Multiscale models of the synapse: a self-modifying memory machine.

Abstract

Event Abstract Back to Event Multiscale models of the synapse: a self-modifying memory machine. Upinder Bhalla1* 1 National Centre for Biological Sciences, Neurobiology, Computational neuroscience and systems biology, India The human brain expresses some 20,000 genes, 100 billion neurons, and around 10 to the power of 15 synapses that connect up the neurons. Purely on a numerical basis, it seems likely that the synaptic connections would be a good place to store the vast amount of information that makes up our memories. There is now a considerable body of experimental data to show that synapses change in an experience-dependent manner, and increasingly point to these modifications as a key cellular basis for memory. This turns out to be a fertile and challenging arena for multiscale modeling and neuroinformatics. Synapses are precisely at the junction of electrical and chemical signaling. Although there are a plethora of models of signaling in memory, they are small pieces in a multidimensional puzzle. Synaptic memory is one of those processes which demand not just signaling models, but multiscale models that encompass neuronal networks, cellular biophysics, structural change, biochemical signaling, protein synthesis, and gene expression. Some of these domains - like biochmical signaling - are well-represented by simulation tools and standards such as SBML. Others - like structural change - have few, mostly incompatible, tools. I will present the process of modeling the synapse across a few of these multiple scales. There are conceptual challenges here, since we are fundamentally trying to understand how to get immensely stable, life-long changes out of a system that can not only reprogram itself, but also rebuild itself. Other challenges are to see how the synapse balances the requirements for fast switching, against long-term stability in the face of biochemical stochasticity. There are interesting couplings across scales, where electrical events have biochemical effects, and vice versa. I suggest that this cross-coupling at the synapse is one of the key systems where the convergence of neuroinformatics tools and standards can make a huge difference. Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010. Presentation Type: Oral Presentation Topic: Keynote speakers Citation: Bhalla U (2010). Multiscale models of the synapse: a self-modifying memory machine.. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 09 Jun 2010; Published Online: 09 Jun 2010. * Correspondence: Upinder Bhalla, National Centre for Biological Sciences, Neurobiology, Computational neuroscience and systems biology, Banglore, India, bhalla@ncbs.res.in Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Upinder Bhalla Google Upinder Bhalla Google Scholar Upinder Bhalla PubMed Upinder Bhalla Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.