MATHEMATICAL SIMULATION

Membrane properties and intracellular Ca2+ dynamics derived from physiological experiments are represented in a mathematical form, and a realistic simulation of neuronal excitability is reconstructed. Neuronal models allow theoretical investigation of membrane biophysics and of neuronal networks.

Multi-compartmental models of Granule and Golgi cell are now available and modeled with high morphological and physiological details.

 

Example of network topology:

modellibelli

Elements of the network. (i) The whole network: the granular layer network was simulated as a cube with edge length 100 μm. It contained 4096 GrCs (blue dots), 27 GoCs (green spheres), and 315 glomeruli (red and cyan dots). 

 

Recently a bio-realistic mathematical modeling of the unipolar brush cells (UBC), an excitatory interneuron of the cerebellum, has been construced using the NEURON-PYTHON simulator (NEURON version 7.3; PYTHON version 2.7.1.).

 

  •  Purkinje Cell – movie

modello purkinje

Click on the image (left) to see the whole movie of a Purkinje model

 

 

 

 

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Relevant pubblications:

  • Thierry Nieus, Elisabetta Sola, Jonathan Mapelli, Elena Saftenku, Paola Rossi, Egidio D’Angelo. (2006) Regulation of repetitive neurotransmission and firing by release probability at the input stage of cerebellum: experimental observations and theoretical predictions on the role of LTP. J Neurophysiol 95, pp. 686-699.
  • Sergio M. Solinas, Lia Forti, Elisabetta Cesana, Jonathan Mapelli, Erik De Schutter and Egidio D’Angelo (2007) Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. Front. Cell. Neurosci. 1:2.
  • R Carrillo, E Ros, S Tolu, T Nieus, E D’Angelo. (2008) Event-driven simulation of cerebellar granule cells, Biosystems. Oct-Nov, 94(1-2), pp. 10-7. Epub 2008 Jun 20.
  • Shyam Diwakar, Jacopo Magistretti, Mitchell Goldfarb, Giovanni Naldi, Egidio D’Angelo. (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells. J Neurophysiol. 2009 Feb;101, pp. 519-32. Epub 2008 Dec 10.
  • E. D’Angelo, S.K.E. Koekkoek, P. Lombardo, S. Solinas, E. Ros, J. Garrido, M. Schonewille and C.I. De Zeeuw (2009) Timing in the cerebellum: oscillations and resonance in the granular layer. Neuroscience Volume 162, Issue 3, 1 September 2009, Pages 805-815.
  • Sergio Solinas, Thierry Nieus, and Egidio D’Angelo (2010). A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties. Frontiers in Cellular Neuroscience. April 2010, volume 4, article 12.
  • Arleo A, Nieus T, Bezzi M, D’Errico A, D’Angelo E, Coenen OJ.Neural Comput. (2010). How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell. Neural Comput. 2010 Aug;22(8):2031-58.
  • Parasuram H, Nair B, Naldi G, D’Angelo E, Diwakar S. (2011). A modeling based study on the origin and nature of evoked post-synaptic local field potentials in granular layer. J Physiol Paris. 2011 Aug 6.
  • 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. 2011;6(7).
  • E. D’Angelo, S. Solinas. Realistic Modeling of Large-Scale Networks: Spatio-temporal Dynamics and Long-Term Synaptic Plasticity in the Cerebellum. Advances in Computational Intelligence. Lecture Notes in Computer Science Volume 6691, 2011, pp 547-553.
  • C. Medini, B. Nair, E. D’Angelo, G. Naldi, and S. Diwakar. (2012) Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles. Computational Intelligence and Neuroscience. Vol. 2012, Article ID 359529, 17 pages.
  • J. A Garrido, E. Ros, E. D‘Angelo. Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study. Frontiers in computational neuroscience. May2013 – Volume7 – Article 64.
  • J.A. Garrido Alcazar, N.R. Luque, E. D’Angelo. Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation. Frontiers in Neural Circuits. 09 October 2013.
  • E. D’Angelo, S. Solinas, J. Garrido, C. Casellato, A. Pedrocchi, J. Mapelli, D. Gandolfi, F. Prestori. Realistic modeling of neurons and networks: towards brain simulation. Functional Neurology 2013; 28(3): 153-166.
  •  S. Subramaniyam, S. Solinas, P. Perin, F. Locatelli, S.
    Masetto, E. D’Angelo. Computational modeling predicts the ionic mechanism of late-onset responses in Unipolar Brush Cells. Frontiers in Cellular Neuroscience. Front. Cell. Neurosci., 20 August 2014. doi:10.3389/fncel.2014.00237. 

– Back to the other techniques

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