Abstract
In this paper, we implemented a virtual laboratory of neurons and synapses via dynamical models on a web-based platform to aid neurophysiology and computational neuroscience education. Online labs are one of the best alternatives to many universities confronting socio-economic issues in maintaining infrastructure for good laboratory practice. The neural network virtual laboratory was implemented using HTML5 and JQuery, which allowed users to access the lab as a browser-based app. The simulator allows reconstructions of population code and biophysics of single neuron firing dynamics and hence will allow experimentalists to explore its use for hypothesis-based predictions. Such tools as educational aids allow an interrelationship of cognitive, social, and teaching presence. We found students could easily reproduce the common voltage and current clamp protocols on such models without significant instructor assistance and the platform was developed to allow further extensions like raster plots, network computations using extensions to code modules. With new technologies, we foresee a potential redesign of the use of such virtual labs for large-scale modeling as teaching and learning tools in blended learning environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Purves, D., Augustine, G.J., Fitzpatrick, D., Hall, W.C., LaMantia, A.-S., McNamara, J.O., Williams, M.S.: Neuroscience. (2004).
Eric Kandel, Thomas Jessell, James Schwartz, Steven Siegelbaum, A.J.H.: Principles of Neural Science, Fifth Edition, http://www.mhprofessional.com/product.php?isbn=0071390111, (2000).
Gerstner, W., Naud, R.: Neuroscience. How good are neuron models? Science. 326, 379–380 (2009).
Gutkin, B., Pinto, D., Ermentrout, B.: Mathematical neuroscience: from neurons to circuits to systems. J Physiol Paris. 97, 209–219 (2003).
Markram, H.: The blue brain project. Nat. Rev. Neurosci. 7, 153–60 (2006).
Hines, M.L., Carnevale, N.T.: The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997).
Ray, S., Deshpande, R., Dudani, N., Bhalla, U.S.: A general biological simulator: the multiscale object oriented simulation environment, MOOSE. BMC Neurosci. 9, P93 (2008).
Bower, J.M., Beeman, D., Hucka, M.: The GENESIS Simulation System, (2003).
Touretzky, D., Albert, M., Daw, N., Ladsariya, A., Bonakdarpour, M.: HHsim: Graphical Hodgkin-Huxley Simulator, http://www.cs.cmu.edu/~dst/HHsim/, (2004).
Diwakar, S., Parasuram, H., Medini, C., Raman, R., Nedungadi, P., Wiertelak, E., Srivastava, S., Achuthan, K., Nair, B.: Complementing Neurophysiology Education for Developing Countries via Cost-Effective Virtual Labs: case Studies and Classroom Scenarios. J. Undergrad. Neurosci. Educ. 12, A130–9 (2014).
Hodgkin, A.L., Huxley, A.F., Katz, B.: Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J Physiol. 116, 424–448 (1952).
RINZEL, J.: Mechanisms for Nonuniform Propagation Along Excitable Cables. Ann. N. Y. Acad. Sci. 591, 51–61 (1990).
Hines, M.L., Carnevale, N.T.: The NEURON simulation environment. Neural Comput. 9, 1179–209 (1997).
Hillle, B.: Ion Channels of Excitable Membranes, http://www.sinauer.com/ion-channels-of-excitable-membranes.html, (2001).
D’Angelo, E., Filippi, G. De, Rossi, P., Taglietti, V.: Synaptic excitation of individual rat cerebellar granule cells in situ: evidence for the role of NMDA receptors. J Physiol. 484 (Pt 2, 397–413 (1995).
D’Angelo, E., De Filippi, G., Rossi, P., Taglietti, V., Filippi, G. De: Ionic mechanism of electroresponsiveness in cerebellar granule cells implicates the action of a persistent sodium current. J. Neurophysiol. 80, 493–503 (1998).
Hodgkin, A.L., Huxley, A.F.: Quantitative description of nerve current. E. J. Physiol. 117, 500–544 (1952).
Dayan, P., Abbot, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press (2005).
Medini, C., Nair, B., D’Angelo, E., Naldi, G., Diwakar, S.: Modeling spike-train processing in the cerebellum granular layer and changes in plasticity reveal single neuron effects in neural ensembles. Comput. Intell. Neurosci. 2012, 359529 (2012).
Anderson, B., Bergstrom, L., Herman, D., Matthews, J., McAllister, K., Goregaokar, M., Moffitt, J., Sapin, S.: Experience Report: Developing the Servo Web Browser Engine using Rust. 1–6 (2015).
Acknowledgments
This work derives direction and ideas from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. The authors would like to thank Harilal Parasuram, Chaitanya Medini, Asha Vijayan, Rakhi Radhamani, Priya Chelliah for their contributions in this work. This work was partially funded by the Sakshat project of NME-ICT, Ministry of HRD, Government of India and by research for a cause initiative by Embracing the World, M.A. Math.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Sridharan, A. et al. (2016). Implementing a Web-Based Simulator with Explicit Neuron and Synapse Models to Aid Experimental Neuroscience and Theoretical Biophysics Education. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3589-7_6
Download citation
DOI: https://doi.org/10.1007/978-81-322-3589-7_6
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-3587-3
Online ISBN: 978-81-322-3589-7
eBook Packages: EngineeringEngineering (R0)