction error signals from higher-order auditory cortex to primary auditory cortex is critical for the change detection in the non-human primates.
- Keitaro Obara
- , Teppei Ebina
- & Masanori Matsuzaki
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Article
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Virtual lesions in MEG reveal increasing vulnerability of the language network from early childhood through adolescence
The robustness of the brain’s language network to injury throughout development is not well understood. Here, the authors use an MEG dataset of children listening to stories to show that the brain connectivity of younger children is more robust to simulated lesions.
- Brady J. Williamson
- , Hansel M. Greiner
- & Darren S. Kadis
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Article
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Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
The cortex contains a recurrent network of stochastically spiking neurons that performs many of the computations underlying perception and behavior. Here, the authors show how such networks can implement sampling-based probabilistic inference.
- Wen-Hao Zhang
- , Si Wu
- & Brent Doiron
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Article
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Neural and computational underpinnings of biased confidence in human reinforcement learning
The mechanism of confidence formation in learning remains poorly understood. Here, the authors show that both dorsal and ventral prefrontal networks encode confidence, but only the ventral network incorporates the valence-induced bias.
- Chih-Chung Ting
- , Nahuel Salem-Garcia
- & Maël Lebreton
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Emergence of cortical network motifs for short-term memory during learning
How learning refines the coordinated activitity of neurons across multiple regions of the mouse cortex remains unclear. Here, the authors identified the emergence of cortical subnetworks during learning of a sensorimotor task.
- Xin Wei Chia
- , Jian Kwang Tan
- & Hiroshi Makino
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Article
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Acetylcholine waves and dopamine release in the striatum
Dopamine release occurs in spatiotemporal waves. Here the authors propose that dopamine waves arise locally in the striatum, and provide evidence for striatal acetylcholine waves.
- Lior Matityahu
- , Naomi Gilin
- & Joshua A. Goldberg
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Article
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Early selection of task-relevant features through population gating
How the brain selects relevant information in complex and dynamic environments remains poorly understood. Here, the authors reveal that distinct neural populations in rat auditory cortex gate stimuli based on context, which could be facilitated by top-down signals from the prefrontal cortex.
- Joao Barbosa
- , Rémi Proville
- & Yves Boubenec
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Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states
Here the authors describe a biophysical layer-5 pyramidal neuronal model linking microscale spiking to macroscale complex dynamics, that predicts distinct burst dynamics and information processing across unconscious, dreaming, and awake states.
- Brandon R. Munn
- , Eli J. Müller
- & James M. Shine
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Article
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Transcriptional dissection of symptomatic profiles across the brain of men and women with depression
Recent research sheds light on sex-specific molecular changes in the brains of MDD patients, but their association with specific symptoms is still uncertain. Here, the authors revealed the existence of gene signatures underlying the expression of distinct symptom domains in the brain of men and women with depression.
- Samaneh Mansouri
- , André M. Pessoni
- & Benoit Labonté
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Article
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Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
Brain structure and function have been observed to be increasingly untethered in transmodal cortex. Here, the authors show that structure-function coupling in these areas can be enhanced by diffusion patterns governed by high-frequency eigenmodes.
- Yaqian Yang
- , Zhiming Zheng
- & Shaoting Tang
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Article
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Domain adapted brain network fusion captures variance related to pubertal brain development and mental health
Charting variance in brain development across adolescence remains a challenge. Here, the authors show that domain adapted brain network fusion based on subject similarities in brain structure is associated with puberty and mental health in two independent cohorts.
- Dominik Kraft
- , Dag Alnæs
- & Tobias Kaufmann
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Continuous multiplexed population representations of task context in the mouse primary visual cortex
Sensory cortex has been primarily shown to represent environmental stimuli. Here, the authors find that the geometry of visual cortical activity permits the parallel representation of stimuli and task context in a format that prevents interference.
- Márton Albert Hajnal
- , Duy Tran
- & Gergő Orbán
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Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning
The functional role of the human thalamus in reinforcement learning is debated. Here, using intra-thalamic recordings in humans, the authors report that thalamic low-frequency oscillations correlate with variables for learning from both reward and punishment.
- Antoine Collomb-Clerc
- , Maëlle C. M. Gueguen
- & Julien Bastin
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Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex
It remains unclear why some decisions take longer than others even when the sensory inputs are similar. Here, the authors show that both initial neural state and sensory input combine in the premotor cortex to influence the speed and geometry of neural population activity during decisions.
- Pierre O. Boucher
- , Tian Wang
- & Chandramouli Chandrasekaran
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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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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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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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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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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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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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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
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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
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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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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
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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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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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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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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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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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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
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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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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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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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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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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
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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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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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Article
| 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
Augmenting interpretable models with large language models during training