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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.

Key Points Dopamine neurons of the ventral tegmental area (VTA) have been theorized to play a part in various aspects of motivated behaviour These different behaviours may be mediated by different dopamine neurons interacting with specific neuronal networks The outputs of VTA neurons are integrated not only with inputs from several brain structures but also with those from local VTA GABA and glutamate neurons (forming a microcircuitry) Emerging evidence indicates that subpopulations of VTA GABA and glutamate neurons receive afferents from and project to the same brain regions that are connected to VTA dopamine neurons The VTA contains subpopulations of combinatorial neurons that co-release either glutamate or GABA with dopamine, as well as glutamate neurons that co-release GABA Optogenetic approaches in transgenic rodents have revealed discrete VTA neuronal phenotypes and connections that have distinct roles in reinforcement, motivation and learning Neurons in the ventral tegmental area (VTA) are highly heterogeneous and project to a range of different brain regions. Morales and Margolis summarize recent efforts to characterise VTA neurons, dissect their circuitry and understand their roles in motivation- and reward-related behaviours. Dopamine-releasing neurons of the ventral tegmental area (VTA) have central roles in reward-related and goal-directed behaviours. VTA dopamine-releasing neurons are heterogeneous in their afferent and efferent connectivity and, in some cases, release GABA or glutamate in addition to dopamine. Recent findings show that motivational signals arising from the VTA can also be carried by non-dopamine-releasing projection neurons, which have their own specific connectivity. Both dopamine-releasing and non-dopamine-releasing VTA neurons integrate afferent signals with local inhibitory or excitatory inputs to generate particular output firing patterns. Various individual inputs, outputs and local connections have been shown to be sufficient to generate reward- or aversion-related behaviour, indicative of the impressive contribution of this small population of neurons to behaviour.

Multichannel recording technologies have revealed travelling waves of neural activity in multiple sensory, motor and cognitive systems. These waves can be spontaneously generated by recurrent circuits or evoked by external stimuli. They travel along brain networks at multiple scales, transiently modulating spiking and excitability as they pass. Here, we review recent experimental findings that have found evidence for travelling waves at single-area (mesoscopic) and whole-brain (macroscopic) scales. We place these findings in the context of the current theoretical understanding of wave generation and propagation in recurrent networks. During the large low-frequency rhythms of sleep or the relatively desynchronized state of the awake cortex, travelling waves may serve a variety of functions, from long-term memory consolidation to processing of dynamic visual stimuli. We explore new avenues for experimental and computational understanding of the role of spatiotemporal activity patterns in the cortex.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery. While the contribution of sharp wave ripples in memory consolidation and decision-making is established in rodent models, our understanding of their role in human memory is incomplete. Here, the authors discuss common methodological challenges in detecting, analyzing, and reporting sharp wave ripples, then they suggest practical solutions to distinguish them from other high-frequency events

Nanopores are single-molecule sensors used in nucleic acid analysis, whereas their applicability towards full protein identification has yet to be demonstrated. Here, we show that an engineered Fragaceatoxin C nanopore is capable of identifying individual proteins by measuring peptide spectra that are produced from hydrolyzed proteins. Using model proteins, we show that the spectra resulting from nanopore experiments and mass spectrometry share similar profiles, hence allowing protein fingerprinting. The intensity of individual peaks provides information on the concentration of individual peptides, indicating that this approach is quantitative. Our work shows the potential of a low-cost, portable nanopore-based analyzer for protein identification. Peptide mass fingerprinting is a traditional approach for protein identification by mass spectrometry. Here, the authors provide evidence that peptide mass fingerprinting is also feasible using FraC nanopores, demonstrating protein identification based on nanopore measurements of digested peptides.

Key Points Hippocampal time cells fire at successive moments in temporally structured experiences. Temporal coding in the hippocampus is observed across a broad range of behavioural tasks and in different animal species and humans. Time cells cannot be explained by variations in location or movement through space. Time cells also encode spatial variables and other dimensions of specific events. Time cells provide a mechanism for the temporal organization of episodic memories. The recently discovered hippocampal 'time cells' are thought to represent the flow of time in specific memories. In this Review, Howard Eichenbaum discusses the evidence for the existence of time cells, describes their characteristics and relationship with place cells, and considers their role in memory. Recent studies have revealed the existence of hippocampal neurons that fire at successive moments in temporally structured experiences. Several studies have shown that such temporal coding is not attributable to external events, specific behaviours or spatial dimensions of an experience. Instead, these cells represent the flow of time in specific memories and have therefore been dubbed 'time cells'. The firing properties of time cells parallel those of hippocampal place cells; time cells thus provide an additional dimension that is integrated with spatial mapping. The robust representation of both time and space in the hippocampus suggests a fundamental mechanism for organizing the elements of experience into coherent memories.

Patients with amyotrophic lateral sclerosis (ALS) can lose all muscle-based routes of communication as motor neuron degeneration progresses, and ultimately, they may be left without any means of communication. While others have evaluated communication in people with remaining muscle control, to the best of our knowledge, it is not known whether neural-based communication remains possible in a completely locked-in state. Here, we implanted two 64 microelectrode arrays in the supplementary and primary motor cortex of a patient in a completely locked-in state with ALS. The patient modulated neural firing rates based on auditory feedback and he used this strategy to select letters one at a time to form words and phrases to communicate his needs and experiences. This case study provides evidence that brain-based volitional communication is possible even in a completely locked-in state. The authors record neural firing rates in a patient with ALS in completely locked-in state and show that the patient can modulate neural firing rates based on auditory feedback to select letters to form words and phrases to communicate his needs and experiences.

ABSTRACT Single-molecule counting is the most accurate and precise method for determining the concentration of a biomarker in solution and is leading to the emergence of digital diagnostic platforms enabling precision medicine. In principle, solid-state nanopores—fully electronic sensors with single-molecule sensitivity—are well suited to the task. Here we present a digital immunoassay scheme capable of reliably quantifying the concentration of a target protein in complex biofluids that overcomes specificity, sensitivity, and consistency challenges associated with the use of solid-state nanopores for protein sensing. This is achieved by employing easily-identifiable DNA nanostructures as proxies for the presence (“1”) or absence (“0”) of the target protein captured via a magnetic bead-based sandwich immunoassay. As a proof-of-concept, we demonstrate quantification of the concentration of thyroid-stimulating hormone from human serum samples down to the high femtomolar range. Further optimization to the method will push sensitivity and dynamic range, allowing for development of precision diagnostic tools compatible with point-of-care format. The concentration of a biomarker in solution can be determined by counting single molecules. Here the authors report a digital immunoassay scheme with solid-state nanopore readout to quantify a target protein and use this to measure thyroid-stimulating hormone from human serum.

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.

There are still unmet needs in finding new technologies for biomedical diagnostic and industrial applications. A technology allowing the analysis of size and sequence of short peptide molecules of only few molecular copies is still challenging. The fast, low-cost and label-free single-molecule nanopore technology could be an alternative for addressing these critical issues. Here, we demonstrate that the wild-type aerolysin nanopore enables the size-discrimination of several short uniformly charged homopeptides, mixed in solution, with a single amino acid resolution. Our system is very sensitive, allowing detecting and characterizing a few dozens of peptide impurities in a high purity commercial peptide sample, while conventional analysis techniques fail to do so. Existing methods used for peptide analysis suffer from low sensitivity and specificity. Here the authors demonstrate the nanopore-based size-discrimination and purity analysis of short homopolymeric peptides with a single amino acid resolution.