Tactile processing in mouse cortex depends on action context.

The brain receives constant tactile input, but only a subset guides ongoing behavior. Actions associated with tactile stimuli thus endow them with behavioral relevance. It remains unclear how the relevance of tactile stimuli affects processing in the somatosensory (S1) cortex. We developed a cross-modal selection task in which head-fixed mice switched between responding to tactile stimuli in the presence of visual distractors or to visual stimuli in the presence of tactile distractors using licking movements to the left or right side in different blocks of trials. S1 spiking encoded tactile stimuli, licking actions, and direction of licking in response to tactile but not visual stimuli. Bidirectional optogenetic manipulations showed that sensory-motor activity in S1 guided behavior when touch but not vision was relevant. Our results show that S1 activity and its impact on behavior depend on the actions associated with a tactile stimulus.

Simultaneous recordings of pupil size variation and locus coeruleus activity in mice.

An extensive literature describes how pupil size reflects neuromodulatory activity, including the noradrenergic system. Here, we present a protocol for the simultaneous recording of optogenetically identified locus coeruleus (LC) units and pupil diameter in mice under different conditions. We describe steps for building an optrode, performing surgery to implant the optrode and headpost, searching for opto-tagged LC units, and performing dual LC-pupil recording. We then detail procedures for data processing and analysis. For complete details on the use and execution of this protocol, please refer to Megemont et al.1.

A clock-dependent brake for rhythmic arousal in the dorsomedial hypothalamus.

Circadian clocks generate rhythms of arousal, but the underlying molecular and cellular mechanisms remain unclear. In Drosophila, the clock output molecule WIDE AWAKE (WAKE) labels rhythmic neural networks and cyclically regulates sleep and arousal. Here, we show, in a male mouse model, that mWAKE/ANKFN1 labels a subpopulation of dorsomedial hypothalamus (DMH) neurons involved in rhythmic arousal and acts in the DMH to reduce arousal at night. In vivo Ca2+ imaging reveals elevated DMHmWAKE activity during wakefulness and rapid eye movement (REM) sleep, while patch-clamp recordings show that DMHmWAKE neurons fire more frequently at night. Chemogenetic manipulations demonstrate that DMHmWAKE neurons are necessary and sufficient for arousal. Single-cell profiling coupled with optogenetic activation experiments suggest that GABAergic DMHmWAKE neurons promote arousal. Surprisingly, our data suggest that mWAKE acts as a clock-dependent brake on arousal during the night, when mice are normally active. mWAKE levels peak at night under clock control, and loss of mWAKE leads to hyperarousal and greater DMHmWAKE neuronal excitability specifically at night. These results suggest that the clock does not solely promote arousal during an animal's active period, but instead uses opposing processes to produce appropriate levels of arousal in a time-dependent manner.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

Norepinephrine potentiates and serotonin depresses visual cortical responses by transforming eligibility traces.

Reinforcement allows organisms to learn which stimuli predict subsequent biological relevance. Hebbian mechanisms of synaptic plasticity are insufficient to account for reinforced learning because neuromodulators signaling biological relevance are delayed with respect to the neural activity associated with the stimulus. A theoretical solution is the concept of eligibility traces (eTraces), silent synaptic processes elicited by activity which upon arrival of a neuromodulator are converted into a lasting change in synaptic strength. Previously we demonstrated in visual cortical slices the Hebbian induction of eTraces and their conversion into LTP and LTD by the retroactive action of norepinephrine and serotonin Here we show in vivo in mouse V1 that the induction of eTraces and their conversion to LTP/D by norepinephrine and serotonin respectively potentiates and depresses visual responses. We also show that the integrity of this process is crucial for ocular dominance plasticity, a canonical model of experience-dependent plasticity.

Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making.

Nervous systems evolved to effectively navigate the dynamics of the environment to achieve their goals. One framework used to study this fundamental problem arose in the study of learning and decision-making. In this framework, the demands of effective behavior require slow dynamics-on the scale of seconds to minutes-of networks of neurons. Here, we review the phenomena and mechanisms involved. Using vignettes from a few species and areas of the nervous system, we view neuromodulators as key substrates for temporal scaling of neuronal dynamics.

Reinforcement learning modeling reveals a reward-history-dependent strategy underlying reversal learning in squirrel monkeys.

Insight into psychiatric disease and development of therapeutics relies on behavioral tasks that study similar cognitive constructs in multiple species. The reversal learning task is one popular paradigm that probes flexible behavior, aberrations of which are thought to be important in a number of disease states. Despite widespread use, there is a need for a high-throughput primate model that can bridge the genetic, anatomic, and behavioral gap between rodents and humans. Here, we trained squirrel monkeys, a promising preclinical model, on an image-guided deterministic reversal learning task. We found that squirrel monkeys exhibited two key hallmarks of behavior found in other species: integration of reward history over many trials and a side-specific bias. We adapted a reinforcement learning model and demonstrated that it could simulate squirrel monkey-like behavior, capture training-related trajectories, and provide insight into the strategies animals employed. These results validate squirrel monkeys as a model in which to study behavioral flexibility. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Serotonin neurons modulate learning rate through uncertainty.

Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change are difficult in environments with noisy relationships between actions and outcomes. Under these conditions, theories propose that uncertainty can be used to modulate learning rates ("meta-learning"). We show that mice behaving in a dynamic foraging task exhibit choice behavior that varied as a function of two forms of uncertainty estimated from a meta-learning model. The activity of dorsal raphe serotonin neurons tracked both types of uncertainty in the foraging task as well as in a dynamic Pavlovian task. Reversible inhibition of serotonin neurons in the foraging task reproduced changes in learning predicted by a simulated lesion of meta-learning in the model. We thus provide a quantitative link between serotonin neuron activity, learning, and decision making.

Aversive stimuli bias corticothalamic responses to motivationally significant cues.

Making predictions about future rewards or punishments is fundamental to adaptive behavior. These processes are influenced by prior experience. For example, prior exposure to aversive stimuli or stressors changes behavioral responses to negative- and positive-value predictive cues. Here, we demonstrate a role for medial prefrontal cortex (mPFC) neurons projecting to the paraventricular nucleus of the thalamus (PVT; mPFC→PVT) in this process. We found that a history of aversive stimuli negatively biased behavioral responses to motivationally relevant cues in mice and that this negative bias was associated with hyperactivity in mPFC→PVT neurons during exposure to those cues. Furthermore, artificially mimicking this hyperactive response with selective optogenetic excitation of the same pathway recapitulated the negative behavioral bias induced by aversive stimuli, whereas optogenetic inactivation of mPFC→PVT neurons prevented the development of the negative bias. Together, our results highlight how information flow within the mPFC→PVT circuit is critical for making predictions about motivationally-relevant outcomes as a function of prior experience.

Entropy-based metrics for predicting choice behavior based on local response to reward.

For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.

Subthreshold basis for reward-predictive persistent activity in mouse prefrontal cortex.

Nervous systems maintain information internally using persistent activity changes. The mechanisms by which this activity arises are incompletely understood. We study prefrontal cortex (PFC) in mice performing behaviors in which stimuli predicted rewards at different delays with different probabilities. We measure membrane potential (Vm) from pyramidal neurons across layers. Reward-predictive persistent firing increases arise due to sustained increases in mean and variance of Vm and are terminated by reward or via centrally generated mechanisms based on reward expectation. Other neurons show persistent decreases in firing rates, maintained by persistent hyperpolarization that is robust to intracellular perturbation. Persistent activity is layer (L)- and cell-type-specific. Neurons with persistent depolarization are primarily located in upper L5, whereas those with persistent hyperpolarization are mostly found in lower L5. L2/3 neurons do not show persistent activity. Thus, reward-predictive persistent activity in PFC is spatially organized and conveys information about internal state via synaptic mechanisms.

Spinal Cord Drift Following Laminoplasty Versus Laminectomy and Fusion for Cervical Spondylotic Myelopathy.

BACKGROUND: Cervical laminoplasty and laminectomy and fusion (LF) are posterior-based surgical techniques for the surgical treatment of cervical spondylotic myelopathy (CSM). Interestingly, the comparative amount of spinal cord drift obtained from these procedures has not been extensively described. The purpose of this study is to compare spinal cord drift between cervical laminoplasty and LF in patients with CSM. METHODS: The laminoplasty group consisted of 22 patients, and the LF group consisted of 44 patients. Preoperative and postoperative alignment was measured using the Cobb angle (C2-C7). Spinal cord position was measured on axial T2-magnetic resonance imaging of the cervical spine preoperatively and postoperatively. Spinal cord drift was quantified by subtracting preoperative values from postoperative values. Functional improvement was assessed using the modified Japanese Orthopaedic Association (mJOA) score. RESULTS: Mean spinal cord drift was higher following LF compared to laminoplasty (2.70 vs 1.71 mm, P < .01). Using logistic regression analysis, there was no correlation between sagittal alignment and spinal cord drift. Both groups showed an improvement in mJOA scores postoperatively compared to their preoperative values (laminoplasty, +2.0, P = .012; LF, +2.4, P < .01). However, there was no difference in mJOA score improvement postoperatively between both groups (P = .482). CONCLUSIONS: This study demonstrates that patients who had LF for CSM achieved more spinal cord drift postoperatively compared to those who had laminoplasty. However, the increased drift did not translate into superior functional outcome as measured by the mJOA score. LEVEL OF EVIDENCE: 3. CLINICAL RELEVANCE: Spinal cord drift following LF may differ from laminoplasty in patients undergoing surgery for CSM. This finding should be considered when assessing CSM patients for surgical intervention.

Dynamic decision making and value computations in medial frontal cortex.

Dynamic decision making requires an intact medial frontal cortex. Recent work has combined theory and single-neuron measurements in frontal cortex to advance models of decision making. We review behavioral tasks that have been used to study dynamic decision making and algorithmic models of these tasks using reinforcement learning theory. We discuss studies linking neurophysiology and quantitative decision variables. We conclude with hypotheses about the role of other cortical and subcortical structures in dynamic decision making, including ascending neuromodulatory systems.

Locus coeruleus spiking differently correlates with S1 cortex activity and pupil diameter in a tactile detection task.

We examined the relationships between activity in the locus coeruleus (LC), activity in the primary somatosensory cortex (S1), and pupil diameter in mice performing a tactile detection task. While LC spiking consistently preceded S1 membrane potential depolarization and pupil dilation, the correlation between S1 and pupil was more heterogeneous. Furthermore, the relationships between LC, S1, and pupil varied on timescales of sub-seconds to seconds within trials. Our data suggest that pupil diameter can be dissociated from LC spiking and cannot be used as a stationary index of LC activity.

A quantitative reward prediction error signal in the ventral pallidum.

The nervous system is hypothesized to compute reward prediction errors (RPEs) to promote adaptive behavior. Correlates of RPEs have been observed in the midbrain dopamine system, but the extent to which RPE signals exist in other reward-processing regions is less well understood. In the present study, we quantified outcome history-based RPE signals in the ventral pallidum (VP), a basal ganglia region functionally linked to reward-seeking behavior. We trained rats to respond to reward-predicting cues, and we fit computational models to predict the firing rates of individual neurons at the time of reward delivery. We found that a subset of VP neurons encoded RPEs and did so more robustly than the nucleus accumbens, an input to the VP. VP RPEs predicted changes in task engagement, and optogenetic manipulation of the VP during reward delivery bidirectionally altered rats' subsequent reward-seeking behavior. Our data suggest a pivotal role for the VP in computing teaching signals that influence adaptive reward seeking.

Stable Representations of Decision Variables for Flexible Behavior.

Decisions occur in dynamic environments. In the framework of reinforcement learning, the probability of performing an action is influenced by decision variables. Discrepancies between predicted and obtained rewards (reward prediction errors) update these variables, but they are otherwise stable between decisions. Although reward prediction errors have been mapped to midbrain dopamine neurons, it is unclear how the brain represents decision variables themselves. We trained mice on a dynamic foraging task in which they chose between alternatives that delivered reward with changing probabilities. Neurons in the medial prefrontal cortex, including projections to the dorsomedial striatum, maintained persistent firing rate changes over long timescales. These changes stably represented relative action values (to bias choices) and total action values (to bias response times) with slow decay. In contrast, decision variables were weakly represented in the anterolateral motor cortex, a region necessary for generating choices. Thus, we define a stable neural mechanism to drive flexible behavior.

Toward a multiscale modeling framework for understanding serotonergic function.

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin.