Literature DB >> 23799465

Recurrent networks learn to tell time.

Alfonso Renart1.   

Abstract

Mesh:

Year:  2013        PMID: 23799465     DOI: 10.1038/nn.3441

Source DB:  PubMed          Journal:  Nat Neurosci        ISSN: 1097-6256            Impact factor:   24.884


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  12 in total

1.  A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum.

Authors:  M D Mauk; N H Donegan
Journal:  Learn Mem       Date:  1997 May-Jun       Impact factor: 2.460

2.  Retrospective and prospective coding for predicted reward in the sensory thalamus.

Authors:  Y Komura; R Tamura; T Uwano; H Nishijo; K Kaga; T Ono
Journal:  Nature       Date:  2001-08-02       Impact factor: 49.962

3.  Self-organizing neural integrator predicts interval times through climbing activity.

Authors:  Daniel Durstewitz
Journal:  J Neurosci       Date:  2003-06-15       Impact factor: 6.167

Review 4.  Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes.

Authors:  Matthew S Matell; Warren H Meck
Journal:  Brain Res Cogn Brain Res       Date:  2004-10

5.  Chaos in random neural networks.

Authors: 
Journal:  Phys Rev Lett       Date:  1988-07-18       Impact factor: 9.161

6.  Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex.

Authors:  Shigeyoshi Fujisawa; Asohan Amarasingham; Matthew T Harrison; György Buzsáki
Journal:  Nat Neurosci       Date:  2008-05-30       Impact factor: 24.884

7.  Chaos in neuronal networks with balanced excitatory and inhibitory activity.

Authors:  C van Vreeswijk; H Sompolinsky
Journal:  Science       Date:  1996-12-06       Impact factor: 47.728

8.  Generating coherent patterns of activity from chaotic neural networks.

Authors:  David Sussillo; L F Abbott
Journal:  Neuron       Date:  2009-08-27       Impact factor: 17.173

9.  Memory without feedback in a neural network.

Authors:  Mark S Goldman
Journal:  Neuron       Date:  2009-02-26       Impact factor: 17.173

10.  Robust timing and motor patterns by taming chaos in recurrent neural networks.

Authors:  Rodrigo Laje; Dean V Buonomano
Journal:  Nat Neurosci       Date:  2013-05-26       Impact factor: 24.884

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