Literature DB >> 26093844

Questioning the role of sparse coding in the brain.

Anton Spanne1, Henrik Jörntell2.   

Abstract

Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2015        PMID: 26093844     DOI: 10.1016/j.tins.2015.05.005

Source DB:  PubMed          Journal:  Trends Neurosci        ISSN: 0166-2236            Impact factor:   13.837


  30 in total

1.  Diverse tuning underlies sparse activity in layer 2/3 vibrissal cortex of awake mice.

Authors:  Yadollah Ranjbar-Slamloo; Ehsan Arabzadeh
Journal:  J Physiol       Date:  2019-04-16       Impact factor: 5.182

2.  High-velocity stimulation evokes "dense" population response in layer 2/3 vibrissal cortex.

Authors:  Yadollah Ranjbar-Slamloo; Ehsan Arabzadeh
Journal:  J Neurophysiol       Date:  2016-12-21       Impact factor: 2.714

3.  Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.

Authors:  Andrea Giovannucci; Aleksandra Badura; Ben Deverett; Farzaneh Najafi; Talmo D Pereira; Zhenyu Gao; Ilker Ozden; Alexander D Kloth; Eftychios Pnevmatikakis; Liam Paninski; Chris I De Zeeuw; Javier F Medina; Samuel S-H Wang
Journal:  Nat Neurosci       Date:  2017-03-20       Impact factor: 24.884

4.  3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code.

Authors:  Michael Beyeler; Nikil Dutt; Jeffrey L Krichmar
Journal:  J Neurosci       Date:  2016-08-10       Impact factor: 6.167

Review 5.  Cerebellar physiology: links between microcircuitry properties and sensorimotor functions.

Authors:  Henrik Jörntell
Journal:  J Physiol       Date:  2016-08-31       Impact factor: 5.182

6.  Optimal Degrees of Synaptic Connectivity.

Authors:  Ashok Litwin-Kumar; Kameron Decker Harris; Richard Axel; Haim Sompolinsky; L F Abbott
Journal:  Neuron       Date:  2017-02-16       Impact factor: 17.173

7.  Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity.

Authors:  Jesse I Gilmer; Abigail L Person
Journal:  J Neurosci       Date:  2017-11-08       Impact factor: 6.167

Review 8.  Memory allocation mechanisms underlie memory linking across time.

Authors:  M Sehgal; M Zhou; A Lavi; S Huang; Y Zhou; A J Silva
Journal:  Neurobiol Learn Mem       Date:  2018-02-26       Impact factor: 2.877

Review 9.  Computational Principles of Supervised Learning in the Cerebellum.

Authors:  Jennifer L Raymond; Javier F Medina
Journal:  Annu Rev Neurosci       Date:  2018-07-08       Impact factor: 12.449

Review 10.  Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective.

Authors:  John H Krystal; Alan Anticevic; Genevieve J Yang; George Dragoi; Naomi R Driesen; Xiao-Jing Wang; John D Murray
Journal:  Biol Psychiatry       Date:  2017-01-13       Impact factor: 13.382

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