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      Spike Timing Regulation on the Millisecond Scale by Distributed Synaptic Plasticity at the Cerebellum Input Stage: A Simulation Study

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          Abstract

          The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular-layer network. In response to mossy fiber (MF) bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at MF to granule cell synapses regulated the delay of the first spike and the weight at MF and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First-spike timing was regulated with millisecond precision and the number of spikes ranged from zero to three. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time-scale and allows the cerebellar granular layer to flexibly control burst transmission along the MF pathway.

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          Most cited references77

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          A theory of cerebellar cortex.

          D. Marr (1969)
          1. A detailed theory of cerebellar cortex is proposed whose consequence is that the cerebellum learns to perform motor skills. Two forms of input-output relation are described, both consistent with the cortical theory. One is suitable for learning movements (actions), and the other for learning to maintain posture and balance (maintenance reflexes).2. It is known that the cells of the inferior olive and the cerebellar Purkinje cells have a special one-to-one relationship induced by the climbing fibre input. For learning actions, it is assumed that:(a) each olivary cell responds to a cerebral instruction for an elemental movement. Any action has a defining representation in terms of elemental movements, and this representation has a neural expression as a sequence of firing patterns in the inferior olive; and(b) in the correct state of the nervous system, a Purkinje cell can initiate the elemental movement to which its corresponding olivary cell responds.3. Whenever an olivary cell fires, it sends an impulse (via the climbing fibre input) to its corresponding Purkinje cell. This Purkinje cell is also exposed (via the mossy fibre input) to information about the context in which its olivary cell fired; and it is shown how, during rehearsal of an action, each Purkinje cell can learn to recognize such contexts. Later, when the action has been learnt, occurrence of the context alone is enough to fire the Purkinje cell, which then causes the next elemental movement. The action thus progresses as it did during rehearsal.4. It is shown that an interpretation of cerebellar cortex as a structure which allows each Purkinje cell to learn a number of contexts is consistent both with the distributions of the various types of cell, and with their known excitatory or inhibitory natures. It is demonstrated that the mossy fibre-granule cell arrangement provides the required pattern discrimination capability.5. The following predictions are made.(a) The synapses from parallel fibres to Purkinje cells are facilitated by the conjunction of presynaptic and climbing fibre (or post-synaptic) activity.(b) No other cerebellar synapses are modifiable.(c) Golgi cells are driven by the greater of the inputs from their upper and lower dendritic fields.6. For learning maintenance reflexes, 2(a) and 2(b) are replaced by2'. Each olivary cell is stimulated by one or more receptors, all of whose activities are usually reduced by the results of stimulating the corresponding Purkinje cell.7. It is shown that if (2') is satisfied, the circuit receptor --> olivary cell --> Purkinje cell --> effector may be regarded as a stabilizing reflex circuit which is activated by learned mossy fibre inputs. This type of reflex has been called a learned conditional reflex, and it is shown how such reflexes can solve problems of maintaining posture and balance.8. 5(a), and either (2) or (2') are essential to the theory: 5(b) and 5(c) are not absolutely essential, and parts of the theory could survive the disproof of either.
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            NMDA receptor subunits: diversity, development and disease

            Current Opinion in Neurobiology, 11(3), 327-335
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              Cerebellar circuitry as a neuronal machine.

              Masao ITO (2006)
              Shortly after John Eccles completed his studies of synaptic inhibition in the spinal cord, for which he was awarded the 1963 Nobel Prize in physiology/medicine, he opened another chapter of neuroscience with his work on the cerebellum. From 1963 to 1967, Eccles and his colleagues in Canberra successfully dissected the complex neuronal circuitry in the cerebellar cortex. In the 1967 monograph, "The Cerebellum as a Neuronal Machine", he, in collaboration with Masao Ito and Janos Szentágothai, presented blue-print-like wiring diagrams of the cerebellar neuronal circuitry. These stimulated worldwide discussions and experimentation on the potential operational mechanisms of the circuitry and spurred theoreticians to develop relevant network models of the machinelike function of the cerebellum. In following decades, the neuronal machine concept of the cerebellum was strengthened by additional knowledge of the modular organization of its structure and memory mechanism, the latter in the form of synaptic plasticity, in particular, long-term depression. Moreover, several types of motor control were established as model systems representing learning mechanisms of the cerebellum. More recently, both the quantitative preciseness of cerebellar analyses and overall knowledge about the cerebellum have advanced considerably at the cellular and molecular levels of analysis. Cerebellar circuitry now includes Lugaro cells and unipolar brush cells as additional unique elements. Other new revelations include the operation of the complex glomerulus structure, intricate signal transduction for synaptic plasticity, silent synapses, irregularity of spike discharges, temporal fidelity of synaptic activation, rhythm generators, a Golgi cell clock circuit, and sensory or motor representation by mossy fibers and climbing fibers. Furthermore, it has become evident that the cerebellum has cognitive functions, and probably also emotion, as well as better-known motor and autonomic functions. Further cerebellar research is required for full understanding of the cerebellum as a broad learning machine for neural control of these functions.
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                Author and article information

                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                22 May 2013
                2013
                : 7
                : 64
                Affiliations
                [1] 1Neurophysiology Unit, Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy
                [2] 2Consorzio Interuniversitario per le Scienze Fisiche della Materia Pavia, Italy
                [3] 3Department of Computer Architecture and Technology, University of Granada Granada, Spain
                [4] 4Brain Connectivity Center, IRCCS Istituto Neurologico Nazionale C. Mondino Pavia, Italy
                Author notes

                Edited by: Julie Wall, University of London, UK

                Reviewed by: Carson C. Chow, National Institutes of Health, USA; Simon Davidson, University of Manchester, UK

                *Correspondence: Jesús A. Garrido and Egidio D’Angelo, Brain Connectivity Center, IRCCS Istituto Neurologico Nazionale C. Mondino, Via Mondino 2, Pavia 27100, Italy. e-mail: jesus.garrido@ 123456unipv.it ; dangelo@ 123456unipv.it
                Article
                10.3389/fncom.2013.00064
                3660969
                23720626
                28937ef1-495d-4f9b-8172-0bd479679035
                Copyright © 2013 Garrido, Ros and D’Angelo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 15 March 2013
                : 02 May 2013
                Page count
                Figures: 10, Tables: 4, Equations: 6, References: 83, Pages: 18, Words: 11424
                Categories
                Neuroscience
                Original Research

                Neurosciences
                spiking network,spike timing,cerebellum,granular layer,ltp,ltd
                Neurosciences
                spiking network, spike timing, cerebellum, granular layer, ltp, ltd

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