These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

125 related articles for article (PubMed ID: 35727818)

  • 1. Towards a more general understanding of the algorithmic utility of recurrent connections.
    Larsen BW; Druckmann S
    PLoS Comput Biol; 2022 Jun; 18(6):e1010227. PubMed ID: 35727818
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Neural Classifiers with Limited Connectivity and Recurrent Readouts.
    Kushnir L; Fusi S
    J Neurosci; 2018 Nov; 38(46):9900-9924. PubMed ID: 30249794
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.
    Spoerer CJ; Kietzmann TC; Mehrer J; Charest I; Kriegeskorte N
    PLoS Comput Biol; 2020 Oct; 16(10):e1008215. PubMed ID: 33006992
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
    Spoerer CJ; McClure P; Kriegeskorte N
    Front Psychol; 2017; 8():1551. PubMed ID: 28955272
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition.
    Nayebi A; Sagastuy-Brena J; Bear DM; Kar K; Kubilius J; Ganguli S; Sussillo D; DiCarlo JJ; Yamins DLK
    Neural Comput; 2022 Jul; 34(8):1652-1675. PubMed ID: 35798321
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.
    Driscoll LN; Shenoy K; Sussillo D
    Nat Neurosci; 2024 Jul; 27(7):1349-1363. PubMed ID: 38982201
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Brain connectivity meets reservoir computing.
    Damicelli F; Hilgetag CC; Goulas A
    PLoS Comput Biol; 2022 Nov; 18(11):e1010639. PubMed ID: 36383563
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Contextual Integration in Cortical and Convolutional Neural Networks.
    Iyer R; Hu B; Mihalas S
    Front Comput Neurosci; 2020; 14():31. PubMed ID: 32390818
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning.
    Alamia A; Gauducheau V; Paisios D; VanRullen R
    Sci Rep; 2020 Dec; 10(1):22172. PubMed ID: 33335190
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Feedforward Approximations to Dynamic Recurrent Network Architectures.
    Muir DR
    Neural Comput; 2018 Feb; 30(2):546-567. PubMed ID: 29162003
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Targeting operational regimes of interest in recurrent neural networks.
    Ekelmans P; Kraynyukova N; Tchumatchenko T
    PLoS Comput Biol; 2023 May; 19(5):e1011097. PubMed ID: 37186668
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification.
    Lippl S; Peters B; Kriegeskorte N
    PLoS One; 2024; 19(3):e0293440. PubMed ID: 38512838
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detecting connectedness.
    Roelfsema PR; Singer W
    Cereb Cortex; 1998; 8(5):385-96. PubMed ID: 9722082
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.
    Kungl AF; Schmitt S; Klähn J; Müller P; Baumbach A; Dold D; Kugele A; Müller E; Koke C; Kleider M; Mauch C; Breitwieser O; Leng L; Gürtler N; Güttler M; Husmann D; Husmann K; Hartel A; Karasenko V; Grübl A; Schemmel J; Meier K; Petrovici MA
    Front Neurosci; 2019; 13():1201. PubMed ID: 31798400
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Canonical circuit computations for computer vision.
    Schmid D; Jarvers C; Neumann H
    Biol Cybern; 2023 Oct; 117(4-5):299-329. PubMed ID: 37306782
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Connectomic constraints on computation in feedforward networks of spiking neurons.
    Ramaswamy V; Banerjee A
    J Comput Neurosci; 2014 Oct; 37(2):209-28. PubMed ID: 24691897
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex.
    Koren V; Emanuel AJ; Panzeri S
    bioRxiv; 2024 Jun; ():. PubMed ID: 38895477
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A no-go theorem for one-layer feedforward networks.
    Giusti C; Itskov V
    Neural Comput; 2014 Nov; 26(11):2527-40. PubMed ID: 25149704
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.
    Song HF; Yang GR; Wang XJ
    PLoS Comput Biol; 2016 Feb; 12(2):e1004792. PubMed ID: 26928718
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The Importance of Lateral Connections in the Parietal Cortex for Generating Motor Plans.
    Asher DE; Oros N; Krichmar JL
    PLoS One; 2015; 10(8):e0134669. PubMed ID: 26252871
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.