Structured behavioral data format: An NWB extension standard for task-based behavioral neuroscience experiments

Understanding brain function necessitates linking neural activity with corresponding behavior. Structured behavioral experiments are crucial for probing the neural computations and dynamics underlying behavior; however, adequately representing their complex data is a significant challenge. Currently, a comprehensive data standard that fully encapsulates task-based experiments, integrating neural activity with the richness of behavioral context, is lacking. We designed a data model, as an extension to the NWB neurophysiology data standard, to represent structured behavioral neuroscience experiments, spanning stimulus delivery, timestamped events and responses, and simultaneous neural recordings. This data format is validated through its application to a variety of experimental designs, showcasing its potential to advance integrative analyses of neural circuits and complex behaviors. This work introduces a comprehensive data standard designed to capture and store a spectrum of behavioral data, encapsulating the multifaceted nature of modern neuroscience experiments.

Monosynaptic restriction of the anterograde herpes simplex virus strain H129 for neural circuit tracing

Identification of synaptic partners is a fundamental task for systems neuroscience. To date, few reliable techniques exist for whole brain labeling of downstream synaptic partners in a cell-type-dependent and monosynaptic manner. Herein, we describe a novel monosynaptic anterograde tracing system based on the deletion of the gene UL6 from the genome of a cre-dependent version of the anterograde Herpes Simplex Virus 1 strain H129. Given that this knockout blocks viral genome packaging and thus viral spread, we reasoned that co-infection of a HSV H129 ΔUL6 virus with a recombinant adeno-associated virus expressing UL6 in a cre-dependent manner would result in monosynaptic spread from target cre-expressing neuronal populations. Application of this system to five nonreciprocal neural circuits resulted in labeling of neurons in expected projection areas. While some caveats may preclude certain applications, this system provides a reliable method to label postsynaptic partners in a brain-wide fashion.

Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortex

Frontal cortex is thought to underlie many advanced cognitive capacities, from self-control to long term planning. Reflecting these diverse demands, frontal neural activity is notoriously idiosyncratic, with tuning properties that are correlated with endless numbers of behavioral and task features. This menagerie of tuning has made it difficult to extract organizing principles that govern frontal neural activity. Here, we contrast two successful yet seemingly incompatible approaches that have begun to address this challenge. Inspired by the indecipherability of single-neuron tuning, the first approach casts frontal computations as dynamical trajectories traversed by arbitrary mixtures of neurons. The second approach, by contrast, attempts to explain the functional diversity of frontal activity with the biological diversity of cortical cell-types. Motivated by the recent discovery of functional clusters in frontal neurons, we propose a consilience between these population and cell-type-specific approaches to neural computations, advancing the conjecture that evolutionarily inherited cell-type constraints create the scaffold within which frontal population dynamics must operate.

Cortex-wide response mode of VIP-expressing inhibitory neurons by reward and punishment

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.

Computational validity: using computation to translate behaviours across species

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Apparent sunk cost effect in rational agents

Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

Rats use memory confidence to guide decisions

Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.

Striatal dopamine mediates hallucination-like perception in mice

Hallucinations, a central symptom of psychotic disorders, are attributed to excessive dopamine in the brain. However, the neural circuit mechanisms by which dopamine produces hallucinations remain elusive, largely because hallucinations have been challenging to study in model organisms. We developed a task to quantify hallucination-like perception in mice. Hallucination-like percepts, defined as high-confidence false detections, increased after hallucination-related manipulations in mice and correlated with self-reported hallucinations in humans. Hallucination-like percepts were preceded by elevated striatal dopamine levels, could be induced by optogenetic stimulation of mesostriatal dopamine neurons, and could be reversed by the antipsychotic drug haloperidol. These findings reveal a causal role for dopamine-dependent striatal circuits in hallucination-like perception and open new avenues to develop circuit-based treatments for psychotic disorders.

Publisher Correction: Distinct synchronization, cortical coupling and behavioral function of two basal forebrain cholinergic neuron types

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Distinct synchronization, cortical coupling and behavioral function of two basal forebrain cholinergic neuron types

Basal forebrain cholinergic neurons (BFCNs) modulate synaptic plasticity, cortical processing, brain states and oscillations. However, whether distinct types of BFCNs support different functions remains unclear. Therefore, we recorded BFCNs in vivo, to examine their behavioral functions, and in vitro, to study their intrinsic properties. We identified two distinct types of BFCNs that differ in their firing modes, synchronization properties and behavioral correlates. Bursting cholinergic neurons (Burst-BFCNs) fired synchronously, phase-locked to cortical theta activity and fired precisely timed bursts after reward and punishment. Regular-firing cholinergic neurons (Reg-BFCNs) were found predominantly in the posterior basal forebrain, displayed strong theta rhythmicity and responded with precise single spikes after behavioral outcomes. In an auditory detection task, synchronization of Burst-BFCNs to the auditory cortex predicted the timing of behavioral responses, whereas tone-evoked cortical coupling of Reg-BFCNs predicted correct detections. We propose that differential recruitment of two basal forebrain cholinergic neuron types generates behavior-specific cortical activation.

Interneuron Types as Attractors and Controllers

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.

Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex

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.

Roadmap for the Emerging Field of Cancer Neuroscience

Mounting evidence indicates that the nervous system plays a central role in cancer pathogenesis. In turn, cancers and cancer therapies can alter nervous system form and function. This Commentary seeks to describe the burgeoning field of "cancer neuroscience" and encourage multidisciplinary collaboration for the study of cancer-nervous system interactions.

Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon

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.

Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value

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.

Reconfigurable nanophotonic silicon probes for sub-millisecond deep-brain optical stimulation

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 (<20 μs) reconfigured by electrically tuning the phase of light. By using an eight-beam probe, we show in anaesthetized mice that small groups of single neurons can be independently stimulated to produce multineuron spike patterns at sub-millisecond precision. We also show that a probe integrating co-fabricated electrical recording sites can simultaneously optically stimulate and electrically measure deep-brain neural activity. The technology is scalable, and it allows for beam focusing and steering and for structured illumination via beam shaping. The high-bandwidth optical-stimulation capacity of the device might facilitate the probing of the spatiotemporal neural codes underlying behaviour.

A deep learning framework for neuroscience

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 artificial neural networks, the three components specified by design are the objective functions, the learning rules and the 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.

A Viral Receptor Complementation Strategy to Overcome CAV-2 Tropism for Efficient Retrograde Targeting of Neurons

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.

Summary: Order and Disorder in Brains and Behavior

The 83rd Cold Spring Harbor Symposium on Quantitative Biology on Brains and Behavior: Order and Disorder in the Nervous System explored the tremendous recent progress in neuroscience and how these advances may be used to improve brain health and address psychiatric and neurological disorders. The Symposium explored a vast array of topics from cell types to cognition. My summary focuses on a few emerging themes. Innovative techniques were ever-present, opening up new experimental possibilities. The commoditization of many state-of-the-art technologies is pushing neuroscience beyond its artisanal ways. Another important theme was "circuits in the middle": Numerous presentations dissected cell type-specific circuits that connect different levels of analysis from molecules to behavior. These new technologies have enabled curiosity-driven investigations in animals to connect more directly with preclinical and clinical studies of human brain disorders. Numerous emerging approaches were presented in human neuroscience, bolstering the hope that circuit-specific manipulations will soon provide improved treatments for brain disorders.

The Neurobiology of Confidence: From Beliefs to Neurons

How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior.

When Acetylcholine Unlocks Feedback Inhibition in Cortex

Acetylcholine promotes cognitive function through the regulation of cortical circuits. In this issue of Neuron, Urban-Ciecko et al. (2018) demonstrate how this neuromodulator can rapidly boost synapses from pyramidal to somatostatin neurons, unlocking a new computational ability in the cortical microcircuitry.

Signatures of a Statistical Computation in the Human Sense of Confidence

Human confidence judgments are thought to originate from metacognitive processes that provide a subjective assessment about one's beliefs. Alternatively, confidence is framed in mathematics as an objective statistical quantity: the probability that a chosen hypothesis is correct. Despite similar terminology, it remains unclear whether the subjective feeling of confidence is related to the objective, statistical computation of confidence. To address this, we collected confidence reports from humans performing perceptual and knowledge-based psychometric decision tasks. We observed two counterintuitive patterns relating confidence to choice and evidence: apparent overconfidence in choices based on uninformative evidence, and decreasing confidence with increasing evidence strength for erroneous choices. We show that these patterns lawfully arise from statistical confidence, and therefore occur even for perfectly calibrated confidence measures. Furthermore, statistical confidence quantitatively accounted for human confidence in our tasks without necessitating heuristic operations. Accordingly, we suggest that the human feeling of confidence originates from a mental computation of statistical confidence.

Midbrain Dopamine Neurons Signal Belief in Choice Accuracy during a Perceptual Decision

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.

Vision: how to train visual cortex to predict reward time

Little 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.

A Mathematical Framework for Statistical Decision Confidence

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.

Demixed principal component analysis of neural population data

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

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

Confidence and certainty: distinct probabilistic quantities for different goals

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.

Emotor control: computations underlying bodily resource allocation, emotions, and confidence

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.

Emotor control: computations underlying bodily resource allocation, emotions, and confidence

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.

Central Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback

Basal 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.

A low-cost programmable pulse generator for physiology and behavior

Precisely timed experimental manipulations of the brain and its sensory environment are often employed to reveal principles of brain function. While complex and reliable pulse trains for temporal stimulus control can be generated with commercial instruments, contemporary options remain expensive and proprietary. We have developed Pulse Pal, an open source device that allows users to create and trigger software-defined trains of voltage pulses with high temporal precision. Here we describe Pulse Pal’s circuitry and firmware, and characterize its precision and reliability. In addition, we supply online documentation with instructions for assembling, testing and installing Pulse Pal. While the device can be operated as a stand-alone instrument, we also provide application programming interfaces in several programming languages. As an inexpensive, flexible and open solution for temporal control, we anticipate that Pulse Pal will be used to address a wide range of instrumentation timing challenges in neuroscience research.

Orbitofrontal cortex is required for optimal waiting based on decision confidence

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.

Interneuron cell types are fit to function

Understanding brain circuits begins with an appreciation of their component parts - the cells. Although GABAergic interneurons are a minority population within the brain, they are crucial for the control of inhibition. Determining the diversity of these interneurons has been a central goal of neurobiologists, but this amazing cell type has so far defied a generalized classification system. Interneuron complexity within the telencephalon could be simplified by viewing them as elaborations of a much more finite group of developmentally specified cardinal classes that become further specialized as they mature. Our perspective emphasizes that the ultimate goal is to dispense with classification criteria and directly define interneuron types by function.

From circuit motifs to computations: mapping the behavioral repertoire of cortical interneurons

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.

Distinct behavioural and network correlates of two interneuron types in prefrontal cortex

Neurons in prefrontal cortex exhibit diverse behavioural correlates^1–4, 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^5–8 in anterior cingulate cortex (ACC) 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 impact and behavioural correlates. Out of a number of events pertaining to behaviour, a subtype of Som neurons selectively responded at reward approach, while Pv neurons responded at reward leaving encoding 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 ACC^9–11. Furthermore, Pv neurons could fire in millisecond synchrony exerting fast and powerful inhibition on principal cell firing, while the inhibitory impact 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^12–16. 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.

The limits of deliberation in a perceptual decision task

While 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 <300 ms regardless of difficulty, but these findings could be interpreted as a tradeoff of accuracy for speed. Here, by systematically manipulating the task contingencies, we demonstrate that this is the maximum time over which sampling time can improve accuracy. Furthermore, we show that decision accuracy increases at no temporal cost when rats can better anticipate either the identity of stimuli or the required timing of responses. These experiments suggest that uncertainty in odor category decisions arises from noise sources that fluctuate slowly from trial-to-trial rather than rapidly within trials and that category decisions in other species and modalities might likewise be optimally served by rapid choices.

Choice ball: a response interface for two-choice psychometric discrimination in head-fixed mice

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.

A computational framework for the study of confidence in humans and animals

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.

Rapid and precise control of sniffing during olfactory discrimination in rats

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.

Gating information by two-state membrane potential fluctuations

Two-state voltage fluctuations between a hyperpolarized down-state and a depolarized up-state have been observed experimentally in a wide variety of neurons across brain regions. Using a biophysical model, we show that synaptic input by NMDA receptors can cause such membrane potential fluctuations. In this model, when a neuron is driven by two input pathways with different AMPA/NMDA receptor content, the NMDA-rich input causes up-state transitions, whereas the AMPA-rich input generates spikes only in the up-state. Therefore the NMDA-rich pathway can gate input from an AMPA pathway in an all-or-none fashion by switching between different membrane potential states. Furthermore, once in the up-state, the NMDA-rich pathway multiplicatively increases the gain of a neuron responding to AMPA-rich input. This proposed mechanism for two-state fluctuations directly suggests specific computations, such as gating and gain modulation based on the distinct receptor composition of different neuronal pathways. The dynamic gating of input by up- and down-states may be an elementary operation for the selective routing of signals in neural circuits, which may explain the ubiquity of two-state fluctuations across brain regions.

Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making

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.

The sniff as a unit of olfactory processing

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.

Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors, and dynamic 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.

Information encoding and computation with spikes and bursts

Neurons compute and communicate by transforming synaptic input patterns into output spike trains. The nature of this transformation depends crucially on the properties of voltage-gated conductances in neuronal membranes. These intrinsic membrane conductances can enable neurons to generate different spike patterns including brief, high-frequency bursts that are commonly observed in a variety of brain regions. Here we examine how the membrane conductances that generate bursts affect neural computation and encoding. We simulated a bursting neuron model driven by random current input signal and superposed noise. We consider two issues: the timing reliability of different spike patterns and the computation performed by the neuron. Statistical analysis of the simulated spike trains shows that the timing of bursts is much more precise than the timing of single spikes. Furthermore, the number of spikes per burst is highly robust to noise. Next we considered the computation performed by the neuron: how different features of the input current are mapped into specific output spike patterns. Dimensional reduction and statistical classification techniques were used to determine the stimulus features triggering different firing patterns. Our main result is that spikes, and bursts of different durations, code for different stimulus features, which can be quantified without a priori assumptions about those features. These findings lead us to propose that the biophysical mechanisms of spike generation enables individual neurons to encode different stimulus features into distinct spike patterns.

Spike-timing-dependent plasticity: common themes and divergent vistas

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.