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Neocortex is classically divided into distinct areas, each specializing in different function, but all could benefit from reinforcement feedback to inform and update local processing. Yet it remains elusive how global signals like reward and punishment are represented in local cortical computations. Previously, we identified a cortical neuron type, vasoactive intestinal polypeptide (VIP)-expressing interneurons, in auditory cortex that is recruited by behavioral reinforcers and mediates disinhibitory control by inhibiting other inhibitory neurons. As the same disinhibitory cortical circuit is present virtually throughout cortex, we wondered whether VIP neurons are likewise recruited by reinforcers throughout cortex. We monitored VIP neural activity in dozens of cortical regions using three-dimensional random access two-photon microscopy and fiber photometry while mice learned an auditory discrimination task. We found that reward and punishment during initial learning produce rapid, cortex-wide activation of most VIP interneurons. This global recruitment mode showed variations in temporal dynamics in individual neurons and across areas. Neither the weak sensory tuning of VIP interneurons in visual cortex nor their arousal state modulation was fully predictive of reinforcer responses. We suggest that the global response mode of cortical VIP interneurons supports a cell-type-specific circuit mechanism by which organism-level information about reinforcers regulates local circuit processing and plasticity.

Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function.

The use of nanophotonics to rapidly and precisely reconfigure light beams for the optical stimulation of neurons in vivo has remained elusive. Here we report the design and fabrication of an implantable silicon-based probe that can switch and route multiple optical beams to stimulate identified sets of neurons across cortical layers and simultaneously record the produced spike patterns. Each switch in the device consists of a silicon nitride waveguide structure that can be rapidly (

How to model hallucinations in mice There has not been enough progress in our understanding of the basic mechanisms underlying psychosis. Studying psychotic disorders in animal models is difficult because the diagnosis relies on self-reported symptoms that can only be assessed in humans. Schmack et al. developed a paradigm to probe and rigorously measure experimentally controlled hallucinations in rodents (see the Perspective by Matamales). Using dopamine-sensor measurements and circuit and pharmacological manipulations, they demonstrated a brain circuit link between excessive dopamine and hallucination-like experience. This could potentially be useful as a translational model of common psychotic symptoms described in various psychiatric disorders. It may also help in the development of new therapeutic approaches based on anatomically selective modulation of dopamine function. Science , this issue p. eabf4740 ; see also p. 33

Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, reinforcement learning mechanisms are continually engaged to produce systematic adjustments of choices even in well-learned perceptual decisions in order to optimize behavior in an uncertain world.

The excessive accumulation of extracellular matrix material in the kidney is a histopathologic hallmark of diabetic kidney disease that correlates closely with declining function. Although considerable research has focused on the role of profibrotic factors, comparatively little attention has been paid to the possibility that a diminution in endogenous antifibrotic factors may also contribute. Among the latter, the ELR− CXC chemokines, CXCL9, CXCL10, and CXCL11, have been shown to provide a stop signal to prevent excessive fibrosis. Although the plasma concentrations of CXCL9 and CXCL11 were similar, those of CXCL10 were markedly lower in diabetic db/db mice compared with control db/m mice. In cell culture, CXCL10 inhibited kidney fibroblast collagen production in response to high glucose and the prosclerotic growth factor, transforming growth factor-β. In vivo, recombinant murine CXCL10 reduced mesangial and peritubular matrix expansion, albuminuria, and glomerular hypertrophy in db/db mice. In bone marrow, a major source of circulating chemokines, the concentration of CXCL10 was lower in cells derived from diabetic mice than from their nondiabetic counterparts. Silencing of CXCR3, the cognate receptor for CXCL10, abrogated the antifibrotic effects of bone marrow–derived secretions. In conclusion, experimental diabetes is a state of CXCL10 deficiency and that restoration of CXCL10 abundance prevented fibrosis and the development of diabetic kidney disease in mice.

Engineered bacteria for therapeutic applications would benefit from control mechanisms that confine the growth of the bacteria within specific tissues or regions in the body. Here we show that the tropism of engineered bacteria can be enhanced by coupling bacterial growth with genetic circuits that sense oxygen, pH or lactate through the control of the expression of essential genes. Bacteria that were engineered with pH or oxygen sensors showed preferential growth in physiologically relevant acidic or oxygen conditions, and reduced growth outside the permissive environments when orally delivered to mice. In syngeneic mice bearing subcutaneous tumours, bacteria engineered with both hypoxia and lactate biosensors coupled through an AND gate showed increased tumour specificity. The multiplexing of genetic circuits may be more broadly applicable for enhancing the localization of bacteria to specified niches. The tropism of engineered bacteria in vivo can be enhanced by genetic circuits, linked by AND-gate logic, that couple bacterial sensing and growth.

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In the case of artificial neural networks, the three components specified by design are the objective functions, the learning rules, and architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Immunological synapse formation between cytotoxic T lymphocytes (CTLs) and the target cells they aim to destroy is accompanied by reorientation of the CTL centrosome to a position beneath the synaptic membrane. Centrosome polarization is thought to enhance the potency and specificity of killing by driving lytic granule fusion at the synapse and thereby the release of perforin and granzymes toward the target cell. To test this model, we employed a genetic strategy to delete centrioles, the core structural components of the centrosome. Centriole deletion altered microtubule architecture, as expected, but surprisingly had no effect on lytic granule polarization and directional secretion. Nevertheless, CTLs lacking centrioles did display substantially reduced killing potential, which was associated with defects in both lytic granule biogenesis and synaptic actin remodeling. These results reveal an unexpected role for the intact centrosome in controlling the capacity, but not the specificity, of cytotoxic killing.

Deciding between stimuli requires combining their learned value with one's sensory confidence. We trained mice in a visual task that probes this combination. Mouse choices reflected not only present confidence and past rewards but also past confidence. Their behavior conformed to a model that combines signal detection with reinforcement learning. In the model, the predicted value of the chosen option is the product of sensory confidence and learned value. We found precise correlates of this variable in the pre-outcome activity of midbrain dopamine neurons and of medial prefrontal cortical neurons. However, only the latter played a causal role: inactivating medial prefrontal cortex before outcome strengthened learning from the outcome. Dopamine neurons played a causal role only after outcome, when they encoded reward prediction errors graded by confidence, influencing subsequent choices. These results reveal neural signals that combine reward value with sensory confidence and guide subsequent learning.

Summary Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more—whether time, effort, or money—when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions.

Organisms have evolved diverse behavioural strategies that enhance the likelihood of encountering and assessing mates

Emotional processes are central to behavior, yet their deeply subjective nature has been a challenge for neuroscientific study as well as for psychiatric diagnosis. Here we explore the relationships between subjective feelings and their underlying brain circuits from a computational perspective. We apply recent insights from systems neuroscience-approaching subjective behavior as the result of mental computations instantiated in the brain-to the study of emotions. We develop the hypothesis that emotions are the product of neural computations whose motor role is to reallocate bodily resources mostly gated by smooth muscles. This "emotor" control system is analagous to the more familiar motor control computations that coordinate skeletal muscle movements. To illustrate this framework, we review recent research on "confidence." Although familiar as a feeling, confidence is also an objective statistical quantity: an estimate of the probability that a hypothesis is correct. This model-based approach helped reveal the neural basis of decision confidence in mammals and provides a bridge to the subjective feeling of confidence in humans. These results have important implications for psychiatry, since disorders of confidence computations appear to contribute to a number of psychopathologies. More broadly, this computational approach to emotions resonates with the emerging view that psychiatric nosology may be best parameterized in terms of disorders of the cognitive computations underlying complex behavior.Los procesos emocionales son centrales para la conducta, pero su naturaleza intensamente subjetiva ha sido un desafío para el estudio neurocientífico y el diagnóstico psiquiátrico. En este artículo se exploran las relaciones entre los sentimientos subjetivos y los circuitos cerebrales subyacentes desde una perspectiva computacional. Para el estudio de las emociones se aplican conocimientos recientes de la neurociencia de sistemas, planteándose la conducta subjetiva como el resultado de cálculos mentales que se ejemplifican concretamente en el cerebro. Se desarrolla la hipótesis que las emociones son el producto de cálculos neurales cuyo papel motor es redistribuir los recursos corporales regulados por los músculos lisos, análogo a los cálculos del control motor que coordinan los movimientos del músculo esquelético. Para ilustrar este modelo se revisa la investigación reciente sobre la “certidumbre”. Aunque ésta es familiar como un sentimiento, la certidumbre es también una magnitud estadística objetiva: una estimación de la probabilidad de que una hipótesis sea correcta. Este enfoque basado en el modelo ayuda a revelar las bases neurales de la certidumbre en las decisiones en mamíferos y construye un enlace al sentimiento subjetivo de certidumbre en humanos. Estos resultados tienen importantes implicancias para la psiquiatría, ya que los trastornos de los cálculos de certidumbre parecen contribuir a numerosas psicopatologías. En un sentido más amplio, este enfoque computacional de las emociones se hace eco de la visión emergente de la nosología psiquiátrica que puede ser mejor parametrizada en términos de trastornos de los cálculos cognitivos subyacentes a las conductas complejas.Les processus émotionnels sont au cœur du comportement, pourtant leur nature subjective profonde s'est révélée être une difficulté pour les études neuroscientifiques comme pour le diagnostic psychiatrique. Nous examinons ici les relations entre les sentiments subjectifs et leurs circuits cérébraux d'un point de vue computationnel. Nous appliquons les connaissances récentes de la neuroscience des systèmes - en considérant le comportement subjectif comme résultat de computations mentales générées dans le cerveau - à l'étude des émotions. Nous développons l'hypothèse que les émotions sont le produit de computations neuronales dont le rôle moteur est de réattribuer les ressources corporelles principalement contrôlées par les muscles lisses. Ce système de contrôle moteur émotionnel est semblable aux calculs plus familiers du contrôle moteur qui coordonnent les mouvements musculaires du squelette. Pour illustrer cette perspective, nous examinons la recherche récente sur la «confiance». Bien que connue comme sentiment, la confiance est aussi une quantité statistique objective: une estimation de la probabilité qu'une hypothèse est exacte. Cette approche fondée sur un modèle permet de révéler les bases neuronales de la confiance décisionnaire chez les mammifères et fournit une passerelle au sentiment subjectif de confiance chez les humains. Les implications de ces résultats en psychiatrie sont importantes, puisque des troubles de computations de confiance semblent contribuer à de nombreuses psychopathologies. Plus largement, cette approche computationnelle des émotions fait émerger la possibilité d'une nosologie psychiatrique pouvant être mieux paramétrée en termes de troubles des computations cognitives sous-tendant des comportements complexes.

Central to the organization of behavior is the ability to predict the values of outcomes to guide choices. The accuracy of such predictions is honed by a teaching signal that indicates how incorrect a prediction was (“reward prediction error,” RPE). In several reinforcement learning contexts, such as Pavlovian conditioning and decisions guided by reward history, this RPE signal is provided by midbrain dopamine neurons. In many situations, however, the stimuli predictive of outcomes are perceptually ambiguous. Perceptual uncertainty is known to influence choices, but it has been unclear whether or how dopamine neurons factor it into their teaching signal. To cope with uncertainty, we extended a reinforcement learning model with a belief state about the perceptually ambiguous stimulus; this model generates an estimate of the probability of choice correctness, termed decision confidence. We show that dopamine responses in monkeys performing a perceptually ambiguous decision task comply with the model’s predictions. Consequently, dopamine responses did not simply reflect a stimulus’ average expected reward value but were predictive of the trial-to-trial fluctuations in perceptual accuracy. These confidence-dependent dopamine responses emerged prior to monkeys’ choice initiation, raising the possibility that dopamine impacts impending decisions, in addition to encoding a post-decision teaching signal. Finally, by manipulating reward size, we found that dopamine neurons reflect both the upcoming reward size and the confidence in achieving it. Together, our results show that dopamine responses convey teaching signals that are also appropriate for perceptual decisions.

Confidence judgments are a central example of metacognition-knowledge about one's own cognitive processes. According to this metacognitive view, confidence reports are generated by a second-order monitoring process based on the quality of internal representations about beliefs. Although neural correlates of decision confidence have been recently identified in humans and other animals, it is not well understood whether there are brain areas specifically important for confidence monitoring. To address this issue, we designed a postdecision temporal wagering task in which rats expressed choice confidence by the amount of time they were willing to wait for reward. We found that orbitofrontal cortex inactivation disrupts waiting-based confidence reports without affecting decision accuracy. Furthermore, we show that a normative model can quantitatively account for waiting times based on the computation of decision confidence. These results establish an anatomical locus for a metacognitive report, confidence judgment, distinct from the processes required for perceptual decisions. info:eu-repo/semantics/publishedVersion

SummaryLittle is known about how the brain learns to anticipate the timing of reward. A new study demonstrates that optogenetic activation of basal forebrain input is sufficient to train reward timing activity in the primary visual cortex.

Neurons in the prefrontal cortex exhibit diverse behavioural correlates, an observation that has been attributed to cell-type diversity. To link identified neuron types with network and behavioural functions, we recorded from the two largest genetically defined inhibitory interneuron classes, the perisomatically targeting parvalbumin (PV) and the dendritically targeting somatostatin (SOM) neurons in anterior cingulate cortex of mice performing a reward foraging task. Here we show that PV and a subtype of SOM neurons form functionally homogeneous populations showing a double dissociation between both their inhibitory effects and behavioural correlates. Out of several events pertaining to behaviour, a subtype of SOM neurons selectively responded at reward approach, whereas PV neurons responded at reward leaving and encoded preceding stay duration. These behavioural correlates of PV and SOM neurons defined a behavioural epoch and a decision variable important for foraging (whether to stay or to leave), a crucial function attributed to the anterior cingulate cortex. Furthermore, PV neurons could fire in millisecond synchrony, exerting fast and powerful inhibition on principal cell firing, whereas the inhibitory effect of SOM neurons on firing output was weak and more variable, consistent with the idea that they respectively control the outputs of, and inputs to, principal neurons. These results suggest a connection between the circuit-level function of different interneuron types in regulating the flow of information and the behavioural functions served by the cortical circuits. Moreover, these observations bolster the hope that functional response diversity during behaviour can in part be explained by cell-type diversity.

Hypothesis/introduction:Renal fibrovascular injury often persists in chronic kidney disease patients treated with renin-angiotensin system blockers. Intriguingly, early outgrowth cell-derived factor infusion also inhibits chronic renal injury. We sought to determine whether early outgrowth cell-derived factor administration provides further renoprotection when added to renin-angiotensin system blockade.Materials and methods:Conditioned medium was generated by incubating rat early outgrowth cells with serum-free endothelial basal medium-2 to collect their secreted factors. Subtotal nephrectomy rats received enalapril 0.5 mg/L in drinking water or placebo, beginning 8 weeks post-surgery. Four weeks later, enalapril-treated rats received intravenous injections of either conditioned medium or control endothelial basal medium-2 for 2 weeks. Glomerular filtration rate, urinary protein excretion and renal structure were assessed 4 weeks later at 16 weeks post-surgery.Results:Enalapril-treated subtotal nephrectomy rats receiving control endothelial basal medium-2 injections experienced only partial renoprotection when compared to vehicle-treated subtotal nephrectomy rats. In contrast, conditioned medium infusion, when administered in addition to enalapril, attenuated the progression of renal dysfunction in subtotal nephrectomy rats, improving glomerular filtration rate and reducing proteinuria without affecting blood pressure.Conclusions:Early outgrowth cell-derived factors exert additive renoprotective effects on top of angiotensin-converting enzyme inhibitor therapy in experimental chronic kidney disease, providing the rationale for clinical trials of early outgrowth cell-based therapies for chronic kidney disease.

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001, eLife digest Many neuroscience experiments today involve using electrodes to record from the brain of an animal, such as a mouse or a monkey, while the animal performs a task. The goal of such experiments is to understand how a particular brain region works. However, modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously. Analysing such large amounts of data then becomes a challenge in itself. This is particularly true for brain regions such as the prefrontal cortex that are involved in the cognitive processes that allow an animal to acquire knowledge. Individual neurons in the prefrontal cortex encode many different types of information relevant to a given task. Imagine, for example, that an animal has to select one of two objects to obtain a reward. The same group of prefrontal cortex neurons will encode the object presented to the animal, the animal’s decision and its confidence in that decision. This simultaneous representation of different elements of a task is called a ‘mixed’ representation, and is difficult to analyse. Kobak, Brendel et al. have now developed a data analysis tool that can ‘demix’ neural activity. The tool breaks down the activity of a population of neurons into its individual components. Each of these relates to only a single aspect of the task and is thus easier to interpret. Information about stimuli, for example, is distinguished from information about the animal’s confidence levels. Kobak, Brendel et al. used the demixing tool to reanalyse existing datasets recorded from several different animals, tasks and brain regions. In each case, the tool provided a complete, concise and transparent summary of the data. The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice. At a technical level, the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future. DOI: http://dx.doi.org/10.7554/eLife.10989.002

The mouse is an important model system for investigating the neural circuits mediating behavior. Because of advances in imaging and optogenetic methods, head-fixed mouse preparations provide an unparalleled opportunity to observe and control neural circuits. To investigate how neural circuits produce behavior, these methods need to be paired with equally well-controlled and monitored behavioral paradigms. Here, we introduce the choice ball, a response device that enables two-alternative forced-choice (2AFC) tasks in head-fixed mice based on the readout of lateral paw movements. We demonstrate the advantages of the choice ball by training mice in the random-click task, a two-choice auditory discrimination behavior. For each trial, mice listened to binaural streams of Poisson-distributed clicks and were required to roll the choice ball laterally toward the side with the greater click rate. In this assay, mice performed hundreds of trials per session with accuracy ranging from 95% for easy stimuli (large interaural click-rate contrast) to near chance level for low-contrast stimuli. We also show, using the record of individual paw strokes, that mice often reverse decisions they have already initiated and that decision reversals correlate with improved performance. The choice ball enables head-fixed 2AFC paradigms, facilitating the circuit-level analysis of sensory processing, decision making, and motor control in mice.

Cell therapy has been extensively investigated in heart disease but less so in the kidney. We considered whether cell therapy also might be useful in diabetic kidney disease. Cognizant of the likely need for autologous cell therapy in humans, we sought to assess the efficacy of donor cells derived from both healthy and diabetic animals. Eight-week-old db/db mice were randomized to receive a single intravenous injection of PBS or 0.5 × 106 early-outgrowth cells (EOCs) from db/m or db/db mice. Effects were assessed 4 weeks after cell infusion. Untreated db/db mice developed mesangial matrix expansion and tubular epithelial cell apoptosis in association with increased reactive oxygen species (ROS) and overexpression of thioredoxin interacting protein (TxnIP). Without affecting blood glucose or blood pressure, EOCs not only attenuated mesangial and peritubular matrix expansion, as well as tubular apoptosis, but also diminished ROS and TxnIP overexpression in the kidney of db/db mice. EOCs derived from both diabetic db/db and nondiabetic db/m mice were equally effective in ameliorating kidney injury and oxidative stress. The similarly beneficial effects of cells from healthy and diabetic donors highlight the potential of autologous cell therapy in the related clinical setting.

Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.

Retrogradely transported neurotropic viruses enable genetic access to neurons based on their long-range projections and have become indispensable tools for linking neural connectivity with function. A major limitation of viral techniques is that they rely on cell-type-specific molecules for uptake and transport. Consequently, viruses fail to infect variable subsets of neurons depending on the complement of surface receptors expressed (viral tropism). We report a receptor complementation strategy to overcome this by potentiating neurons for the infection of the virus of interest-in this case, canine adenovirus type-2 (CAV-2). We designed AAV vectors for expressing the coxsackievirus and adenovirus receptor (CAR) throughout candidate projection neurons. CAR expression greatly increased retrograde-labeling rates, which we demonstrate for several long-range projections, including some resistant to other retrograde-labeling techniques. Our results demonstrate a receptor complementation strategy to abrogate endogenous viral tropism and thereby facilitate efficient retrograde targeting for functional analysis of neural circuits.

Most forms of chronic, progressive kidney disease are characterized by fibrosis whereby the prototypical prosclerotic growth factor, transforming growth factor β (TGF-β), is thought to play a pivotal role. With the recent understanding that TGF-β's canonical signaling pathway may be modified by acetylation as well as phosphorylation, we explored the role of the NAD + -dependent lysine deacetylase, sirtuin 1 (SIRT1) in fibrogenesis in the cell culture, animal model, and human settings. In vitro , the increase in collagen production that results from TGF-β1 stimulation was ameliorated by the allosteric modifier of Sirt1 deacetylase, SRT3025, in association with a reduction in Smad3 reporter activity. In the remnant kidney model (subtotally or 5/6 nephrectomized rats) that develops progressive kidney disease in association with TGF-β overexpression, administration of SRT3025 attenuated glomerular filtration rate decline and proteinuria without affecting blood pressure. Glomerulosclerosis and tubulointerstitial fibrosis were similarly reduced with Sirt1 activation as were cardiac structure and function in this rodent model of primary kidney and secondary cardiac disease. Relating these findings to the human setting, we noted a reduction in SIRT1 mRNA in kidney biopsies obtained from individuals with focal glomerulosclerosis. Together these studies highlight the potential of SIRT1 activation as a therapeutic strategy in progressive, fibrotic kidney disease.

When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons to distinguish between these two concepts. Specifically, we propose that confidence should be defined as the probability that a decision or a proposition, overt or covert, is correct given the evidence, a critical quantity in complex sequential decisions. We suggest that the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables. We also discuss strategies for studying the neural codes for confidence and certainty and argue that clear definitions of neural codes are essential to understanding the relative contributions of various cortical areas to decision making.

Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations.

Persistent neural activity is observed in many systems, and is thought to be a neural substrate for holding memories over time delays of a few seconds. Recent work has addressed two issues. First, how can networks of neurons robustly hold such an active memory? Computer systems obtain significant robustness to noise by approximating analogue quantities with discrete digital representations. In a similar manner, theoretical models of persistent activity in spiking neurons have shown that the most robust and stable way to store the short-term memory of a continuous parameter is to approximate it with a discrete representation. This general idea applies very broadly to mechanisms that range from biochemical networks to single cells and to large circuits of neurons. Second, why is it commonly observed that persistent activity in the cortex can be strongly time-varying? This observation is almost ubiquitous, and therefore must be taken into account in our models and our understanding of how short-term memories are held in the cortex.

Cell-based regenerative therapies offer a new alternative approach to the treatment of chronic disease. Specifically, studies by our laboratory and others have shown that a subpopulation of cells derived from the bone marrow, known as early outgrowth cells (EOCs), are able to attenuate the progression of chronic kidney disease (CKD). In this study we examined the efficacy of a tissue engineering system, in which EOCs were embedded into submillimeter-sized collagen cylinders. These small individual units are referred to as modules and together form a functional microtissue. Due to their resemblance to endothelial cells, late outgrowth cells (LOCs) were used to coat the module surface, hypothesizing that as such they would promote vascularization and enhance engraftment of the encapsulated EOCs. These coated modules were transplanted subcutaneously into the subtotally nephrectomized rat model of CKD. While coated module therapy significantly improved both renal structure and function, noncoated modules with embedded EOCs were unable to reproduce these salutary effects on the kidney. Nevertheless, in both treatments, the embedded EOCs quickly degraded the modular environment and were seen to migrate to the liver, spleen, and bone marrow as early as 6 days after transplantation. With the efflux of EOCs, and unexpectedly no evidence of vascularization, we hypothesized that the LOCs did not enhance EOC engraftment, but rather augmented the renoprotection provided by EOCs by secretion of their own soluble and potent antifibrotic factors. To the best of our knowledge, this is the first study to document an effective subcutaneous approach for renoprotection.

The cellular diversity of interneurons in the neocortex is thought to reflect subtype-specific roles of cortical inhibition. Here we ask whether perturbations to two subtypes—parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneurons—can be compensated for with respect to their contributions to cortical development. We use a genetic cell fate switch to delete both PV+ and SST+ interneurons selectively in cortical layers 2–4 without numerically changing the total interneuron population. This manipulation is compensated for at the level of synaptic currents and receptive fields (RFs) in the somatosensory cortex. By contrast, we identify a deficit in inhibitory synchronization in vitro and a large reduction in cortical gamma oscillations in vivo. This reveals that, while the roles of inhibition in establishing cortical inhibitory/excitatory balance and RFs can be subserved by multiple interneuron subtypes, gamma oscillations depend on cellular properties that cannot be compensated for—likely, the fast signalling properties of PV+ interneurons., The neocortex contains a large diversity of interneurons but the subtype-specific roles of these cells in establishing excitatory and inhibitory circuits are not well understood. Here the authors genetically delete parvalbumin- and somatostatin-positive interneurons during mouse development and study the functional effects in postnatal cortical circuits.

Nutrient overabundance and diminished physical activity underlie the epidemic of obesity and its consequences of insulin resistance and type 2 diabetes. These same phenomena, obesity and insulin resistance, are also observed in mammals as they ready themselves for the nutrient deprivation of winter, yet their plasma glucose does not rise. Given the role of silent information regulator 2 (Sir2) and its mammalian orthologue, Sirt1, in survival and life extension during energy deprivation, we hypothesised that enhancing its activity may reduce the insensible energy loss engendered by hyperglycaemia and glycosuria.At 8 weeks of age, db/db and db/m mice were randomised to receive the SIRT1 activator SRT3025 milled in chow (3.18 g/kg) or regular chow and followed for a further 12 weeks.When compared with vehicle, SIRT1 activation greatly improved glycaemic control, augmented plasma insulin concentrations, increased pancreatic islet beta cell mass and elevated hepatic expression of the beta cell growth factor, betatrophin in db/db mice. Despite the dramatic reduction in hyperglycaemia, db/db mice displayed worsening insulin resistance, diminished physical activity and further weight gain. These findings along with reduced food intake and reduction in body temperature resembled torpor and hibernation. By contrast, SIRT1 activation conferred only minimal changes in non-diabetic db/m mice.While reducing hyperglycaemia and promoting beta cell expansion, enhancing the activity of SIRT1 facilitates a phenotypic change in a db/db mouse model of diabetes to one that more closely resembles the physiological state of torpor or hibernation.

The exquisite architecture of cortex incorporates a myriad of inhibitory interneuron types. Until recently, the dearth of techniques for cell type identification in awake animals has made it difficult to link interneuron activity with circuit function, computation and behavior. This situation has changed dramatically in recent years with the advent of novel tools for targeting genetically distinct interneuron types so their activity can be observed and manipulated. The association of different interneuron subtypes with specific circuit functions, such as gain modulation or disinhibition, is starting to reveal canonical circuit motifs conserved across neocortical regions. Moreover, it appears that some interneuron types are recruited at specific behavioral events and likely control the flow of information among and within brain areas at behavioral time scales. Based on these results we propose that interneuron function goes beyond network coordination and interneurons should be viewed as integral elements of cortical computations serving behavior.

Sense organs are often actively controlled by motor processes and such active sensing profoundly shapes the timing of sensory information flow. The temporal coordination between different active sensing processes is less well understood but is essential for multisensory integration, coordination between brain regions, and energetically optimal sampling strategies. Here we studied the coordination between sniffing and whisking, the motor processes in rodents that control the acquisition of smell and touch information, respectively. Sniffing, high-frequency respiratory bouts, and whisking, rapid back and forth movements of mystacial whiskers, occur in the same theta frequency range (4–12 Hz) leading to a hypothesis that these sensorimotor rhythms are phase locked. To test this, we monitored sniffing using a thermocouple in the nasal cavity and whisking with an electromyogram of the mystacial pad in rats engaged in an open field reward foraging behavior. During bouts of exploration, sniffing and whisking showed strong one-to-one phase locking within the theta frequency range (4–12 Hz). Interestingly, we also observed multimode phase locking with multiple whisks within a sniff cycle or multiple sniffs within a whisk cycle—always at the same preferred phase. This specific phase relationship coupled the acquisition phases of the two sensorimotor rhythms, inhalation and whisker protraction. Our results suggest that sniffing and whisking may be under the control of interdependent rhythm generators that dynamically coordinate active acquisition of olfactory and somatosensory information.

Adult bone marrow-derived cells can improve organ function in chronic disease models, ostensibly by the release of paracrine factors. It has, however, been difficult to reconcile this prevailing paradigm with the lack of cell retention within injured organs and their rapid migration to the reticuloendothelial system. Here, we provide evidence that the salutary antifibrotic effects of bone marrow-derived early outgrowth cells (EOCs) are more consistent with an endocrine mode of action, demonstrating not only the presence of antifibrotic factors in the plasma of EOC-treated rats but also that EOC conditioned medium (EOC-CM) potently attenuates both TGF-β- and angiotensin II-induced fibroblast collagen production in vitro. To examine the therapeutic relevance of these findings in vivo, 5/6 subtotally nephrectomized rats, a model of chronic kidney and heart failure characterized by progressive fibrosis of both organs, were randomized to receive i.v. injections of EOC-CM, unconditioned medium, or 106 EOCs. Rats that received unconditioned medium developed severe kidney injury with cardiac diastolic dysfunction. In comparison, EOC-CM-treated rats demonstrated substantially improved renal and cardiac function and structure, mimicking the changes found in EOC-treated animals. Mass spectrometric analysis of EOC-CM identified proteins that regulate cellular functions implicated in fibrosis. These results indicate that EOCs secrete soluble factor(s) with highly potent antifibrotic activity, that when injected intravenously replicate the salutary effects of the cells themselves. Together, these findings suggest that an endocrine mode of action may underlie the effectiveness of cell therapy in certain settings and portend the possibility for systemic delivery of cell-free therapy.

We have identified two forms of a major histocompatibility complex (MHC) class I molecule, H-2Kb, distinguishable by specific antibodies through a study of a genetically engineered mouse cell line that overexpresses these molecules. One form, a complex associated with beta 2-microglobulin (native, beta 2m+ class I), is detectable by conformation-dependent antibodies. The other form, which remains after preclearing cell lysates of native class I, is only poorly, if at all, associated with beta 2-microglobulin (beta 2m- class I) and is detectable by an antiserum against the cytoplasmic tail region of H-2K molecules. Both forms are also present in normal cell lines. The affinity-purified native class I molecules bind short peptides (8 or 9 residues) and assemble tightly with beta 2-microglobulin. In striking contrast, the beta 2m- class I molecules bind peptides that are longer (> 15 residues) than those bound to native class I molecules. This finding is consistent with the recent evidence that peptides longer than 8-10 amino acid residues are transported into the endoplasmic reticulum and suggests the possibility of a control step for peptide presentation by MHC in which the incompletely processed peptides bind to the heavy chain and a selected fraction undergoes final processing and presentation on the cell surface.

The orbitofrontal cortex (OFC) is important in processing rewards and other behavioral outcomes. Here, we review from a computational perspective recent progress in understanding this complex function. OFC neurons appear to represent abstract outcome values, which may facilitate the comparison of options, as well as concrete outcome attributes, such as flavor or location, which may enable predictive cues to access current outcome values in the face of dynamic modulation by internal state, context and learning. OFC can use reinforcement learning to generate outcome predictions; it can also generate outcome predictions using other mechanisms, including the evaluation of decision confidence or uncertainty. OFC neurons encode not only the mean expected outcome but also the variance, consistent with the idea that OFC uses a probabilistic population code to represent outcomes. We suggest that further attention to the nature of its representations and algorithms will be critical to further elucidating OFC function.

Humans and other animals must often make decisions on the basis of imperfect evidence. Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus-outcome associations. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition' and conscious awareness are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.

Olfactory perception relies on an active sampling process, sniffing, to rapidly deliver odorants from the environment to the olfactory receptors. The respiration cycle strongly patterns the flow of information into the olfactory systems, but the behavioral significance of particular sniffing patterns is not well understood. Here, we monitored the frequency and timing of nasal respiration in rats performing an odor-mixture–discrimination task that allowed us to test subjects near psychophysical limits and to quantify the precise timing of their behavior. We found that respiration frequencies varied widely from 2 to 12 Hz, but odor discrimination was dependent on 6- to 9-Hz sniffing: rats almost always entered and maintained this frequency band during odor sampling and their accuracy on difficult discrimination dropped when they did not. Moreover, the switch from baseline respiration to sniffing occurred not in response to odor delivery but in anticipation of odor sampling and was executed rapidly, almost always within a single cycle. Interestingly, rats also switched from respiration to rapid sniffing in anticipation of reward delivery, but in a distinct frequency band, 9–12 Hz. These results demonstrate the speed and precision of control over respiration and its significance for olfactory behavioral performance.

Intuitively, decisions should always improve with more time for the accumulation of evidence, yet psychophysical data show a limit of 200-300 ms for many perceptual tasks. Here, we consider mechanisms that favour such rapid information processing in vision and olfaction. We suggest that the brain limits some types of perceptual processing to short, discrete chunks (for example, eye fixations and sniffs) in order to facilitate the construction of global sensory images.

Sniffing is a rhythmic motor process essential for the acquisition of olfactory information. Recent behavioral experiments show that using a single sniff rats can accurately discriminate between very similar odors and fail to improve their accuracy by taking multiple sniffs. This implies that each sniff has the potential to provide a complete snapshot of the local olfactory environment. The discrete and intermittent nature of sniffing has implications beyond the physical process of odor capture as it strongly shapes the flow of information into the olfactory system. We review electrophysiological studies-primarily from anesthetized rodents-demonstrating that olfactory neural responses are coupled to respiration. Hence, the "sniff cycle" might play a role in odor coding, by allowing the timing of spikes with respect to the phase of the respiration cycle to encode information about odor identity or concentration. We also discuss behavioral and physiological results indicating that sniffing can be dynamically coordinated with other rhythmic behaviors, such as whisking, as well as with rhythmic neural activity, such as hippocampal theta oscillations. Thus, the sniff cycle might also facilitate the coordination of the olfactory system with other brain areas. These converging lines of empirical data support the notion that each sniff is a unit of olfactory processing relevant for both neural coding and inter-areal coordination. Further electrophysiological recordings in behaving animals will be necessary to assess these proposals.

Brief bursts of high-frequency action potentials represent a common firing mode of pyramidal neurons, and there are indications that they represent a special neural code. It is therefore of interest to determine whether there are particular spatial and temporal features of neuronal inputs that trigger bursts. Recent work on pyramidal cells indicates that bursts can be initiated by a specific spatial arrangement of inputs in which there is coincident proximal and distal dendritic excitation (Larkum et al., 1999). Here we have used a computational model of an important class of bursting neurons to investigate whether there are special temporal features of inputs that trigger bursts. We find that when a model pyramidal neuron receives sinusoidally or randomly varying inputs, bursts occur preferentially on the positive slope of the input signal. We further find that the number of spikes per burst can signal the magnitude of the slope in a graded manner. We show how these computations can be understood in terms of the biophysical mechanism of burst generation. There are several examples in the literature suggesting that bursts indeed occur preferentially on positive slopes (Guido et al., 1992; Gabbiani et al., 1996). Our results suggest that this selectivity could be a simple consequence of the biophysics of burst generation. Our observations also raise the possibility that neurons use a burst duration code useful for rapid information transmission. This possibility could be further examined experimentally by looking for correlations between burst duration and stimulus variables.

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

Dynamics of single cells and large cell populations are the subject of investigation by using differently detailed models. Multicompartmental modeling techniques are used to systematically investigate the location-dependent effects of GABA-ergic inhibition on the firing patterns of hippocampal pyramidal cells. Appearance of stochastic resonance in a model of mitral and granule cells of the olfactory bulb is demonstrated by using a single-compartmental model approach. Spatial propagation of synchronized activities in hippocampal slices are studied by a model of large neural populations.

In this article we model hippocampal pyramidal cells and interneurons with GENESIS.

SummaryBasal forebrain cholinergic neurons constitute a major neuromodulatory system implicated in normal cognition and neurodegenerative dementias. Cholinergic projections densely innervate neocortex, releasing acetylcholine to regulate arousal, attention, and learning. However, their precise behavioral function is poorly understood because identified cholinergic neurons have never been recorded during behavior. To determine which aspects of cognition their activity might support, we recorded cholinergic neurons using optogenetic identification in mice performing an auditory detection task requiring sustained attention. We found that a non-cholinergic basal forebrain population—but not cholinergic neurons—were correlated with trial-to-trial measures of attention. Surprisingly, cholinergic neurons responded to reward and punishment with unusual speed and precision (18 ± 3 ms). Cholinergic responses were scaled by the unexpectedness of reinforcement and were highly similar across neurons and two nuclei innervating distinct cortical areas. These results reveal that the cholinergic system broadcasts a rapid and precisely timed reinforcement signal, supporting fast cortical activation and plasticity.

Basal forebrain cholinergic neurons (BFCN) densely innervate the forebrain and modulate synaptic plasticity, cortical processing, brain states and oscillations. However, little is known about the functional diversity of cholinergic neurons and whether distinct types support different functions. To examine this question we recorded BFCN in vivo, to examine their behavioral functions, and in vitro, to study their intrinsic properties. We identified two distinct types of BFCN that markedly differ in their firing modes, synchronization properties and behavioral correlates. Bursting cholinergic neurons (BFCNBURST) fired in zero-lag synchrony with each other, phase-locked to cortical theta activity and fired precisely timed bursts of action potentials after reward and punishment. Regular firing cholinergic neurons (BFCNREG) were found predominantly in the posterior basal forebrain, displayed strong theta rhythmicity (5-10 Hz), fired asynchronously with each other and responded with precise single spikes after behavioral outcomes. In an auditory detection task, synchronization of BFCNBURST neurons to auditory cortex predicted the timing of mouse responses, whereas tone-evoked cortical coupling of BFCNREG predicted correct detections. We propose that cortical activation relevant for behavior is controlled by the balance of two cholinergic cell types, where the precise proportion of the strongly activating BFCNBURST follows an anatomical gradient along the antero-posterior axis of the basal forebrain.

SummaryWhile it is commonly assumed that decisions taken slowly result in superior outcomes, is it possible that optimal decision making does not always require sacrificing speed? For odor categorization decisions, it was previously shown that rats use

SummarySerotonin dysfunction is implicated in many neuropsychiatric disorders yet the precise behavioral functions of this neuromodulator are not well understood. A new study employs optogenetic methods to activate serotonin neurons during an effort-demanding waiting behavior and demonstrates that serotonin release increases patience, the capacity for self-control.

The basal forebrain (BF) houses major ascending projections to the entire neocortex that have long been implicated in arousal, learning, and attention. The disruption of the BF has been linked with major neurological disorders, such as coma and Alzheimer's disease, as well as in normal cognitive aging. Although it is best known for its cholinergic neurons, the BF is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific BF cell types have led to a renaissance in the study of the BF and are beginning to yield new insights about cell-type-specific circuit mechanisms during behavior. These approaches enable us to determine the behavioral conditions under which cholinergic and noncholinergic BF neurons are activated and how they control cortical processing to influence behavior. Here we discuss recent advances that have expanded our knowledge about this poorly understood brain region and laid the foundation for future cell-type-specific manipulations to modulate arousal, attention, and cortical plasticity in neurological disorders.SIGNIFICANCE STATEMENTAlthough the basal forebrain is best known for, and often equated with, acetylcholine-containing neurons that provide most of the cholinergic innervation of the neocortex, it is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific cell types in the basal forebrain have led to a renaissance in this field and are beginning to dissect circuit mechanisms in the basal forebrain during behavior. This review discusses recent advances in the roles of basal forebrain cholinergic and noncholinergic neurons in cognition via their dynamic modulation of cortical activity.