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.

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.

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.

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.

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'.

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.

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.

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.

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.

The pheromone darcin drives a circuit for innate and reinforced behaviours.

Organisms have evolved diverse behavioural strategies that enhance the likelihood of encountering and assessing mates1. Many species use pheromones to communicate information about the location, sexual and social status of potential partners2. In mice, the major urinary protein darcin-which is present in the urine of males-provides a component of a scent mark that elicits approach by females and drives learning3,4. Here we show that darcin elicits a complex and variable behavioural repertoire that consists of attraction, ultrasonic vocalization and urinary scent marking, and also serves as a reinforcer in learning paradigms. We identify a genetically determined circuit-extending from the accessory olfactory bulb to the posterior medial amygdala-that is necessary for all behavioural responses to darcin. Moreover, optical activation of darcin-responsive neurons in the medial amygdala induces both the innate and the conditioned behaviours elicited by the pheromone. These neurons define a topographically segregated population that expresses neuronal nitric oxide synthase. We suggest that this darcin-activated neural circuit integrates pheromonal information with internal state to elicit both variable innate behaviours and reinforced behaviours that may promote mate encounters and mate selection.

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.

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.

Frontal cortex neuron types categorically encode single decision variables.

Individual neurons in many cortical regions have been found to encode specific, identifiable features of the environment or body that pertain to the function of the region1-3. However, in frontal cortex, which is involved in cognition, neural responses display baffling complexity, carrying seemingly disordered mixtures of sensory, motor and other task-related variables4-13. This complexity has led to the suggestion that representations in individual frontal neurons are randomly mixed and can only be understood at the neural population level14,15. Here we show that neural activity in rat orbitofrontal cortex (OFC) is instead highly structured: single neuron activity co-varies with individual variables in computational models that explain choice behaviour. To characterize neural responses across a large behavioural space, we trained rats on a behavioural task that combines perceptual and value-guided decisions. An unbiased, model-free clustering analysis identified distinct groups of OFC neurons, each with a particular response profile in task-variable space. Applying a simple model of choice behaviour to these categorical response profiles revealed that each profile quantitatively corresponds to a specific decision variable, such as decision confidence. Additionally, we demonstrate that a connectivity-defined cell type, orbitofrontal neurons projecting to the striatum, carries a selective and temporally sustained representation of a single decision variable: integrated value. We propose that neurons in frontal cortex, as in other cortical regions, form a sparse and overcomplete representation of features relevant to the region's function, and that they distribute this information selectively to downstream regions to support 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.