Literature DB >> 24709593

The use and abuse of large-scale brain models.

Chris Eliasmith1, Oliver Trujillo2.   

Abstract

We provide an overview and comparison of several recent large-scale brain models. In addition to discussing challenges involved with building large neural models, we identify several expected benefits of pursuing such a research program. We argue that these benefits are only likely to be realized if two basic guidelines are made central to the pursuit. The first is that such models need to be intimately tied to behavior. The second is that models, and more importantly their underlying methods, should provide mechanisms for varying the level of simulated detail. Consequently, we express concerns with models that insist on a 'correct' amount of detail while expecting interesting behavior to simply emerge.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 24709593     DOI: 10.1016/j.conb.2013.09.009

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  22 in total

Review 1.  Synapse-type-specific plasticity in local circuits.

Authors:  Rylan S Larsen; P Jesper Sjöström
Journal:  Curr Opin Neurobiol       Date:  2015-08-25       Impact factor: 6.627

2.  A spiking neural model of adaptive arm control.

Authors:  Travis DeWolf; Terrence C Stewart; Jean-Jacques Slotine; Chris Eliasmith
Journal:  Proc Biol Sci       Date:  2016-11-30       Impact factor: 5.349

3.  The receptive field is dead. Long live the receptive field?

Authors:  Adrienne Fairhall
Journal:  Curr Opin Neurobiol       Date:  2014-03-04       Impact factor: 6.627

Review 4.  Is realistic neuronal modeling realistic?

Authors:  Mara Almog; Alon Korngreen
Journal:  J Neurophysiol       Date:  2016-08-17       Impact factor: 2.714

5.  A spiking neural network model of spatial and visual mental imagery.

Authors:  Sean N Riley; Jim Davies
Journal:  Cogn Neurodyn       Date:  2019-12-05       Impact factor: 5.082

Review 6.  Cognitive computational neuroscience.

Authors:  Nikolaus Kriegeskorte; Pamela K Douglas
Journal:  Nat Neurosci       Date:  2018-08-20       Impact factor: 24.884

7.  Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.

Authors:  Olivia Eriksson; Upinder Singh Bhalla; Kim T Blackwell; Sharon M Crook; Daniel Keller; Andrei Kramer; Marja-Leena Linne; Ausra Saudargienė; Rebecca C Wade; Jeanette Hellgren Kotaleski
Journal:  Elife       Date:  2022-07-06       Impact factor: 8.713

Review 8.  On the nature and use of models in network neuroscience.

Authors:  Danielle S Bassett; Perry Zurn; Joshua I Gold
Journal:  Nat Rev Neurosci       Date:  2018-09       Impact factor: 34.870

Review 9.  Genetic variants in Alzheimer disease - molecular and brain network approaches.

Authors:  Chris Gaiteri; Sara Mostafavi; Christopher J Honey; Philip L De Jager; David A Bennett
Journal:  Nat Rev Neurol       Date:  2016-06-10       Impact factor: 42.937

Review 10.  A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain.

Authors:  Maria I Falcon; Viktor Jirsa; Ana Solodkin
Journal:  Curr Opin Neurol       Date:  2016-08       Impact factor: 5.710

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.