Modular organization of cerebellar climbing fiber inputs during goal-directed behavior

The cerebellum has a parasagittal modular architecture characterized by precisely organized climbing fiber (CF) projections that are congruent with alternating aldolase C/zebrin II expression. However, the behavioral relevance of CF inputs into individual modules remains poorly understood. Here, we used two-photon calcium imaging in the cerebellar hemisphere Crus II in mice performing an auditory go/no-go task to investigate the functional differences in CF inputs to modules. CF signals in medial modules show anticipatory decreases, early increases, secondary increases, and reward-related increases or decreases, which represent quick motor initiation, go cues, fast motor behavior, and positive reward outcomes. CF signals in lateral modules show early increases and reward-related decreases, which represent no-go and/or go cues and positive reward outcomes. The boundaries of CF functions broadly correspond to those of aldolase C patterning. These results indicate that spatially segregated CF inputs in different modules play distinct roles in the execution of goal-directed behavior.

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Author response: Modular organization of cerebellar climbing fiber inputs during goal-directed behavior

10.7554/elife.47021.038 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shinichiro Tsutsumi ◽

Naoki Hidaka ◽

Yoshikazu Isomura ◽

Masanori Matsuzaki ◽

Kenji Sakimura ◽

...

Keyword(s):

Climbing Fiber ◽

Author Response ◽

Modular Organization ◽

Goal Directed Behavior

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Decision letter: Modular organization of cerebellar climbing fiber inputs during goal-directed behavior

10.7554/elife.47021.037 ◽

2019 ◽

Keyword(s):

Climbing Fiber ◽

Modular Organization ◽

Goal Directed Behavior

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Aldolase C/Zebrin II is Released to the Extracellular Space after Stroke and Inhibits the Network Activity of Cortical Neurons

Neurochemical Research ◽

10.1007/s11064-006-9169-9 ◽

2006 ◽

Vol 31 (11) ◽

pp. 1297-1303 ◽

Cited By ~ 10

Author(s):

Stephanie Linke ◽

Philipp Goertz ◽

Stephan L. Baader ◽

Volkmar Gieselmann ◽

Mario Siebler ◽

...

Keyword(s):

Cortical Neurons ◽

Extracellular Space ◽

Network Activity ◽

Aldolase C ◽

Zebrin Ii

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Modular organization of motor behavior in the frog's spinal cord

Trends in Neurosciences ◽

10.1016/0166-2236(95)94494-p ◽

1995 ◽

Vol 18 (10) ◽

pp. 442-446 ◽

Cited By ~ 117

Author(s):

Emilio Bizzi ◽

Simon F. Giszter ◽

Eric Loeb ◽

Fernando A. Mussa-Ivaldi ◽

Philippe Saltiel

Keyword(s):

Spinal Cord ◽

Motor Behavior ◽

Modular Organization

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Congruence of zebrin II expression and functional zones defined by climbing fiber topography in the flocculus

Neuroscience ◽

10.1016/j.neuroscience.2008.08.062 ◽

2008 ◽

Vol 157 (1) ◽

pp. 57-69 ◽

Cited By ~ 15

Author(s):

J.M.P. Pakan ◽

D.R.W. Wylie

Keyword(s):

Climbing Fiber ◽

Zebrin Ii ◽

Functional Zones

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Zebrin II / Aldolase C Expression in the Cerebellum of the Western Diamondback Rattlesnake (Crotalus atrox)

PLoS ONE ◽

10.1371/journal.pone.0117539 ◽

2015 ◽

Vol 10 (2) ◽

pp. e0117539 ◽

Cited By ~ 6

Author(s):

Joel W. Aspden ◽

Carol L. Armstrong ◽

Cristian I. Gutierrez-Ibanez ◽

Richard Hawkes ◽

Andrew N. Iwaniuk ◽

...

Keyword(s):

Crotalus Atrox ◽

Aldolase C ◽

Zebrin Ii ◽

Diamondback Rattlesnake

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Cerebellar complex spikes multiplex complementary behavioral information

PLoS Biology ◽

10.1371/journal.pbio.3001400 ◽

2021 ◽

Vol 19 (9) ◽

pp. e3001400

Author(s):

Akshay Markanday ◽

Junya Inoue ◽

Peter W. Dicke ◽

Peter Thier

Keyword(s):

Motor Behavior ◽

Low Frequency ◽

Climbing Fiber ◽

Related Information ◽

Oculomotor Vermis ◽

Corrective Saccades ◽

Motor Error ◽

Saccade Task ◽

Time Specific ◽

Complex Spikes

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves 2 types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC’s climbing fiber input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks, one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibers? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that, in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. Hence PCs receive a multiplexed climbing fiber input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.

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The ZEBRA activation domain: modular organization and mechanism of action

Molecular and Cellular Biology ◽

10.1128/mcb.13.11.7045-7055.1993 ◽

1993 ◽

Vol 13 (11) ◽

pp. 7045-7055

Author(s):

T Chi ◽

M Carey

Keyword(s):

Rna Polymerase Ii ◽

Transcriptional Response ◽

Nuclear Extract ◽

Modular Organization ◽

Modular Architecture ◽

Activation Domain ◽

Kinetic Measurements ◽

Complex Assembly ◽

Amino Terminal

An RNA polymerase II activator often contains several regions that contribute to its potency, an organization ostensibly analogous to the modular architecture of promoters and enhancers. The regulatory significance of this parallel organization has not been systematically explored. We considered this problem by examining the activation domain of the Epstein-Barr virus transactivator ZEBRA. We performed our experiments in vitro so that the activator concentrations, stabilities, and affinities for DNA could be monitored. ZEBRA and various amino-terminal deletion derivatives, expressed in and purified from Escherichia coli, were assayed in a HeLa cell nuclear extract for the ability to activate model reporter templates bearing one, three, five, and seven upstream ZEBRA binding sites. Our data show that ZEBRA contains four modules that contribute to its potency in vitro. The modules operate interchangeably with promoter sites to determine the transcriptional response such that the loss of modules can be compensated for by increasing promoter sites. Potassium permanganate footprinting was used to show that transcriptional stimulation is a consequence of the activator's ability to promote preinitiation complex assembly. Kinetic measurements of transcription complex assembly in a reconstituted system indicate that ZEBRA promotes formation of a subcomplex requiring the TFIIA and TFIID fractions, where TFIIA acts as an antirepressor. We propose a model in which the concentration of DNA-bound activation modules in the vicinity of the gene initiates synergistic transcription complex assembly.

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MOSAIC Model for Sensorimotor Learning and Control

Neural Computation ◽

10.1162/089976601750541778 ◽

2001 ◽

Vol 13 (10) ◽

pp. 2201-2220 ◽

Cited By ~ 426

Author(s):

Masahiko Haruno ◽

Daniel M. Wolpert ◽

Mitsuo Kawato

Keyword(s):

Em Algorithm ◽

Gradient Descent ◽

Motor Behavior ◽

Descent Method ◽

Gradient Descent Method ◽

Modular Architecture ◽

Mosaic Model ◽

Inverse Models ◽

And Control ◽

Initial Starting

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

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