The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity

Jordan, Rebecca; Keller, Georg B.

doi:10.1101/2022.11.08.515698

Cited by 5 publications

(5 citation statements)

References 71 publications

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“…In addition, NE may exert its effects on both neuronal and non-neuronal cells (Paukert et al, 2014; Mu et al, 2019), providing a rich substrate for broadcasting an ascending learning signal across large volumes of cortex. This learning signal may generalize beyond reinforcement learning; artificial excitation of LC changes sensory-driven learning as well (Glennon et al, 2019; Jordan and Keller, 2022; McBurney-Lin et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

Cohen

2022

Preprint

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The cerebral cortex generates flexible behavior by learning. Reinforcement learning is thought to be driven by error signals in midbrain dopamine neurons. However, they project more densely to basal ganglia than cortex, leaving open the possibility of another source of learning signals for cortex. The locus coeruleus (LC) contains most of the brain's norepinephrine (NE) neurons and project broadly to cortex. We measured activity from identified mouse LC-NE neurons during a behavioral task requiring ongoing learning from reward prediction errors (RPEs). We found two types of LC-NE neurons: neurons with wide action potentials (type I) were excited by positive RPE and showed an increasing relationship with change of choice likelihood. Neurons with thin action potentials (type II) were excited by lack of reward and showed a decreasing relationship with change of choice likelihood. Silencing LC-NE neurons changed future choices, as predicted from the electrophysiological recordings and a model of how RPEs are used to guide learning. We reveal functional heterogeneity of a neuromodulatory system in the brain and show that NE inputs to cortex act as a quantitative learning signal for flexible behavior.

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Section: Discussionmentioning

confidence: 99%

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

Cohen

2022

Preprint

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“…Prior work has demonstrated that LC neurons and axons fire synchronously to aversive events and prediction errors ( Uematsu et al, 2017 ; Breton-Provencher et al, 2022 ; Jordan and Keller, 2022 ) but that axons can be asynchronous for appetitive events ( Breton-Provencher et al, 2022 ). We hypothesized that these distinct patterns of LC NE neuronal activity (local asynchronous and global synchronous) might be reflected at cellular scale within the mPFC in the temporal correlations of local and global NE fields.…”

Section: Resultsmentioning

confidence: 99%

“…The spatial autocorrelation was approximated well by a double exponential model, indicating that local NE correlations fall off with spatial distance (Fig.2C). These dynamics thus appeared compatible with a model in which diffusive spread of NE from release sites contributes to variation in local NE dynamics.Prior work has demonstrated that LC neurons and axons fire synchronously to aversive events and prediction errors(Breton-Provencher et al, 2022;Jordan & Keller, 2022;Uematsu et al, 2017), but that axons can be asynchronous for appetitive events(Breton-Provencher et al, 2022). We hypothesized that these distinct patterns of LCNE neuronal activity (local asynchronous and global synchronous) might be reflected at cellular scale within mPFC in the temporal correlations of local and global NE fields.…”

mentioning

confidence: 89%

Spatiotemporal Organization of Prefrontal Norepinephrine Influences Neuronal Activity

Glaeser-Khan,

Savalia,

Cressy

et al. 2024

eNeuro

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Norepinephrine (NE), a neuromodulator released by locus coeruleus (LC) neurons throughout cortex, influences arousal and learning through extra-synaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the prefrontal cortex (PFC) affect neuronal firing, we employed in vivo two-photon imaging of layer 2/3 of PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. In this proof of principle study, we found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.Significance StatementNE is a neuromodulator which plays a critical role in learning and arousal, but understanding its spatial scale has been limited by technical barriers. Here, we utilized two-photon imaging of GPCR-based sensors, light sheet imaging, and computational modeling to gain insight into the fine scale organization of NE in PFC. We found that NE can influence neuronal activity at a local scale within cortex, which has not been shown before, and we developed new computational approaches to analyzing two-photon imaging of GPCR based fluorescent sensors. This insight will facilitate improved understanding of NE's role in motivated behaviors, as well as new approaches for understanding local neurotransmitter function.

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“…Alternatively, errors could be computed in the pulvinar or in V1 from the comparison of visual input with predictions conveyed by top-down input from higher-order cortical areas [5][6][7][8][9][10]14 , and amplified through pulvinar-V1 recurrent connections. Either way, signals in V1 may be further enhanced by neuromodulators such as acetylcholine or noradrenaline 28,46 that may signal stimulus saliency and novelty, or surprise more generally 51,52 .…”

Section: Discussionmentioning

confidence: 99%

Cooperative thalamocortical circuit mechanism for sensory prediction errors

Furutachi

Franklin

Mrsic-Flogel

et al. 2023

Preprint

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The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events. Despite the importance of prediction error signals for various neural computations across multiple brain regions, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit required for generating sensory prediction errors in mouse primary visual cortex (V1). Using calcium imaging with optogenetic manipulations as mice traverse a familiar virtual environment, we show that violation of animals' predictions by an unexpected visual stimulus preferentially boosts responses of layer 2/3 V1 neurons most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing a non-specific surprise or difference signal about how the visual input deviates from animals' predictions. Selective amplification of unexpected visual input is implemented by a cooperative mechanism requiring thalamic input from the pulvinar, and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing (SOM) inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response-amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.

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The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity

Cited by 5 publications

References 71 publications

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

Spatiotemporal Organization of Prefrontal Norepinephrine Influences Neuronal Activity

Cooperative thalamocortical circuit mechanism for sensory prediction errors

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