Literature DB >> 23042882

Theory and simulation in neuroscience.

Wulfram Gerstner1, Henning Sprekeler, Gustavo Deco.   

Abstract

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.

Mesh:

Year:  2012        PMID: 23042882     DOI: 10.1126/science.1227356

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  32 in total

1.  Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity.

Authors:  Önder Gürcan; Kemal S Türker; Jean-Pierre Mano; Carole Bernon; Oğuz Dikenelli; Pierre Glize
Journal:  J Comput Neurosci       Date:  2013-07-04       Impact factor: 1.621

2.  Sodium and potassium conductances in principal neurons of the mouse piriform cortex: a quantitative description.

Authors:  Kaori Ikeda; Norimitsu Suzuki; John M Bekkers
Journal:  J Physiol       Date:  2018-10-14       Impact factor: 5.182

3.  Perception of successive brief objects as a function of stimulus onset asynchrony: model experiments based on two-stage synchronization of neuronal oscillators.

Authors:  Talis Bachmann; Toomas Kirt
Journal:  Cogn Neurodyn       Date:  2013-03-19       Impact factor: 5.082

Review 4.  Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry.

Authors:  John D Murray; Murat Demirtaş; Alan Anticevic
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2018-07-19

5.  Analytically determining frequency and amplitude of spontaneous alpha oscillation in Jansen's neural mass model using the describing function method.

Authors:  Yao Xu; Chun-Hui Zhang; Ernst Niebur; Jun-Song Wang
Journal:  Chin Phys B       Date:  2018-04       Impact factor: 1.494

6.  Training deep neural density estimators to identify mechanistic models of neural dynamics.

Authors:  Pedro J Gonçalves; Jan-Matthis Lueckmann; Michael Deistler; Marcel Nonnenmacher; Kaan Öcal; Giacomo Bassetto; Chaitanya Chintaluri; William F Podlaski; Sara A Haddad; Tim P Vogels; David S Greenberg; Jakob H Macke
Journal:  Elife       Date:  2020-09-17       Impact factor: 8.140

7.  Latent structure in random sequences drives neural learning toward a rational bias.

Authors:  Yanlong Sun; Randall C O'Reilly; Rajan Bhattacharyya; Jack W Smith; Xun Liu; Hongbin Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2015-03-09       Impact factor: 11.205

8.  Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach.

Authors:  Sobanawartiny Wijeakumar; Joseph P Ambrose; John P Spencer; Rodica Curtu
Journal:  J Math Psychol       Date:  2016-12-21       Impact factor: 2.223

Review 9.  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 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

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