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5 6 Summary: 7 8Dendritic coincidence detection is thought fundamental to neuronal processing, yet 9 the underlying dendritic voltage-calcium relationship remains unexplored in awake animals. 10Here, using simultaneous voltage and calcium two-photon imaging of Purkinje neuron spiny 11 dendrites, we show how coincident sub-and suprathreshold synaptic inputs modulate 12 dendritic calcium signaling during sensory stimulation in awake mice. Sensory stimulation 13 evokes subthreshold excitatory and inhibitory post-synaptic potentials, that coincide with 14 suprathreshold dendritic spikes triggered by climbing fiber and parallel fiber synaptic input. 15Purkinje neuron dendrites integrate these inputs in a time-dependent and non-linear fashion to 16 enhance the sensory evoked dendritic calcium signal. Intrinsic supra-linear dendritic 17 mechanisms, including voltage gated calcium channels and metabotropic glutamate receptors, 18 are recruited cooperatively to expand the dynamic range of sensory evoked dendritic calcium 19 signals. This establishes how dendrites use multiple interplaying mechanisms to perform 20 coincidence detection, as a fundamental and ongoing feature of dendritic integration during 21 behavior. 22 23 24 Keywords: 25 26 Dendritic integration, coincidence detection, Purkinje neuron, cerebellum, awake, 27 subthreshold, dendritic complex spike, dendritic spike voltage imaging, two-photon 28 microscopy 29 Introduction: 30 31Dendritic integration is fundamental to signal processing in the brain. So far, most 32 studies on dendritic integration were performed in vitro, in the absence of physiological 33 inputs (Larkum et al., 2009;Markram et al., 1997; Stuart and Häusser, 2001; Wang et al., 34 2000). As such, our understanding of the basic components of dendritic integration; the 35 frequency, amplitude, and spatio-temporal distribution of synaptic inputs under physiological 36 conditions, and how these inputs are integrated by dendrites in awake behaving animals, 37 remains incomplete. 38 39 Coincidence detection is a basic form of dendritic integration. By detecting coincident 40 synaptic input, it is thought that neurons distinguish important signals from ongoing synaptic 41 activity and modify synaptic strength through synaptic plasticity (Brown et al., 1990). 42 43Purkinje neuron (PN) dendrites in the cerebellum are ideally suited to perform 44 coincidence detection. They receive excitatory synaptic input from two distinct pathways; the 45 climbing fiber (CF) and numerous parallel fibers (PF). CFs project from the inferior olive and 46 evoke suprathreshold dendritic calcium spikes. PFs relay mossy fiber activity originating 47 of PF and CF input has not been confirmed in vivo, and recent studies have failed to detect 64 supra-linear dendritic calcium signals triggered by coincident PF and CF input (Gaffield et 65 al., 2019;Gaffield et al., 2018). 66 67 Due to technical limitations, PF evoked input to PN dendrites -that are predominantly 68 subthreshold voltage signals -have been thus far undetect...
5 6 Summary: 7 8Dendritic coincidence detection is thought fundamental to neuronal processing, yet 9 the underlying dendritic voltage-calcium relationship remains unexplored in awake animals. 10Here, using simultaneous voltage and calcium two-photon imaging of Purkinje neuron spiny 11 dendrites, we show how coincident sub-and suprathreshold synaptic inputs modulate 12 dendritic calcium signaling during sensory stimulation in awake mice. Sensory stimulation 13 evokes subthreshold excitatory and inhibitory post-synaptic potentials, that coincide with 14 suprathreshold dendritic spikes triggered by climbing fiber and parallel fiber synaptic input. 15Purkinje neuron dendrites integrate these inputs in a time-dependent and non-linear fashion to 16 enhance the sensory evoked dendritic calcium signal. Intrinsic supra-linear dendritic 17 mechanisms, including voltage gated calcium channels and metabotropic glutamate receptors, 18 are recruited cooperatively to expand the dynamic range of sensory evoked dendritic calcium 19 signals. This establishes how dendrites use multiple interplaying mechanisms to perform 20 coincidence detection, as a fundamental and ongoing feature of dendritic integration during 21 behavior. 22 23 24 Keywords: 25 26 Dendritic integration, coincidence detection, Purkinje neuron, cerebellum, awake, 27 subthreshold, dendritic complex spike, dendritic spike voltage imaging, two-photon 28 microscopy 29 Introduction: 30 31Dendritic integration is fundamental to signal processing in the brain. So far, most 32 studies on dendritic integration were performed in vitro, in the absence of physiological 33 inputs (Larkum et al., 2009;Markram et al., 1997; Stuart and Häusser, 2001; Wang et al., 34 2000). As such, our understanding of the basic components of dendritic integration; the 35 frequency, amplitude, and spatio-temporal distribution of synaptic inputs under physiological 36 conditions, and how these inputs are integrated by dendrites in awake behaving animals, 37 remains incomplete. 38 39 Coincidence detection is a basic form of dendritic integration. By detecting coincident 40 synaptic input, it is thought that neurons distinguish important signals from ongoing synaptic 41 activity and modify synaptic strength through synaptic plasticity (Brown et al., 1990). 42 43Purkinje neuron (PN) dendrites in the cerebellum are ideally suited to perform 44 coincidence detection. They receive excitatory synaptic input from two distinct pathways; the 45 climbing fiber (CF) and numerous parallel fibers (PF). CFs project from the inferior olive and 46 evoke suprathreshold dendritic calcium spikes. PFs relay mossy fiber activity originating 47 of PF and CF input has not been confirmed in vivo, and recent studies have failed to detect 64 supra-linear dendritic calcium signals triggered by coincident PF and CF input (Gaffield et 65 al., 2019;Gaffield et al., 2018). 66 67 Due to technical limitations, PF evoked input to PN dendrites -that are predominantly 68 subthreshold voltage signals -have been thus far undetect...
27Classical models of cerebellar learning posit that climbing fibers operate according to a 28 supervised learning rule to instruct changes in motor output by signaling the occurrence of 29 movement errors. However, cerebellar output is also associated with non-motor behaviors, and 30 recently with modulating reward association pathways in the VTA. To test how the cerebellum 31 processes reward related signals in the same type of classical conditioning behavior typically 32 studied to evaluate reward processing in the VTA and striatum, we have used calcium imaging to 33 visualize instructional signals carried by climbing fibers across the lateral cerebellum before and 34 after learning. We find distinct climbing fiber responses in three lateral cerebellar regions that can 35 each signal reward prediction. These instructional signals are well suited to guide cerebellar 36 learning based on reward expectation and enable a cerebellar contribution to reward driven 37 behaviors, thus suggesting a broad role for the lateral cerebellum in reward-based learning. 38 39 Introduction 40The cerebellum plays a key role in motor control and motor learning. However, it is becoming 41 widely appreciated that the cerebellum also contributes significantly to many non-motor processes 42 (Schmahmann, 1991) such as cognition (Kim et al., 1994), social processing (Van Overwalle et 43 al., 2014), aggression (Reis et al., 1973 and emotion (Schmahmann and Caplan, 2006). 44Moreover, cerebellar deficits are associated with non-motor conditions including autism spectrum 45 disorders (Wang et al., 2014), deficits of language processing and vocal learning (Ackermann, 46 2008), schizophrenia (Mothersill et al., 2016 and other impairments (Ivry and Spencer, 2004). 47Together, these observations strongly suggest that the cerebellum, and cerebellar learning, must 48 operate in a manner compatible with adjusting motor as well as non-motor processes. 49Classical models posit that cerebellar learning is instructed by signals carried by an afferent fiber 50 projection from the inferior olive called climbing fibers (Albus, 1971; Ito, 1972; Marr, 1969). Such 51 models suggest that climbing fibers operate according to a supervised learning rule to instruct 52 changes in motor output by signaling the occurrence of movement errors. However, it is unclear 53 how such a supervised learning rule could contribute to modification of many non-motor 54 behaviors. In line with this view, accumulating evidence has demonstrated a wide range of 55 climbing fiber responses that are distinct from motor error signals (Kitazawa et al., 1998; Streng 56 et al., 2017), including prediction error and reward related signals (Heffley et al., 2018; Kostadinov 57 et al., 2019; Larry et al., 2019;Ohmae and Medina, 2015). Together, such data provide evidence 58 that the cerebellum may utilize alternative learning rules to support a wider range of behaviors 59 than can be driven by error-based supervised learning alone. 60Under what conditions might the climbing fiber...
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