A defining feature of sleep is reduced responsiveness to external stimuli, but the mechanisms mediating sensory-evoked arousal remain unclear. We hypothesized that reduced locus coeruleus (LC) norepinephrine (NE) activity during sleep mediates unresponsiveness, and its action promotes sensory-evoked awakenings. We tested this using electrophysiological, behavioral, pharmacological, and optogenetic techniques alongside auditory stimulation in freely behaving rats. We found that systemic reduction in NE signaling lowered probability of sound-evoked awakenings (SEAs). The level of tonic LC activity during sleep anticipated SEAs. Optogenetic LC activation promoted arousal as evident in sleep-wake transitions, EEG desynchronization, and pupil dilation. Minimal LC excitation before sound presentation increased SEA probability. Optogenetic LC silencing using a soma-targeted anion-conducting channelrhodopsin (stGtACR2) suppressed LC spiking and constricted pupils. Brief periods of LC opto-silencing reduced the probability of SEAs. Thus, LC-NE activity determines the likelihood of sensory-evoked awakenings, and its reduction during sleep constitutes a key factor mediating behavioral unresponsiveness.

The locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI). We simulate a classic Go/No-go reward learning task and a three-arm ‘explore/exploit’ task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action’ prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error–reflected in LC firing and noradrenaline release–to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC). In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC that translates state-action prediction errors into an optimal balance between plasticity and stability.

16The locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) Author Summary 35The brain uses sensory information to build internal models and make predictions about the world. 36When errors of prediction occur, models must be updated to ensure desired outcomes are still 37 achieved. Neuromodulator chemicals provide a possible pathway for triggering such changes in brain 38 state. One such neuromodulator, noradrenaline, originates predominantly from a cluster of neurons 39 in the brainstem -the locus coeruleus (LC) -and plays a key role in behaviour, for instance, in 40 determining the balance between exploiting or exploring the environment. 41Here we use Active Inference (AI), a mathematical model of perception and action, to formally 42 describe LC function. We propose that LC activity is triggered by errors in prediction and that the 43 subsequent release of noradrenaline alters the rate of learning about the environment. Biologically, 44this describes an LC-cortex feedback loop promoting behavioural flexibility in times of uncertainty. 45We model LC output as a simulated animal performs two tasks known to elicit archetypal 46 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/340620 doi: bioRxiv preprint first posted online Jun. 6, 2018; 3 responses. We find that experimentally observed 'phasic' and 'tonic' patterns of LC activity emerge 47 naturally, and that modulation of learning rates improves task performance. This provides a simple, 48 unified computational account of noradrenergic computational function within a general model of 49 behaviour. 50 51

Micturition requires precise control of bladder and urethral sphincter via parasympathetic, sympathetic and somatic motoneurons. This involves a spino-bulbospinal control circuit incorporating Barrington’s nucleus in the pons (Barr). Ponto-spinal glutamatergic neurons that express corticotrophin-releasing hormone (CRH) form one of the largest Barr cell populations. BarrCRH neurons can generate bladder contractions, but it is unknown whether they act as a simple switch or provide a high-fidelity pre-parasympathetic motor drive and whether their activation can actually trigger voids. Combined opto- and chemo-genetic manipulations along with multisite extracellular recordings in urethane anaesthetised CRHCre mice show that BarrCRH neurons provide a probabilistic drive that generates co-ordinated voids or non-voiding contractions depending on the phase of the micturition cycle. CRH itself provides negative feedback regulation of this process. These findings inform a new inferential model of autonomous micturition and emphasise the importance of the state of the spinal gating circuit in the generation of voiding.

AbstractA defining feature of sleep is reduced responsiveness to external stimuli, but the mechanisms gating sensory-evoked arousal remain unclear. We hypothesized that reduced locus-coeruleus norepinephrine (LC-NE) activity during sleep mediates unresponsiveness, and its action promotes sensory-evoked awakenings. We tested this using electrophysiological, behavioral, pharmacological, and optogenetic techniques alongside auditory stimulation in freely behaving rats. We found that systemic reduction of NE signaling lowered probability of sound-evoked awakenings (SEAs). The level of tonic LC activity during sleep anticipated SEAs. Optogenetic LC activation promoted arousal as evident in sleep-wake transitions, EEG desynchronization, and pupil dilation. Importantly, liminal LC excitation before sound presentation increased SEA probability. Optogenetic LC silencing using a soma-targeted anion-conducting channelrhodopsin (stGtACR2) suppressed LC spiking and constricted pupils. Brief periods of LC opto-silencing reduced the probability of SEAs. Thus, LC-NE activity determines the likelihood of sensory-evoked awakenings and its reduction during sleep constitutes a key factor mediating behavioral unresponsiveness.

We anticipate that the adoption of these techniques will improve microneurography experimental efficiency, adds an important visual learning aid and increases the generalisability of the approach.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Laser evoked potentials (LEPs) – the EEG response to temporally-discrete thermal stimuli – are commonly used in experimental pain studies in humans. Such stimuli selectively activate nociceptors and produce EEG features which correlate with pain intensity. The rodent LEP has been proposed to be a translational biomarker of nociception and pain, however its validity has been questioned because of reported differences in the classes of nociceptive fibres mediating the response. Here we use a machine learning, trial by trial analysis approach on wavelet-denoised LEPs generated by stimulation of the plantar hindpaw of rats. The LEP amplitude was more strongly related to behavioural response than to laser stimulus energy. A simple decision tree classifier using LEP features was able to predict behavioural responses with 73% accuracy. An examination of the features used by the classifier showed that mutually exclusive short and long latency LEP peaks were clearly seen in single-trial data, yet were not evident in grand average data pooled from multiple trials. This bimodal distribution of LEP latencies was mirrored in the paw withdrawal latencies which were preceded and predicted by the LEP responses. The proportion of short latency events was increased after intradermal application of high dose capsaicin (to defunctionalise TRPV1 expressing nociceptors), suggesting they were mediated by Aδ-fibres (specifically AMH-I). These findings demonstrate that both C- and Aδ-fibres contribute to rodent LEPs and concomitant behavioural responses, providing a real-time assay of specific fibre function in conscious animals. Single-trial analysis approaches can improve the utility of LEPs as a translatable biomarker of pain.