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Unsupervised neural network models of the ventral visual stream

Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins
2021 Proceedings of the National Academy of Sciences of the United States of America  
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream.  ...  of models derived using today's best supervised methods and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream.  ...  Unsupervised neural network models of the ventral visual stream Fig. 4 . 4 Behavioral consistency and semisupervised learning.  ... 
doi:10.1073/pnas.2014196118 pmid:33431673 fatcat:remyumahdze3jopzuxbz5rudhu

Unsupervised Neural Network Models of the Ventral Visual Stream [article]

Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael Frank, James DiCarlo, Daniel Yamins
2020 bioRxiv   pre-print
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream.  ...  of models derived using today's best supervised methods, and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream.  ...  image-evoked neural responses in multiple visual cortical areas along the primate ventral 233 233 visual pathway, equaling the predictive power of supervised models.  ... 
doi:10.1101/2020.06.16.155556 fatcat:qdya2vcpxveyrpxeuso6zy56ju

Titelei/Inhaltsverzeichnis [chapter]

Thomas Guthier
2016 Visual Motion Processing  
For the ventral stream the VNMF algorithm learns distict gradient structures, resembling edges and corners. All these patterns represent simple cells of the feed-forward hierachy.  ...  In the first layer of the dorsal stream, the VNMF is modified to solve the optical flow estimation problem.  ...  For the ventral stream the VNMF algorithm learns distict gradient structures, resembling edges and corners. All these patterns represent simple cells of the feed-forward hierachy.  ... 
doi:10.51202/9783186251084-i fatcat:5rbzl4l36bberpde2y5vvsrct4

Unsupervised Models of Mouse Visual Cortex [article]

Aran Nayebi, Nathan C. L. Kong, Chengxu Zhuang, Justin L. Gardner, Anthony M. Norcia, Daniel L. K. Yamins
2021 bioRxiv   pre-print
Task-optimized deep convolutional neural networks are the most quantitatively accurate models of the primate ventral visual stream.  ...  Moreover, these models' neural predictivity significantly surpasses those of supervised, deep architectures that are known to correspond well to the primate ventral visual stream.  ...  Hermann, and Akshay Jagadeesh for helpful discussions, and Eshed Margalit for helpful feedback on the manuscript. N.C.L.  ... 
doi:10.1101/2021.06.16.448730 fatcat:gvmndognfjdwfdwxrqrpz22rcu

Encoding Voxels with Deep Learning

P. Wang, V. Malave, B. Cipollini
2015 Journal of Neuroscience  
neural network model mapped onto the ventral visual stream.  ...  ., 2014) and are inspired by the types of neural computations and feedforward component of the ventral visual stream.  ... 
doi:10.1523/jneurosci.3454-15.2015 pmid:26631460 fatcat:t453xwq7tzb5rbu3bg5r76f2fu

The functional neuroanatomy of face perception: from brain measurements to deep neural networks

Kalanit Grill-Spector, Kevin S. Weiner, Jesse Gomez, Anthony Stigliani, Vaidehi S. Natu
2018 Interface Focus  
Filters in each layer are applied [18] , though some models begin in the retina [13] . (b) The ventral stream visual hierarchy of the human ventral face network.  ...  We are hopeful that these neural features will be incorporated into modern DNNs to generate a new class of neurally accurate computational models of the ventral stream and specifically of the face network  ... 
doi:10.1098/rsfs.2018.0013 pmid:29951193 pmcid:PMC6015811 fatcat:sshfwwcmljf5ro2rnbpylwqrxm

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models [article]

Seyed-Mahdi Khaligh-Razavi, Linda Henriksson, Kendrick Kay, Nikolaus Kriegeskorte
2014 biorxiv/medrxiv   pre-print
However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network, and mixing of its feature set was essential for  ...  The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas.  ...  Acknowledgments We would like to thank Katherine Storrs for helpful comments on the manuscript. We would also like to thank all those who shared their model implementations with us.  ... 
doi:10.1101/009936 fatcat:kdzlfn5f2jax7d73vcot4kxvfa

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models

Seyed-Mahdi Khaligh-Razavi, Linda Henriksson, Kendrick Kay, Nikolaus Kriegeskorte
2017 Journal of Mathematical Psychology  
However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for  ...  The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas.  ...  Acknowledgments We would like to thank Katherine Storrs for helpful comments on the manuscript. We would also like to thank all those who shared their model implementations with us.  ... 
doi:10.1016/j.jmp.2016.10.007 pmid:28298702 pmcid:PMC5341758 fatcat:6qh6jmscxfcxnmzzsgxqozm5ea

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Yalda Mohsenzadeh, Caitlin Mullin, Benjamin Lahner, Aude Oliva
2020 Scientific Reports  
ventral visual cortex.  ...  The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural  ...  Recently, a class of computational models, termed deep convolutional neural networks (DCNNs), inspired by the hierarchical architectures of ventral visual streams demonstrated striking similarities with  ... 
doi:10.1038/s41598-020-61409-0 pmid:32170209 fatcat:xul6cplv7zhbxfzrsgegbid554

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks [article]

Yalda Mohsenzadeh, Caitlin Mullin, Benjamin Lahner, Aude Oliva
2020 bioRxiv   pre-print
ventral visual cortex.  ...  The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural  ...  ventral visual streams 62 demonstrated striking similarities with the cascade of processing stages in the human 63 visual system 17-25 .  ... 
doi:10.1101/2020.02.19.956748 fatcat:mlo7rzlq7zhlnf43ulgnx442aq

A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream

Jingwei Li, Chi Zhang, Linyuan Wang, Penghui Ding, Lulu Hu, Bin Yan, Li Tong
2021 Brain Sciences  
From the view of unsupervised learning mechanisms, this paper utilized a pre-trained neural network to construct a visual encoding model based on contrastive self-supervised learning for the ventral visual  ...  Visual encoding models are important computational models for understanding how information is processed along the visual stream.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/brainsci11081004 fatcat:w4uoaozviffnpfiqh7u3xgam4y

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

U. Guclu, M. A. J. van Gerven
2015 Journal of Neuroscience  
Using deep convolutional neural networks, we can now quantitatively demonstrate that there is indeed an explicit gradient for feature complexity in the ventral pathway of the human brain.  ...  Finally, it is shown that deep convolutional neural networks allow decoding of representations in the human brain at a previously unattainable degree of accuracy, providing a more sensitive window into  ...  confirm the existence of a gradient in complexity of neural representations across visual areas on the main afferent pathway of the ventral stream.  ... 
doi:10.1523/jneurosci.5023-14.2015 pmid:26157000 fatcat:earigmu2irhkbiprv7pnsar7ze

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

Grace Lindsay
2020 Journal of Cognitive Neuroscience  
They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks.  ...  Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision.  ...  Much of the use of CNNs as a model of visual processing has focused on the ventral visual stream.  ... 
doi:10.1162/jocn_a_01544 pmid:32027584 fatcat:3ntotmq2hrcdxjljdktmdegxpe

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Bardia Yousefi, Chu Kiong Loo
2019 Electronics  
Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system.  ...  The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented.  ...  One of the famous biologically inspired model [26, 28] has proposed two independent pathways, which model the dorsal and ventral processing streams in the mammalian visual system.  ... 
doi:10.3390/electronics8101169 fatcat:v6cuchycurd2xg7cx545dwujy4

Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream [article]

Franziska Geiger, Martin Schrimpf, Tiago Marques, James DiCarlo
2020 bioRxiv   pre-print
After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition.  ...  Third, we find that, by training only ~5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream.  ...  This work was supported the Prosa scholarship of LMU, foundation of the University of Augsburg, the Foundation "Rohde Stiftung" (F.G.), the Massachusetts Institute of Technology Shoemaker Fellowship (M.S  ... 
doi:10.1101/2020.06.08.140111 fatcat:ysstg34kjzbp5nw7anyieu2p7u
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