Literature DB >> 24492069

Single neuron dynamics and computation.

Nicolas Brunel1, Vincent Hakim2, Magnus J E Richardson3.   

Abstract

At the single neuron level, information processing involves the transformation of input spike trains into an appropriate output spike train. Building upon the classical view of a neuron as a threshold device, models have been developed in recent years that take into account the diverse electrophysiological make-up of neurons and accurately describe their input-output relations. Here, we review these recent advances and survey the computational roles that they have uncovered for various electrophysiological properties, for dendritic arbor anatomy as well as for short-term synaptic plasticity.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2014        PMID: 24492069     DOI: 10.1016/j.conb.2014.01.005

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


  19 in total

1.  Model reduction of strong-weak neurons.

Authors:  Bosen Du; Danny Sorensen; Steven J Cox
Journal:  Front Comput Neurosci       Date:  2014-12-16       Impact factor: 2.380

2.  Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality.

Authors:  James K Johnson; Nathaniel C Wright; Jì Xià; Ralf Wessel
Journal:  J Neurosci       Date:  2019-04-05       Impact factor: 6.167

3.  T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells.

Authors:  Hermann Cuntz; Peter Jedlicka; Marcel Beining; Lucas Alberto Mongiat; Stephan Wolfgang Schwarzacher
Journal:  Elife       Date:  2017-11-22       Impact factor: 8.140

4.  Distinct current modules shape cellular dynamics in model neurons.

Authors:  Adel Alturki; Feng Feng; Ajay Nair; Vinay Guntu; Satish S Nair
Journal:  Neuroscience       Date:  2016-08-13       Impact factor: 3.590

5.  Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.

Authors:  Ueli Rutishauser; Jean-Jacques Slotine; Rodney J Douglas
Journal:  Neural Comput       Date:  2018-03-22       Impact factor: 2.026

Review 6.  Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance.

Authors:  Julijana Gjorgjieva; Guillaume Drion; Eve Marder
Journal:  Curr Opin Neurobiol       Date:  2016-01-15       Impact factor: 6.627

7.  Spike generation estimated from stationary spike trains in a variety of neurons in vivo.

Authors:  Anton Spanne; Pontus Geborek; Fredrik Bengtsson; Henrik Jörntell
Journal:  Front Cell Neurosci       Date:  2014-07-25       Impact factor: 5.505

8.  Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

Authors:  Saeed Afshar; Libin George; Jonathan Tapson; André van Schaik; Tara J Hamilton
Journal:  Front Neurosci       Date:  2014-11-25       Impact factor: 4.677

9.  High-Degree Neurons Feed Cortical Computations.

Authors:  Nicholas M Timme; Shinya Ito; Maxym Myroshnychenko; Sunny Nigam; Masanori Shimono; Fang-Chin Yeh; Pawel Hottowy; Alan M Litke; John M Beggs
Journal:  PLoS Comput Biol       Date:  2016-05-09       Impact factor: 4.475

10.  A Pruning Neural Network Model in Credit Classification Analysis.

Authors:  Yajiao Tang; Junkai Ji; Shangce Gao; Hongwei Dai; Yang Yu; Yuki Todo
Journal:  Comput Intell Neurosci       Date:  2018-02-11
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.