Literature DB >> 3045969

Computational neuroscience.

T J Sejnowski1, C Koch, P S Churchland.   

Abstract

The ultimate aim of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. This goal is not new, but much has changed in the last decade. More is known now about the brain because of advances in neuroscience, more computing power is available for performing realistic simulations of neural systems, and new insights are available from the study of simplifying models of large networks of neurons. Brain models are being used to connect the microscopic level accessible by molecular and cellular techniques with the systems level accessible by the study of behavior.

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Year:  1988        PMID: 3045969     DOI: 10.1126/science.3045969

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


  47 in total

1.  Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end?

Authors:  P Veng-Pedersen; N B Modi
Journal:  J Pharmacokinet Biopharm       Date:  1992-08

2.  From circuits to behavior: a bridge too far?

Authors:  Matteo Carandini
Journal:  Nat Neurosci       Date:  2012-03-27       Impact factor: 24.884

Review 3.  On some two-way barriers between models and mechanisms.

Authors:  W R Uttal
Journal:  Percept Psychophys       Date:  1990-08

4.  Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.

Authors:  G Reibnegger; G Weiss; G Werner-Felmayer; G Judmaier; H Wachter
Journal:  Proc Natl Acad Sci U S A       Date:  1991-12-15       Impact factor: 11.205

5.  A computational model of the vertical anatomical organization of primary visual cortex.

Authors:  E Thomas; P Patton; R E Wyatt
Journal:  Biol Cybern       Date:  1991       Impact factor: 2.086

6.  Reviewing multi-disciplinary papers: a challenge in neuroscience?

Authors:  Erik De Schutter
Journal:  Neuroinformatics       Date:  2008-10-21

Review 7.  The benefits of noise in neural systems: bridging theory and experiment.

Authors:  Mark D McDonnell; Lawrence M Ward
Journal:  Nat Rev Neurosci       Date:  2011-06-20       Impact factor: 34.870

Review 8.  Using theoretical models to analyse neural development.

Authors:  Arjen van Ooyen
Journal:  Nat Rev Neurosci       Date:  2011-05-18       Impact factor: 34.870

Review 9.  A roadmap to integrate astrocytes into Systems Neuroscience.

Authors:  Ksenia V Kastanenka; Rubén Moreno-Bote; Maurizio De Pittà; Gertrudis Perea; Abel Eraso-Pichot; Roser Masgrau; Kira E Poskanzer; Elena Galea
Journal:  Glia       Date:  2019-05-06       Impact factor: 7.452

10.  Decoding the brain's algorithm for categorization from its neural implementation.

Authors:  Michael L Mack; Alison R Preston; Bradley C Love
Journal:  Curr Biol       Date:  2013-10-03       Impact factor: 10.834

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