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How is it that groups of neurons dispersed through the brain interact to generate complex behaviors? Three papers in this issue present brain-scale studies of neuronal activity and dynamics (see the Perspective by Huk and Hart). Allen et al. found that in thirsty mice, there is widespread neural activity related to stimuli that elicit licking and drinking. Individual neurons encoded task-specific responses, but every brain area contained neurons with different types of response. Optogenetic stimulation of thirst-sensing neurons in one area of the brain reinstated drinking and neuronal activity across the brain that previously signaled thirst. Gründemann et al. investigated the activity of mouse basal amygdala neurons in relation to behavior during different tasks. Two ensembles of neurons showed orthogonal activity during exploratory and nonexploratory behaviors, possibly reflecting different levels of anxiety experienced in these areas. Stringer et al. analyzed spontaneous neuronal firing, finding that neurons in the primary visual cortex encoded both visual information and motor activity related to facial movements. The variability of neuronal responses to visual stimuli in the primary visual area is mainly related to arousal and reflects the encoding of latent behavioral states. Science , this issue p. eaav3932 , p. eaav8736 , p. eaav7893 ; see also p. 236 Neurons in the primary visual cortex encode both visual information and motor activity. Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed ‘inception loops’, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli—most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.

A working memory task in rats demonstrates that the posterior parietal cortex is a critical locus for the representation and use of prior stimulus information. How sensory history affects behaviour Recent sensory experiences, even when irrelevant to the current task at hand, bias memory and perception in humans and monkeys. Carlos Brody and colleagues show that sensory stimulus history also influences the working memory of rats. Silencing the activity of the posterior parietal cortex (PPC), an area previously implicated in working memory, paradoxically improved the rats' performance in a memory and behaviour task—identifying and reporting the loudest of two auditory stimuli. This improvement was due to the selective reduction of the effects of previous sensory stimuli. Electrophysiological recordings showed that PPC neurons carried more information about sensory stimuli of previous trials than about stimuli of the current trial. These findings suggest a role for PPC in maintaining information on recent sensory history. Many models of cognition and of neural computations posit the use and estimation of prior stimulus statistics 1 , 2 , 3 , 4 : it has long been known that working memory and perception are strongly impacted by previous sensory experience, even when that sensory history is not relevant to the current task at hand. Nevertheless, the neural mechanisms and regions of the brain that are necessary for computing and using such prior experience are unknown. Here we report that the posterior parietal cortex (PPC) is a critical locus for the representation and use of prior stimulus information. We trained rats in an auditory parametric working memory task, and found that they displayed substantial and readily quantifiable behavioural effects of sensory-stimulus history, similar to those observed in humans 5 , 6 and monkeys 7 . Earlier proposals that the PPC supports working memory 8 , 9 predict that optogenetic silencing of this region would impair behaviour in our working memory task. Contrary to this prediction, we found that silencing the PPC significantly improved performance. Quantitative analyses of behaviour revealed that this improvement was due to the selective reduction of the effects of prior sensory stimuli. Electrophysiological recordings showed that PPC neurons carried far more information about the sensory stimuli of previous trials than about the stimuli of the current trial. Furthermore, for a given rat, the more information about previous trial sensory history in the neural firing rates of the PPC, the greater the behavioural effect of sensory history, suggesting a tight link between behaviour and PPC representations of stimulus history. Our results indicate that the PPC is a central component in the processing of sensory-stimulus history, and could enable further neurobiological investigation of long-standing questions regarding how perception and working memory are affected by prior sensory information.

It is generally believed that cells in the gut transduce sensory information through the paracrine action of hormones. Kaelberer et al. found that, in addition to the well-described classical paracrine transduction, enteroendocrine cells also form fast, excitatory synapses with vagal afferents (see the Perspective by Hoffman and Lumpkin). This more direct circuit for gut-brain signaling uses glutamate as a neurotransmitter. Thus, sensory cues that stimulate the gut could potentially be manipulated to influence specific brain functions and behavior, including those linked to food choices. Science , this issue p. eaat5236 ; see also p. 1203 A neuroepithelial circuit that connects the intestinal lumen to the brain stem in one synapse has been identified. The brain is thought to sense gut stimuli only via the passive release of hormones. This is because no connection has been described between the vagus and the putative gut epithelial sensor cell—the enteroendocrine cell. However, these electrically excitable cells contain several features of epithelial transducers. Using a mouse model, we found that enteroendocrine cells synapse with vagal neurons to transduce gut luminal signals in milliseconds by using glutamate as a neurotransmitter. These synaptically connected enteroendocrine cells are referred to henceforth as neuropod cells. The neuroepithelial circuit they form connects the intestinal lumen to the brainstem in one synapse, opening a physical conduit for the brain to sense gut stimuli with the temporal precision and topographical resolution of a synapse.

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

Cognitive control guides behaviour by controlling what, when, and how information is represented in the brain 1 . For example, attention controls sensory processing; top-down signals from prefrontal and parietal cortex strengthen the representation of task-relevant stimuli 2 – 4 . A similar ‘selection’ mechanism is thought to control the representations held ‘in mind’—in working memory 5 – 10 . Here we show that shared neural mechanisms underlie the selection of items from working memory and attention to sensory stimuli. We trained rhesus monkeys to switch between two tasks, either selecting one item from a set of items held in working memory or attending to one stimulus from a set of visual stimuli. Neural recordings showed that similar representations in prefrontal cortex encoded the control of both selection and attention, suggesting that prefrontal cortex acts as a domain-general controller. By contrast, both attention and selection were represented independently in parietal and visual cortex. Both selection and attention facilitated behaviour by enhancing and transforming the representation of the selected memory or attended stimulus. Specifically, during the selection task, memory items were initially represented in independent subspaces of neural activity in prefrontal cortex. Selecting an item caused its representation to transform from its own subspace to a new subspace used to guide behaviour. A similar transformation occurred for attention. Our results suggest that prefrontal cortex controls cognition by dynamically transforming representations to control what and when cognitive computations are engaged. The prefrontal cortex in monkeys controls working memory in a similar way to attention, by selectively transforming the representations of remembered items.

Posterior parietal cortex (PPC) activity correlates with monkeys’ decisions during visual discrimination and categorization tasks. However, recent work has questioned whether decision-correlated PPC activity plays a causal role in such decisions. That study focused on PPC’s contribution to motor aspects of decisions (deciding where to move), but not sensory evaluation aspects (deciding what you are looking at). We employed reversible inactivation to compare PPC’s contributions to motor and sensory aspects of decisions. Inactivation affected both aspects of behavior, but preferentially impaired decisions when visual stimuli, rather than motor response targets, were in the inactivated visual field. This demonstrates a causal role for PPC in decision-making, with preferential involvement in evaluating attended task-relevant sensory stimuli compared with motor planning.

Triggering behavioral adaptation upon the detection of adversity is crucial for survival. The insular cortex has been suggested to process emotions and homeostatic signals, but how the insular cortex detects internal states and mediates behavioral adaptation is poorly understood. By combining data from fiber photometry, optogenetics, awake two-photon calcium imaging and comprehensive whole-brain viral tracings, we here uncover a role for the posterior insula in processing aversive sensory stimuli and emotional and bodily states, as well as in exerting prominent top-down modulation of ongoing behaviors in mice. By employing projection-specific optogenetics, we describe an insula-to-central amygdala pathway to mediate anxiety-related behaviors, while an independent nucleus accumbens-projecting pathway regulates feeding upon changes in bodily state. Together, our data support a model in which the posterior insular cortex can shift behavioral strategies upon the detection of aversive internal states, providing a new entry point to understand how alterations in insula circuitry may contribute to neuropsychiatric conditions.

Distinguishing sensory stimuli caused by changes in the environment from those caused by an animal’s own actions is a hallmark of sensory processing 1 . Saccades are rapid eye movements that shift the image on the retina. How visual systems differentiate motion of the image induced by saccades from actual motion in the environment is not fully understood 2 . Here we discovered that in mouse primary visual cortex (V1) the two types of motion evoke distinct activity patterns. This is because, during saccades, V1 combines the visual input with a strong non-visual input arriving from the thalamic pulvinar nucleus. The non-visual input triggers responses that are specific to the direction of the saccade and the visual input triggers responses that are specific to the direction of the shift of the stimulus on the retina, yet the preferred directions of these two responses are uncorrelated. Thus, the pulvinar input ensures differential V1 responses to external and self-generated motion. Integration of external sensory information with information about body movement may be a general mechanism for sensory cortices to distinguish between self-generated and external stimuli. Distinct activity patterns in the primary visual cortex distinguish movement in the environment from motion caused by eye movements.

Individuals with Autism Spectrum Disorders (ASDs) tend to have sensory processing difficulties (Baranek et al. in J Child Psychol Psychiatry 47:591–601, 2006 ). These difficulties include over- and under-responsiveness to sensory stimuli, and problems modulating sensory input (Ben-Sasson et al. in J Autism Dev Disorders 39:1–11, 2009 ). As those with ASD exist at the extreme end of a continuum of autistic traits that is also evident in the general population, we investigated the link between ASD and sensory sensitivity in the general population by administering two questionnaires online to 212 adult participants. Results showed a highly significant positive correlation ( r  = .775, p  < .001) between number of autistic traits and the frequency of sensory processing problems. These data suggest a strong link between sensory processing and autistic traits in the general population, which in turn potentially implicates sensory processing problems in social interaction difficulties.