What are the real challenges for Computational Neuroscience?

Abstract

Event Abstract Back to Event What are the real challenges for Computational Neuroscience? Marc-Oliver Gewaltig1* 1 Honda Research Institute Europe GmbH, Germany The human brain contains some 1011 neurons with some 1015 synaptic connections between them. It is tempting to believe that we can model the brain, once computers are fast enough and computer memory is big enough to store and solve the models for all neurons and their connections. The largest models today have 105 neurons with up to 109 synapses, corresponding to one cubic millimeter of human cortex. These models already require computer clusters, but use simple neuron and synapse models. Larger models merely show what is technically feasible. To model a large neuronal system, we cannot take the best neuron model. Instead, we must use the smallest or fastest model to squeeze our network into the available computer memory. Recent trends in computing technology indicate that faster and more powerful computers will soon be available to a wide group of researchers. The most interesting for scientific computing are: multi-core processors for parallel computing and so-called graphical processing units (GPU), the high-performance engines of 3D graphics cards. These technologies may speed up simulations by orders of magnitude and we may soon be able to simulate big parts of the brain in short times. However, the biggest challenge for computational neuroscience is the complexity of the brain, not its size. Each nerve cell is already so complex that researchers disagree about the appropriate level of description. Even less is known about neural circuits or systems. Conceptual progress in systems neuroscience depends on a concise and powerful notation to develop, analyze, and covey ideas and models of neurons, networks and systems. Today, simulation code is the only reliable source of information about a model. But simulation code cannot replace a formal notation, because it is incomplete and platform. Only with an appropriate formal notation can we cope with the increasing complexity of neural models, because it allows us to formally manipulate, analyze, and enhance our models. Complex models require complex simulation software. Today's simulation code is mostly hand-written for a particular model. But with increasing complexity, it becomes more difficult to validate published results. There are no accepted quality standards to ensure that simulation results indeed describe neuroscientific phenomena rather than errors in the implementation. Researchers must adopt standards and practices long common in software engineering, to keep up with the ever increasing complexity of models, simulation software, and computing hardware. Reviewers must begin to critically review not only the model, but also the simulation methods. Journals must accept that simulation methods must not be banished to the supplementary material section where few are likely to see them. In this talk, I will review recent results and trends in simulation technology for large neural systems and will discuss possible solutions to the challenges posed by the ever increasing complexity of models, simulation software and computing architectures. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Oral Presentation Topic: Workshop Citation: Gewaltig M (2008). What are the real challenges for Computational Neuroscience?. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.150 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: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Marc-Oliver Gewaltig, Honda Research Institute Europe GmbH, Offenbach, Germany, marc-oliver.gewaltig@epfl.ch 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 Marc-Oliver Gewaltig Google Marc-Oliver Gewaltig Google Scholar Marc-Oliver Gewaltig PubMed Marc-Oliver Gewaltig 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.