Advertisement

Computational Neuroscience of Timing, Plasticity and Function in Cerebellum Microcircuits

  • Shyam DiwakarEmail author
  • Chaitanya Medini
  • Manjusha Nair
  • Harilal Parasuram
  • Asha Vijayan
  • Bipin Nair
Chapter
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 6)

Abstract

Cerebellum has been known to show homogeneity in circuit organization and hence the “modules” or various circuits in the cerebellum are attributed to the diversity of functions such as timing, pattern recognition, movement planning and dysfunctions such as ataxia related to the cerebellum. Ataxia-like conditions, induced by intrinsic excitability changes, disable spiking or bursts and thereby limit the quanta of downstream information. Understanding timing, plasticity and functional roles of cerebellum involve large-scale and microcircuit reconstructions validating molecular mechanisms in population activity. Using mathematical modelling, we attempted to reconstruct information transmission at the granular layer of the cerebellum, a circuit whose role in dysfunctions remain yet to be fully explored. We have employed spiking models to reconstruct timing roles and detailed biophysical models for extracellular activity and local field population response. The roles of inhibition, induced plasticity and their implications in information transmission were evaluated. Modulatory roles of Golgi inhibition and pattern abstraction via optimal storage were estimated. An abstraction of the granular and Purkinje layer circuit for neurorobotic roles such as pattern recognition and spike encoding via two new methods was developed. Simulations suggest plasticity at cerebellar relays may be an important element of tremendous storage capacity reliable in the learning of coordination of actions, sensorimotor or cognitive, in which the cerebellum participates.

Keywords

Granule Cell Spike Train Granular Layer Mossy Fiber Granule Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work derives direction and ideas from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. Authors would like to acknowledge Egidio D’Angelo of University of Pavia, Giovanni Naldi of University of Milan, Sergio Solinas, Thierry Nieus of IIT Genova for their support towards work in this manuscript. This work is supported by Grants SR/CSI/49/2010, SR/CSI/60/2011, SR/CSRI/60/2013, SR/CSRI/61/2014 and Indo-Italy POC 2012-2014 from DST and BT/PR5142/MED/30/764/2012 from DBT, Government of India and partially by Embracing the World.

References

  1. 1.
    Lange W (1975) Cell number and cell density in the cerebellar cortex of man and some other mammals. Cell Tissue Res 157:115–24Google Scholar
  2. 2.
    Herculano-Houzel S (2009) The human brain in numbers: a linearly scaled-up primate brain. Front Hum Neurosci 3:31Google Scholar
  3. 3.
    Luciani L (1891) Il Cervelletto, nuovi studi di fisiologia normale e patologica. coi tipi dei successori Le Monnier, FirenzeGoogle Scholar
  4. 4.
    Manni E, Petrosini L (1997) Luciani’s work on the cerebellum a century later. Trends Neurosci 20:112–116Google Scholar
  5. 5.
    Holmes G (1917) The symptoms of acute cerebellar injuries due to gunshot injuries. Brain 40:461–535Google Scholar
  6. 6.
    Bower JM (1997) Is the cerebellum sensory for motor’s sake, or motor for sensory’s sake: the view from the whiskers of a rat? Prog Brain Res 114:463–96Google Scholar
  7. 7.
    Ivry RB, Baldo J V (1992) Is the cerebellum involved in learning and cognition? Curr Opin Neurobiol 2:212–6Google Scholar
  8. 8.
    Boyd CAR (2010) Cerebellar agenesis revisited. Brain 133:941–4Google Scholar
  9. 9.
    Yu F, Jiang Q, Sun X, Zhang R (2015) A new case of complete primary cerebellar agenesis: clinical and imaging findings in a living patient. Brain 138:e353Google Scholar
  10. 10.
    Medina JF, Garcia KS, Nores WL, Taylor NM, Mauk MD (2000) Timing mechanisms in the cerebellum: testing predictions of a large-scale computer simulation. J Neurosci 20:5516–5525Google Scholar
  11. 11.
    Vos BP, Volny-Luraghi A, Schutter E De (1999) Cerebellar Golgi cells in the rat: receptive fields and timing of responses to facial stimulation. Eur J Neurosci 11:2621–2634Google Scholar
  12. 12.
    Albus JS (1975) A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller(CMAC). J. Dyn. Syst. Meas. ControlGoogle Scholar
  13. 13.
    Tyrrell T, Willshaw D (1992) Cerebellar cortex: its simulation and the relevance of Marr’s theory. Philos Trans R Soc Lond B Biol Sci 336:239–57Google Scholar
  14. 14.
    Eccles JC (1981) Physiology of motor control in man. Appl Neurophysiol 44:5–15Google Scholar
  15. 15.
    Ito M (2000) Mechanisms of motor learning in the cerebellum. Brain Res 886:237–245Google Scholar
  16. 16.
    Albus JS (1971) A theory of cerebellar function. Math Biosci 10:25–61Google Scholar
  17. 17.
    Marr D (1969) A theory of cerebellar cortex. J Physiol 202:437–470Google Scholar
  18. 18.
    Mazzarello P, Haines D, Manto M-U (2012) Camillo Golgi on Cerebellar Granule Cells. Cerebellum 11:5–24–7Google Scholar
  19. 19.
    Eccles JC, Llinás R, Sasaki K (1965) Inhibitory systems in the cerebellar cortex. Proc Aust Assoc Neurol 3:7–14Google Scholar
  20. 20.
    Brunel N, Hakim V, Isope P, Nadal JP, Barbour B (2004) Optimal information storage and the distribution of synaptic weights: Perceptron versus Purkinje cell. Neuron 43:745–757Google Scholar
  21. 21.
    D’Angelo E, Mazzarello P, Prestori F, Mapelli J, Solinas S, Lombardo P, Cesana E, Gandolfi D, Congi L (2011) The cerebellar network: from structure to function and dynamics. Brain Res Rev 66:5–15Google Scholar
  22. 22.
    D’Angelo E, De Zeeuw CI (2009) Timing and plasticity in the cerebellum: focus on the granular layer. Trends Neurosci 32:30–40Google Scholar
  23. 23.
    D’Angelo E, Koekkoek SKE, Lombardo P, Solinas S, Ros E, Garrido J, Schonewille M, De Zeeuw CI (2009) Timing in the cerebellum: oscillations and resonance in the granular layer. Neuroscience 162:805–15Google Scholar
  24. 24.
    Eccles JC (1982) The initiation of voluntary movements by the supplementary motor area. Arch Psychiatr Nervenkr 231:423–441Google Scholar
  25. 25.
    Horne MK, Butler EG (1995) The role of the cerebello-thalamo-cortical pathway in skilled movement. Prog Neurobiol 46:199–213Google Scholar
  26. 26.
    Prestori F, Rossi P, Bearzatto B, Lainé J, Necchi D, Diwakar S, Schiffmann SN, Axelrad H, D’Angelo E (2008) Altered neuron excitability and synaptic plasticity in the cerebellar granular layer of juvenile prion protein knock-out mice with impaired motor control. J Neurosci. doi: 10.1523/JNEUROSCI.0409-08.2008
  27. 27.
    Goldfarb M, Schoorlemmer J, Williams A, et al (2007) Fibroblast growth factor homologous factors control neuronal excitability through modulation of voltage-gated sodium channels. Neuron 55:449–463Google Scholar
  28. 28.
    Bower JM, Woolston DC (1983) Congruence of spatial organization of tactile projections to granule cell and Purkinje cell layers of cerebellar hemispheres of the albino rat: vertical organization of cerebellar cortex. J Neurophysiol 49:745–66Google Scholar
  29. 29.
    Maex R, Vos B, Ã EDES, Volny-Luraghi a, Vosdagger B, De Schutter E (2002) Peripheral stimuli excite coronal beams of Golgi cells in rat cerebellar cortex. Neuroscience 113:363–73Google Scholar
  30. 30.
    Carrillo RR, Ros E, Boucheny C, Coenen OJ-MD (2008) A real-time spiking cerebellum model for learning robot control. Biosystems 94:18–27Google Scholar
  31. 31.
    Memmesheimer RM, Rubin R, Ölveczky B, Sompolinsky H (2014) Learning Precisely Timed Spikes. Neuron 82:925–938Google Scholar
  32. 32.
    Carrillo RR, Ros E, Tolu S, Nieus T, D’Angelo E (2008) Event-driven simulation of cerebellar granule cells. Biosystems 94:10–17Google Scholar
  33. 33.
    Gamez D, Fidjeland AK, Lazdins E (2012) iSpike: a spiking neural interface for the iCub robot. Bioinspir Biomim 7:25008Google Scholar
  34. 34.
    Medini C, Vijayan A, Zacharia RM, Rajagopal LP, Nair B, Diwakar S (2015) Spike Encoding for Pattern Recognition: Comparing Cerebellum Granular Layer Encoding and BSA algorithms. In: Adv. Comput. Commun. Informatics (ICACCI), 2015 Int. Conf. IEEE, Kochi, pp 1619–1625Google Scholar
  35. 35.
    Vijayan A, Medini C, Palolithazhe A, et al (2015) Modeling Pattern Abstraction in Cerebellum and Estimation of Optimal Storage Capacity. In: Fourth Int. Conf. Adv. Comput. Commun. Informatics. IEEE, Kochi, New York, USA, pp 335–347Google Scholar
  36. 36.
    Burke RE (2007) Sir Charles Sherrington’s the integrative action of the nervous system: a centenary appreciation. Brain 130:887–94Google Scholar
  37. 37.
    Ghez C, Hening W, Gordon J (1991) Organization of voluntary movement. Curr Opin Neurobiol 1:664–671Google Scholar
  38. 38.
    Mehring C, Rickert J, Vaadia E, Cardosa de Oliveira S, Aertsen A, Rotter S (2003) Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci 6:1253–4Google Scholar
  39. 39.
    Schaal S (2002) Arm and Hand Movement Control. 110–113Google Scholar
  40. 40.
    Hemminger S (2010) Linking Error, Passage of Time, the Cerebellum and the Primary Motor Cortex to the Multiple Timescales of Motor Memory By.Google Scholar
  41. 41.
    Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9:718–727Google Scholar
  42. 42.
    Gomi H, Kawato M (1996) Equilibrium-Point Control Hypothesis Examined by Measured Arm Stiffness During Multijoint Movement. Science (80-) 272:117–120Google Scholar
  43. 43.
    Snider RS, Stowell A (1944) Receiving Areas of the Tactile, Auditory, and Visual Systems in the Cerebellum. J Neurophysiol 7:331–357Google Scholar
  44. 44.
    Azizi SA, Woodward DJ (1990) Interactions of visual and auditory mossy fiber inputs in the paraflocculus of the rat: a gating action of multimodal inputs. Brain Res 533:255–62Google Scholar
  45. 45.
    Gao J-H, Parsons LM, Bower JM, Xiong J, Li J, Fox PT (1996) Cerebellum Implicated in Sensory Acquisition and Discrimination Rather Than Motor Control. Science (80-) 272:545–547Google Scholar
  46. 46.
    Eccles JC, Ito M, Szentágothai J (1967) The Cerebellum as a Neuronal Machine. doi: 10.1007/978-3-662-13147-3
  47. 47.
    Morissette J, Bower JM (1996) Contribution of somatosensory cortex to responses in the rat cerebellar granule cell layer following peripheral tactile stimulation. Exp brain Res 109:240–250Google Scholar
  48. 48.
    Mapelli J, D’Angelo E (2007) The spatial organization of long-term synaptic plasticity at the input stage of cerebellum. J Neurosci 27:1285–96Google Scholar
  49. 49.
    Roggeri L, Rivieccio B, Rossi P, D’Angelo E (2008) Tactile stimulation evokes long-term synaptic plasticity in the granular layer of cerebellum. J Neurosci 28:6354–9Google Scholar
  50. 50.
    Diwakar S, Lombardo P, Solinas S, Naldi G, D’Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One 6:e21928Google Scholar
  51. 51.
    Parasuram H, Nair B, Naldi G, D’Angelo E, Diwakar S (2015) Exploiting point source approximation on detailed neuronal models to reconstruct single neuron electric field and population LFP. In: 2015 Int. Jt. Conf. Neural Networks. IEEE, pp 1–7Google Scholar
  52. 52.
    Reimann MW, Anastassiou CA, Perin R, Hill SL, Markram H, Koch C (2013) A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79:375–90Google Scholar
  53. 53.
    Diwakar S, Lombardo P, Solinas S, Naldi G, D’Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One 6:e21928Google Scholar
  54. 54.
    Courtemanche R, Robinson JC, Aponte DI (2013) Linking oscillations in cerebellar circuits. Front Neural Circuits 7:125Google Scholar
  55. 55.
    Einevoll GT, Kayser C, Logothetis NK, Panzeri S (2013) Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat Rev Neurosci 14:770–85Google Scholar
  56. 56.
    Solinas S, Nieus T, D’Angelo E (2010) A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties. Front Cell Neurosci 4:12Google Scholar
  57. 57.
    Medini C, Nair B, D’Angelo E, Naldi G, Diwakar S (2012) Modeling spike-train processing in the cerebellum granular layer and changes in plasticity reveal single neuron effects in neural ensembles. Comput Intell Neurosci 2012:359529Google Scholar
  58. 58.
    Courtemanche R, Chabaud P, Lamarre Y (2009) Synchronization in primate cerebellar granule cell layer local field potentials: basic anisotropy and dynamic changes during active expectancy. Front Cell Neurosci 3:6Google Scholar
  59. 59.
    Bower JM, Woolston DC (1983) Congruence of spatial organization of tactile projections to granule cell and Purkinje cell layers of cerebellar hemispheres of the albino rat: vertical organization of cerebellar cortex. J Neurophysiol 49:745–766Google Scholar
  60. 60.
    Mitzdorf U (1985) Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol Rev 65:37–100Google Scholar
  61. 61.
    Parasuram H, Nair B, D’Angelo E, Hines M, Naldi G, Diwakar S (2016) Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim. Front Comput Neurosci 10:65Google Scholar
  62. 62.
    Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci U S A 105:3593–3598Google Scholar
  63. 63.
    La Camera G, Rauch A, Lüscher H-R, Senn W, Fusi S (2004) Minimal models of adapted neuronal response to in vivo-like input currents. Neural Comput 16:2101–2124Google Scholar
  64. 64.
    Yoosef A, Rajendran AG, Nair B, Diwakar S (2014) Parallelization of Cerebellar Granular Layer Circuitry Model for Physiological Predictions. Proc. Int. Symp. Transl. Neurosci. {&} XXXII Annu. Conf. Indian Acad. Neurosci.Google Scholar
  65. 65.
    Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94:3637–3642Google Scholar
  66. 66.
    Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572Google Scholar
  67. 67.
    Naud R, Marcille N, Clopath C, Gerstner W (2008) Firing patterns in the adaptive exponential integrate-and-fire model. Biol Cybern 99:335–347Google Scholar
  68. 68.
    Medini C, Vijayan A, D’Angelo E, Nair B, Diwakar S (2014) Computationally Efficient Biorealistic Reconstructions of Cerebellar Neuron Spiking Patterns. Int Conf Interdiscip Adv Appl Comput - ICONIAAC ’14 1–6Google Scholar
  69. 69.
    Rossant C, Goodman DFM, Fontaine B, Platkiewicz J, Magnusson AK, Brette R (2011) Fitting neuron models to spike trains. Front Neurosci 5:9Google Scholar
  70. 70.
    D’Angelo E, Nieus T, Maffei a, Armano S, Rossi P, Taglietti V, Fontana a, Naldi G (2001) Theta-frequency bursting and resonance in cerebellar granule cells: experimental evidence and modeling of a slow k + -dependent mechanism. J Neurosci 21:759–70Google Scholar
  71. 71.
    Rancz EA, Ishikawa T, Duguid I, Chadderton P, Mahon S, Häusser M (2007) High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons. Nature 450:1245–8Google Scholar
  72. 72.
    Maex R, Schutter E De (1998) Synchronization of golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer. J Neurophysiol 80:2521–2537Google Scholar
  73. 73.
    Vos BP, Maex R, Volny-Luraghi A, Schutter E De (1999) Parallel fibers synchronize spontaneous activity in cerebellar Golgi cells. J Neurosci 19:RC6Google Scholar
  74. 74.
    Prestori F, Person AL, D’Angelo E, Solinas S, Mapelli J, Gandolfi D, Mapelli L (2013) The cerebellar Golgi cell and spatiotemporal organization of granular layer activity. Front Neural Circuits 7:93Google Scholar
  75. 75.
    Diwakar S, Magistretti J, Goldfarb M, Naldi G, D’Angelo E (2009) Axonal Na + channels ensure fast spike activation and back-propagation in cerebellar granule cells. J Neurophysiol 101:519–532Google Scholar
  76. 76.
    Solinas S, Forti L, Cesana E, Mapelli J, Schutter E De, Angelo ED (2007) Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar golgi cells. doi: 10.3389/neuro.03/002.2007
  77. 77.
    Solinas S, Nieus T, D’Angelo E (2010) A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties. Front Cell Neurosci 4:12Google Scholar
  78. 78.
    Parasuram H, Nair B, Naldi G, Angelo ED, Diwakar S, D’Angelo E (2011) A modeling based study on the origin and nature of evoked post-synaptic local field potentials in granular layer. J Physiol Paris 105:71–82Google Scholar
  79. 79.
    Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM (2004) ModelDB: A Database to Support Computational Neuroscience. J Comput Neurosci 17:7–11Google Scholar
  80. 80.
    Shannon C (1948) A Mathematical Theory of Communication. Bell Syst Tech J 27:379–423Google Scholar
  81. 81.
    Brasselet R, Johansson RS, Arleo A (2011) Quantifying neurotransmission reliability through metrics-based information analysis. Neural Comput 23:852–81Google Scholar
  82. 82.
    Arleo A, Nieus T, Bezzi M, D’Errico A (2010) How synaptic release probability shapes neuronal transmission: Information-theoretic analysis in a cerebellar granule cell. Neural …Google Scholar
  83. 83.
    Nicholson C, Llinas R (1971) Field potentials in the alligator cerebellum and theory of their relationship to Purkinje cell dendritic spikes. J Neurophysiol 34:509–531Google Scholar
  84. 84.
    D’Angelo E (2011) Neural circuits of the cerebellum: hypothesis for function. J Integr Neurosci 10:317–52Google Scholar
  85. 85.
    Chadderton P, Margrie TW, Häusser M (2004) Integration of quanta in cerebellar granule cells during sensory processing. Nature 428:856–60Google Scholar
  86. 86.
    Reinagel P, Reid RC (2000) Temporal coding of visual information in the thalamus. J Neurosci 20:5392–5400Google Scholar
  87. 87.
    Rieke F, Warland D, De Ruyter Van Steveninck R, Bialek W (1997) Spikes: Exploring the Neural Code. MIT Press 20:xvi, 395Google Scholar
  88. 88.
    Ghosh-Dastidar S, Adeli H (2007) Improved Spiking Neural Networks for EEG Classification and Epilepsy and Seizure Detection. Integr Comput Aided Eng 14:187–212Google Scholar
  89. 89.
    McKennoch S, Liu DLD, Bushnell LG (2006) Fast Modifications of the SpikeProp Algorithm. 2006 IEEE Int Jt Conf Neural Netw Proc 3970–3977Google Scholar
  90. 90.
    Rosenblatt F (1962) Principles of Neurodynamics.Google Scholar
  91. 91.
    Vijayan A, Nutakki C, Medini C, Singanamala H, Nair B (2013) Classifying Movement Articulation for Robotic Arms via Machine Learning. J Intell Comput 4:123–134Google Scholar
  92. 92.
    Hansel C, Linden DJ (2000) Long-Term Depression of the Cerebellar Climbing Fiber–Purkinje Neuron Synapse. Neuron 26:473–482Google Scholar
  93. 93.
    Clopath C, Nadal JP, Brunel N (2012) Storage of correlated patterns in standard and bistable Purkinje cell models. PLoS Comput Biol 8:1–10Google Scholar
  94. 94.
    Rubin R, Monasson R, Sompolinsky H (2010) Theory of spike timing based neural classifiers. 4Google Scholar
  95. 95.
    Mapelli J, Gandolfi D, D’Angelo E (2010) Combinatorial responses controlled by synaptic inhibition in cerebellum granular layer. J Neurophysiol 103:250–261Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shyam Diwakar
    • 1
    Email author
  • Chaitanya Medini
    • 1
  • Manjusha Nair
    • 1
    • 2
  • Harilal Parasuram
    • 1
  • Asha Vijayan
    • 1
  • Bipin Nair
    • 1
  1. 1.Amrita School of BiotechnologyAmrita Vishwa Vidyapeetham (Amrita University)KollamIndia
  2. 2.Amrita School of EngineeringAmrita Vishwa Vidyapeetham (Amrita University)KollamIndia

Personalised recommendations