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Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that full automation without human intervention allowed rapid, cost-efficient training, and decreased stress as measured by corticosterone levels. Training breaks introduced only a transient drop in performance, and mice readily generalized across training systems when transferred from automated to manual protocols. We further validated our automated training system with wireless optogenetics and pharmacology experiments, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.

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Foraging animals have to evaluate, compare and select food patches in order to increase their fitness. Understanding what drives foraging decisions requires careful manipulation of the value of alternative options while monitoring animals choices. Value-based decision-making tasks in combination with formal learning models have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is despite the fact that fruit flies have served as model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we also show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of reward foraging decisions.

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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.

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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.

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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.

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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.

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In the mammalian cerebral cortex the diversity of interneuronal subtypes underlies a division of labour subserving distinct modes of inhibitory control. A unique mode of inhibitory control may be provided by inhibitory neurons that specifically suppress the firing of other inhibitory neurons. Such disinhibition could lead to the selective amplification of local processing and serve the important computational functions of gating and gain modulation. Although several interneuron populations are known to target other interneurons to varying degrees, little is known about interneurons specializing in disinhibition and their in vivo function. Here we show that a class of interneurons that express vasoactive intestinal polypeptide (VIP) mediates disinhibitory control in multiple areas of neocortex and is recruited by reinforcement signals. By combining optogenetic activation with single-cell recordings, we examined the functional role of VIP interneurons in awake mice, and investigated the underlying circuit mechanisms in vitro in auditory and medial prefrontal cortices. We identified a basic disinhibitory circuit module in which activation of VIP interneurons transiently suppresses primarily somatostatin- and a fraction of parvalbumin-expressing inhibitory interneurons that specialize in the control of the input and output of principal cells, respectively. During the performance of an auditory discrimination task, reinforcement signals (reward and punishment) strongly and uniformly activated VIP neurons in auditory cortex, and in turn VIP recruitment increased the gain of a functional subpopulation of principal neurons. These results reveal a specific cell type and microcircuit underlying disinhibitory control in cortex and demonstrate that it is activated under specific behavioural conditions.

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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.

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