Mini-Review: Synaptic Integration in the Cerebellar Nuclei—Perspectives From Dynamic Clamp and Computer Simulation Studies

Jaeger, Dieter

doi:10.1007/s12311-011-0248-3

Cited by 25 publications

(16 citation statements)

References 72 publications

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“…Smaller neurons have a lower capacitance with a broader action potential, a strong late component after-hyperpolarization and longer rebound (for review see [17]). Our whole-cell in vivo recordings of post-hoc morphologically identified cells under anesthesia support these trends and will allow modelers of the CN to further expand and fine-tune the computational repertoire of CNNs in intact animals [63,78,79]. …”

Section: Discussionmentioning

confidence: 76%

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

2016

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Cerebellar nuclei neurons integrate sensorimotor information and form the final output of the cerebellum, projecting to premotor brainstem targets. This implies that, in contrast to specialized neurons and interneurons in cortical regions, neurons within the nuclei encode and integrate complex information that is most likely reflected in a large variation of intrinsic membrane properties and integrative capacities of individual neurons. Yet, whether this large variation in properties is reflected in a heterogeneous physiological cell population of cerebellar nuclei neurons with well or poorly defined cell types remains to be determined. Indeed, the cell electrophysiological properties of cerebellar nuclei neurons have been identified in vitro in young rodents, but whether these properties are similar to the in vivo adult situation has not been shown. In this comprehensive study we present and compare the in vivo properties of 144 cerebellar nuclei neurons in adult ketamine-xylazine anesthetized mice. We found regularly firing (N = 88) and spontaneously bursting (N = 56) neurons. Membrane-resistance, capacitance, spike half-width and firing frequency all widely varied as a continuum, ranging from 9.63 to 3352.1 MΩ, from 6.7 to 772.57 pF, from 0.178 to 1.98 ms, and from 0 to 176.6 Hz, respectively. At the same time, several of these parameters were correlated with each other. Capacitance decreased with membrane resistance (R2 = 0.12, P<0.001), intensity of rebound spiking increased with membrane resistance (for 100 ms duration R2 = 0.1503, P = 0.0011), membrane resistance decreased with membrane time constant (R2 = 0.045, P = 0.031) and increased with spike half-width (R2 = 0.023, P<0.001), while capacitance increased with firing frequency (R2 = 0.29, P<0.001). However, classes of neuron subtypes could not be identified using merely k-clustering of their intrinsic firing properties and/or integrative properties following activation of their Purkinje cell input. Instead, using whole-cell parameters in combination with morphological criteria revealed by intracellular labelling with Neurobiotin (N = 18) allowed for electrophysiological identification of larger (29.3–50 μm soma diameter) and smaller (< 21.2 μm) cerebellar nuclei neurons with significant differences in membrane properties. Larger cells had a lower membrane resistance and a shorter spike, with a tendency for higher capacitance. Thus, in general cerebellar nuclei neurons appear to offer a rich and wide continuum of physiological properties that stand in contrast to neurons in most cortical regions such as those of the cerebral and cerebellar cortex, in which different classes of neurons operate in a narrower territory of electrophysiological parameter space. The current dataset will help computational modelers of the cerebellar nuclei to update and improve their cerebellar motor learning and performance models by incorporating the large variation of the in vivo properties of cerebellar nuclei neurons. The cellular complexity of cerebellar nuclei neurons m...

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Section: Discussionmentioning

confidence: 76%

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

2016

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“…Realistic modeling of cerebellar cells has been taking place for decades, and there are several models that could be used for this endeavor (e.g. [25, 38, 65, 66]). These models can point out possible necessary conditions for the granule layer inputs, Purkinje cells, and molecular layer interneurons in order to express stochastic synchronization.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves

Verduzco-Flores

2015

The Journal of Mathematical Neuroscience (JMN)

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Simple-spike synchrony between Purkinje cells projecting to a common neuron in the deep cerebellar nucleus is emerging as an important factor in the encoding of output information from cerebellar cortex. A phenomenon known as stochastic synchronization happens when uncoupled oscillators synchronize due to correlated inputs. Stochastic synchronization is a viable mechanism through which simple-spike synchrony could be generated, but it has received scarce attention, perhaps because the presence of feedforward inhibition in the input to Purkinje cells makes insights difficult. This paper presents a method to account for feedforward inhibition so the usual mathematical approaches to stochastic synchronization can be applied. The method consists in finding a single Phase Response Curve, called the equivalent PRC, that accounts for the effects of both excitatory inputs and delayed feedforward inhibition from molecular layer interneurons. The results suggest that a theory of stochastic synchronization for the case of feedforward inhibition may not be necessary, since this case can be approximately reduced to the case of inputs characterized by a single PRC. Moreover, feedforward inhibition could in many situations increase the level of synchrony experienced by Purkinje cells.

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“…Recently, we have shown that a model encompassing bidirectional plasticity at the GC-PC synapse supervised by CFs6, PC inhibition by molecular layer interneurons (MLIs)78 as well as plasticity at the mossy fiber (MF) to medial vestibular nuclei (MVN) synapse910 (Fig. 1a) can reliably reproduce empirical data of the vestibulo-ocular reflex (VOR) phase-reversal task.…”

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confidence: 99%

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

Badura

Clopath

Schonewille

et al. 2016

Sci Rep

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Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity.

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Mini-Review: Synaptic Integration in the Cerebellar Nuclei—Perspectives From Dynamic Clamp and Computer Simulation Studies

Cited by 25 publications

References 72 publications

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

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