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Vision, choice, action and behavioural engagement arise from neuronal activity that may be distributed across brain regions. Here we delineate the spatial distribution of neurons underlying these processes. We used Neuropixels probes 1 , 2 to record from approximately 30,000 neurons in 42 brain regions of mice performing a visual discrimination task 3 . Neurons in nearly all regions responded non-specifically when the mouse initiated an action. By contrast, neurons encoding visual stimuli and upcoming choices occupied restricted regions in the neocortex, basal ganglia and midbrain. Choice signals were rare and emerged with indistinguishable timing across regions. Midbrain neurons were activated before contralateral choices and were suppressed before ipsilateral choices, whereas forebrain neurons could prefer either side. Brain-wide pre-stimulus activity predicted engagement in individual trials and in the overall task, with enhanced subcortical but suppressed neocortical activity during engagement. These results reveal organizing principles for the distribution of neurons encoding behaviourally relevant variables across the mouse brain. Recordings from 30,000 neurons in 42 brain regions are used to delineate the spatial distribution of neuronal activity underlying vision, choice, action and behavioural engagement in mice.

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

The cortex projects to the dorsal striatum topographically 1 , 2 to regulate behaviour 3 – 5 , but spiking activity in the two structures has previously been reported to have markedly different relations to sensorimotor events 6 – 9 . Here we show that the relationship between activity in the cortex and striatum is spatiotemporally precise, topographic, causal and invariant to behaviour. We simultaneously recorded activity across large regions of the cortex and across the width of the dorsal striatum in mice that performed a visually guided task. Striatal activity followed a mediolateral gradient in which behavioural correlates progressed from visual cue to response movement to reward licking. The summed activity in each part of the striatum closely and specifically mirrored activity in topographically associated cortical regions, regardless of task engagement. This relationship held for medium spiny neurons and fast-spiking interneurons, whereas the activity of tonically active neurons differed from cortical activity with stereotypical responses to sensory or reward events. Inactivation of the visual cortex abolished striatal responses to visual stimuli, supporting a causal role of cortical inputs in driving the striatum. Striatal visual responses were larger in trained mice than untrained mice, with no corresponding change in overall activity in the visual cortex. Striatal activity therefore reflects a consistent, causal and scalable topographical mapping of cortical activity. Simultaneous mapping of activity across the cortex and dorsal striatum in mice shows that activity in each part of the striatum precisely mirrors that in topographically associated cortical regions, consistently across behavioural contexts.

Persistent and ramping neural activity in the frontal cortex anticipates specific movements 1 – 6 . Preparatory activity is distributed across several brain regions 7 , 8 , but it is unclear which brain areas are involved and how this activity is mediated by multi-regional interactions. The cerebellum is thought to be primarily involved in the short-timescale control of movement 9 – 12 ; however, roles for this structure in cognitive processes have also been proposed 13 – 16 . In humans, cerebellar damage can cause defects in planning and working memory 13 . Here we show that persistent representation of information in the frontal cortex during motor planning is dependent on the cerebellum. Mice performed a sensory discrimination task in which they used short-term memory to plan a future directional movement. A transient perturbation in the medial deep cerebellar nucleus (fastigial nucleus) disrupted subsequent correct responses without hampering movement execution. Preparatory activity was observed in both the frontal cortex and the cerebellar nuclei, seconds before the onset of movement. The silencing of frontal cortex activity abolished preparatory activity in the cerebellar nuclei, and fastigial activity was necessary to maintain cortical preparatory activity. Fastigial output selectively targeted the behaviourally relevant part of the frontal cortex through the thalamus, thus closing a cortico-cerebellar loop. Our results support the view that persistent neural dynamics during motor planning is maintained by neural circuits that span multiple brain regions 17 , and that cerebellar computations extend beyond online motor control 13 – 15 , 18 . The cerebellum is critical for the coding of future movement in the frontal cortex.

Key Points Visual stimuli in the real world are often highly predictable on the basis of spatial context, transition probabilities and prior information from previous glances. Normative Bayesian models dictate how expectations (that is, the prior) should be combined with incoming sensory evidence (that is, the likelihood) for optimal perceptual inference. Prior information about upcoming percepts modulates baseline neural activity in sensory neurons encoding the expected stimulus and in decision-related neurons that integrate the sensory evidence. Predictive coding is a neurobiologically plausible computational framework that seeks to explain how top-down priors and bottom-up inputs are combined. Expectation and attention are often entangled but they are conceptually distinct. Expectation relates to the probability of a sensory event, whereas selective attention pertains to the relevance of a sensory event. A stimulus can be probable or improbable, irrespective of its behavioural relevance. During decision making, expectation can alter the gain of information processing towards stimuli that are expected to occur. Visual stimuli can often be predicted by other stimuli in the environment — for example, a barking sound would predict the sight of a dog but not a cat. In this Review, Summerfield and de Lange discuss how expectation modulates neural signals and behaviour in response to visual stimuli. Sensory signals are highly structured in both space and time. These structural regularities in visual information allow expectations to form about future stimulation, thereby facilitating decisions about visual features and objects. Here, we discuss how expectation modulates neural signals and behaviour in humans and other primates. We consider how expectations bias visual activity before a stimulus occurs, and how neural signals elicited by expected and unexpected stimuli differ. We discuss how expectations may influence decision signals at the computational level. Finally, we consider the relationship between visual expectation and related concepts, such as attention and adaptation.

While judging their sensory environments, decision-makers seem to use the uncertainty about their choices to guide adjustments of their subsequent behaviour. One possible source of these behavioural adjustments is arousal: decision uncertainty might drive the brain’s arousal systems, which control global brain state and might thereby shape subsequent decision-making. Here, we measure pupil diameter, a proxy for central arousal state, in human observers performing a perceptual choice task of varying difficulty. Pupil dilation, after choice but before external feedback, reflects three hallmark signatures of decision uncertainty derived from a computational model. This increase in pupil-linked arousal boosts observers’ tendency to alternate their choice on the subsequent trial. We conclude that decision uncertainty drives rapid changes in pupil-linked arousal state, which shape the serial correlation structure of ongoing choice behaviour. Decision uncertainty is often associated with higher order cognition and can impact choices. Here the authors show that post-decision pupil dilation scales with uncertainty and predicts a change in upcoming choice patterns.

Noradrenaline released from the locus coeruleus (LC) is a ubiquitous neuromodulator 1 – 4 that has been linked to multiple functions including arousal 5 – 8 , action and sensory gain 9 – 11 , and learning 12 – 16 . Whether and how activation of noradrenaline-expressing neurons in the LC (LC-NA) facilitates different components of specific behaviours is unknown. Here we show that LC-NA activity displays distinct spatiotemporal dynamics to enable two functions during learned behaviour: facilitating task execution and encoding reinforcement to improve performance accuracy. To examine these functions, we used a behavioural task in mice with graded auditory stimulus detection and task performance. Optogenetic inactivation of the LC demonstrated that LC-NA activity was causal for both task execution and optimization. Targeted recordings of LC-NA neurons using photo-tagging, two-photon micro-endoscopy and two-photon output monitoring showed that transient LC-NA activation preceded behavioural execution and followed reinforcement. These two components of phasic activity were heterogeneously represented in LC-NA cortical outputs, such that the behavioural response signal was higher in the motor cortex and facilitated task execution, whereas the negative reinforcement signal was widely distributed among cortical regions and improved response sensitivity on the subsequent trial. Modular targeting of LC outputs thus enables diverse functions, whereby some noradrenaline signals are segregated among targets, whereas others are broadly distributed. Noradrenaline-expressing neurons in the locus coeruleus in mouse facilitate task execution and encode reinforcement in learning tasks, via partially modular projections to the cortex.

Key Points In primates, number neurons in a dedicated parieto-frontal network encode the number of elements in a stimulus. Number neurons in the prefrontal cortex respond in an abstract manner, and their responses generalize across spatial, temporal and visuo-auditory item presentations. Number neurons are present in numerically naive monkeys, suggesting that the brain is hard-wired to extract number. Number processing provides mechanistic insight into how relevant information is selected and maintained in working memory. Rule neurons and neuron populations in the frontal lobe guide decisions based on number information. Primates have a functional network in frontal and parietal cortices that allows them to quantify the number of elements in a stimulus; that is, its numerosity or cardinality. In this Review, Andreas Nieder examines how neurons in this network process cardinal numbers. Humans and non-human primates share an elemental quantification system that resides in a dedicated neural network in the parietal and frontal lobes. In this cortical network, 'number neurons' encode the number of elements in a set, its cardinality or numerosity, irrespective of stimulus appearance across sensory motor systems, and from both spatial and temporal presentation arrays. After numbers have been extracted from sensory input, they need to be processed to support goal-directed behaviour. Studying number neurons provides insights into how information is maintained in working memory and transformed in tasks that require rule-based decisions. Beyond an understanding of how cardinal numbers are encoded, number processing provides a window into the neuronal mechanisms of high-level brain functions.

Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs.

Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. Here we show that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, we reconstructed the multisensory circuit supporting the synergy, spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, we identified functionally connected circuit nodes that trigger the fastest locomotor mode, and others that facilitate it, and we provide evidence that multiple levels of multimodal integration contribute to escape mode selection. We propose that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input–output functions and selective tuning to ecologically relevant combinations of cues. Combining neural manipulation in freely behaving animals, physiological studies and electron microscopy reconstruction in the Drosophila larva identifies a complex multilsensory circuit involved in the selection of larval escape modes that exhibits a multilevel multimodal convergence architecture. A multisensory circuit in Drosophila larvae When making decisions, animals must integrate diverse sensory stimuli but whether multi-modal sensory information is combined early or late during information processing is largely unknown. Using neural manipulation in freely behaving animals, combined with physiological studies and electron microscopy reconstruction, Marta Zlatic and colleagues have tracked all 138 neurons — among many thousands — that allow the Drosophila larva to escape mechanical or nociceptive stimuli. They map full functional connectivity at single-synapse resolution. The resulting connectome reveals a complex multilevel convergence architecture in which the two signalling pathways converge and interact at every stage, from sensory neurons to interneurons and motor neurons, which increases both the sensitivity of the system and the richness of its input–output functions. The availability of this multisensory circuit in a genetically tractable model system provides a resource for investigating multiple brain and nerve cord pathway interactions.