Computational neuroscience

It is unknown how humans establish stable visual percepts despite the incessant motion of their eyes. Here the authors report that visual judgments of spatial relations incorporate fine-scale motor knowledge of eye position.

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.

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.

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.

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.

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.

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.

Mapping brain connections is critical for decoding brain functions. Here, the authors present an integrated resource of awake resting-state fMRI and neuronal tracing data of marmosets to understand structural-functional relationships of brain connections.

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.

The default mode network (DMN) is known to be involved in consciousness. Here the authors show intrinsic EEG oscillations in default mode network can predict upcoming involuntarily perceptual transitions.

Previous work has described a neuroprosthesis to directly decode full words in real time during attempts to speak. Here the authors demonstrate that a patient with anarthria can control this neuroprosthesis to spell out intended messages in real time using attempts to silently speak.

How functional connectivity gradients in the cortex arise and vary dynamically is not fully understood. Here the authors show that gradients are determined by structural wiring but may be modulated by arousal levels.

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.

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.

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.

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.

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.

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.

The ability to infer the dynamics of physical objects is hypothesized to rely on running simulations of mental models. Here, the authors test this hypothesis by comparing human and monkey behavior to recurrent neural network models in a physical inference task.

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.

Center-surround receptive fields are typically considered to mediate edge detection. Here, by studying retinal bipolar cells responding to flashed and moving stimuli, the authors reveal an additional function: enhanced representation of newly appearing visual items.

Motion vision is critical for survival. Here the authors show that motion detection occurs already in bipolar cells of the mouse retina, which may contribute to motion processing throughout the visual system.

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.

Designing efficient bio-inspired vision systems remains a challenge. Here, the authors report a bio-inspired striate visual cortex with binocular and orientation selective receptive field based on self-powered memristor to enable machine vision with brisk edge and corner detection in the future applications.

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.

Activity in the superficial layers of the sensory cortex is believed to be largely driven by incoming sensory stimuli. Here the authors demonstrate how learning changes neural responses to sensations according to both behavioral relevance and timing, suggesting a high degree of non-sensory modulation.

How animals are able to rapidly adapt their behaviour to changing environmental demands remains poorly understood. Here, the authors use a modelling approach to show that synaptic plasticity in motor cortex may underlie rapid motor learning, demonstrating that small, correlated connectivity changes that preserve neural covariance are highly effective in driving behavioural adaptation.

How animals make multiple-choice decisions over three or more alternatives is not well understood. Here the authors use simulations to uncover that there is not one but many optimal parameter value configurations on the reward landscape of the multiple-choice threshold boundaries.

The brain connectome maps region-to-region connections but often ignores the role of the connecting pathways. Here, the authors mapped the tract-to-region relations to reveal the hierarchical relation of fiber bundles and dorsal, ventral, and limbic networks.

Although spontaneous brain activity is complex and clinically relevant, it is still unclear whether transitions in resting brain activity follow an underlying arrangement or whether they are unpredictable. In this work, the authors revealed a transition state of the brain that acts like a switch between states and forms the basis for the continuous evolution of brain activity patterns at rest.

Changes to structural and functional connectivity can give rise to neurodegeneration and neurodevelopmental diseases. Here the authors investigate molecular and connectomic patterns in 13 different neurological, psychiatric and neurodevelopmental diseases from the ENIGMA consortium.

Flies navigate to food sources by combining odour and wind-direction cues. This study identifies pathways to the fan-shaped body that encode these signals, and demonstrates how local neurons integrate odour- and wind information to guide navigation.

Dynamics of neural circuits mapping brain functions such as sensory processing and decision making, can be characterized by probabilistic representations and inference. The authors elaborate the role of spatiotemporal neural dynamics for more efficient performance of probabilistic computations.

Epilepsy is a brain network disorder with associated genetic risk factors. Here, the authors show that spatial patterns of transcriptomic vulnerability co-vary with structural brain network alterations in focal and generalized epilepsy.

Memory formation and recall are complementary processes within the hippocampus. Here the authors demonstrate a synaptic signal of novelty in the hippocampus and provide a computational framework for how such a novelty-driven switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones.

Compulsive alcohol drinking is a core feature of alcohol use disorder. Here the authors find that in rodents, neural signals in a key decision-making brain region (dmPFC) shift from behavioral control to alcohol seeking during compulsive alcohol drinking behaviour.

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.

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.

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.

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.

A leaky accumulation model can predict rats’ changes of mind during decision making in a dynamic environment explaining reversals in frontal cortical activity and demonstrating a stable choice code despite environmental uncertainty.

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.

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.

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.

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.

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.

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.

3’ untranslated regions (3’UTRs) play a crucial role in regulating gene expression, but our 3’UTR catalogue is incomplete. Here, the authors develop a machine learning-based framework to predict previously unannotated 3’UTRs in 39 human tissues.

Here, the authors use data from the Adolescent Brain Cognitive Development study to show how individual variation in cognition, personality and mental health can be predicted by shared and unique brain network features.