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Examining Representational Similarity in ConvNets and the Primate Visual Cortex [article]

Abhimanyu Dubey, Jayadeva, Sumeet Agarwal
2016 arXiv   pre-print
We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate  ...  visual cortex.  ...  This can help understand whether sparsity and increased network depth create representations which are closer to representations employed by the primate visual cortex, and also validate the hypothesis  ... 
arXiv:1609.03529v1 fatcat:igklyrtrgbf3xjt6j7oresjhj4

Gradient-free activation maximization for identifying effective stimuli [article]

Will Xiao, Gabriel Kreiman
2019 arXiv   pre-print
visual cortex (Ponce et al., 2019).  ...  We show that XDream is applicable across network layers, architectures, and training sets; examine design choices in the algorithm; and provide practical guides for choosing hyperparameters in the algorithm  ...  Because ConvNets have been shown to share similar representations with each other (Morcos et al., 2018) as well as with the primate ventral visual stream (Yamins et al., 2014) , one hypothesis is that  ... 
arXiv:1905.00378v1 fatcat:wlg5ttozojayvbferbit7dsfiy

Spectral classification using convolutional neural networks [article]

Pavel Hála
2014 arXiv   pre-print
Author developed several scripts and C programs for datasets preparation, preprocessing and postprocessing of the data.  ...  There is a great need for accurate and autonomous spectral classification methods in astrophysics.  ...  Visual cortex From the LGN, the signal goes into the primary visual cortex (V1 in figure 3 .3). The primary visual cortex is an important and most studied area of the human visual system.  ... 
arXiv:1412.8341v1 fatcat:vev56lszdrdqrb5h4ebrv2fqby

Large-Scale Benchmarking of Diverse Artificial Vision Models in Prediction of 7T Human Neuroimaging Data [article]

Colin Conwell, Jacob S. Prince, George Alvarez, Talia Konkle
2022 bioRxiv   pre-print
Broadly, this work presents a lay-of-the-land for the emergent correspondences between the feature spaces of modern deep neural network models and the representational structure inherent to the human visual  ...  Rapid simultaneous advances in machine vision and cognitive neuroimaging present an unparalleled opportunity to (re)assess the current state of artificial models of the human visual system.  ...  One possible alternative, similar to work done recently in the neural network modeling of mouse visual cortex (Nayebi et al., 2021) , is to directly incorporate the activity of other human brains into  ... 
doi:10.1101/2022.03.28.485868 fatcat:fvsfluvvureylhv7f62uncxdem

Data-driven analyses of motor impairments in animal models of neurological disorders

Hardeep Ryait, Edgar Bermudez-Contreras, Matthew Harvey, Jamshid Faraji, Behroo Mirza Agha, Andrea Gomez-Palacio Schjetnan, Aaron Gruber, Jon Doan, Majid Mohajerani, Gerlinde A. S. Metz, Ian Q. Whishaw, Artur Luczak (+1 others)
2019 PLoS Biology  
The same network was also trained to successfully score movements in a variety of other behavioral tasks.  ...  The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator.  ...  Lambert, and A. Shienh for help with data analyses and W. Samek and his group for helpful discussion.  ... 
doi:10.1371/journal.pbio.3000516 pmid:31751328 pmcid:PMC6871764 fatcat:rzi5owtbpbfkhf6ue45qexnspe

A performance-optimized model of neural responses across the ventral visual stream [article]

Darren Seibert, Daniel L Yamins, Diego Ardila, Ha Hong, James J DiCarlo, Justin L Gardner
2016 bioRxiv   pre-print
Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by  ...  We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging  ...  representations in the whole set of hierarchical visual areas we examined in human cortex.  ... 
doi:10.1101/036475 fatcat:avhkxp524jfongidzliigtkzwq

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Edmund T. Rolls
2012 Frontiers in Computational Neuroscience  
Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described.  ...  The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting.  ...  Support from the Medical Research Council, the Wellcome Trust, the Oxford McDonnell Centre in Cognitive Neuroscience, and the Oxford Centre for Computational Neuroscience (, where .pdfs of  ... 
doi:10.3389/fncom.2012.00035 pmid:22723777 pmcid:PMC3378046 fatcat:vywfnubxvzaptmjdhzazbvjsre

Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks [article]

Bryan Tripp
2016 arXiv   pre-print
Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream.  ...  In particular, activity in early, intermediate, and late layers is closely related to activity in V1, V4, and the inferotemporal cortex (IT).  ...  ACKNOWLEDGMENT Victor Reyes Osorio extracted the stimulus images from [17] . Salman Khan provided comments on an earlier draft.  ... 
arXiv:1612.06975v1 fatcat:arcebzqzafcj7abd46efyleir4

A distributed, hierarchical and recurrent framework for reward-based choice

Laurence T. Hunt, Benjamin Y. Hayden
2017 Nature Reviews Neuroscience  
information processing across the cortex.  ...  First, decisions may be formed in a distributed fashion across many brain regions that act in concert and perform similar computations.  ...  The design of the 'local convolution' and 'max pooling' steps in a ConvNet were based on the response properties of primary visual cortex (V1) simple and complex cells, respectively60.  ... 
doi:10.1038/nrn.2017.7 pmid:28209978 pmcid:PMC5621622 fatcat:mwcgxwvt6zd6nbaxbjtwlaxcyi

Feedforward object-vision models only tolerate small image variations compared to human

Masoud Ghodrati, Amirhossein Farzmahdi, Karim Rajaei, Reza Ebrahimpour, Seyed-Mahdi Khaligh-Razavi
2014 Frontiers in Computational Neuroscience  
Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks.  ...  Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations.  ...  Moreover, it opens new ways for developing models that have similar representations and performance to the primates' brain (Yamins et al., 2014) .  ... 
doi:10.3389/fncom.2014.00074 pmid:25100986 pmcid:PMC4103258 fatcat:4vxbxclqwzhr5kvv4fqmdedeka

Processing Bias: Extending Sensory Drive to Include Efficacy and Efficiency in Information Processing [article]

Julien P. Renoult, Tamra C. Mendelson
2019 arXiv   pre-print
We refer to the causal link between preference and the emotionally rewarding experience of effective and efficient information processing as the processing bias, and we apply it to the evolutionary model  ...  In parallel, the field of empirical aesthetics aims to understand why people like some designs more than others.  ...  Gomez and other members of the CEFE and ISEM journal clubs in Montpellier, other friendly reviewers.  ... 
arXiv:1901.00782v1 fatcat:4yb6ff2qnbglphgl6tyylr6hj4

COVID ‐19 vs influenza viruses: A cockroach optimized deep neural network classification approach

Mohamed A. El‐dosuky, Mona Soliman, Aboul Ella Hassanien
2021 International journal of imaging systems and technology (Print)  
Five hundred ninety-four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.  ...  The pathogenesis of the COVID-19 depends on the virus's ability to attach to and enter into a suitable human host cell.  ...  The convolutional neural network is a process in deep learning, consisting of several layers. In physiology, the ConvNets is driven by the visual cortex.  ... 
doi:10.1002/ima.22562 pmid:33821096 pmcid:PMC8014556 fatcat:vbc23pqsmjecvkiqs7ww4kqnvq

Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

Bo Zhao, Ruoxi Ding, Shoushun Chen, Bernabe Linares-Barranco, Huajin Tang
2015 IEEE Transactions on Neural Networks and Learning Systems  
The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of Leaky Integrate-and-Fire spiking neurons).  ...  The system was further evaluated on the MNIST-DVS dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.  ...  It has been a hot topic for decades to model the feature representations in the visual cortex and design systems that mimic the cortical information processing.  ... 
doi:10.1109/tnnls.2014.2362542 pmid:25347889 fatcat:3gfbn3f7sncu5ijzryuzxoowuu

Modeling the shape hierarchy for visually guided grasping

Omid Rezai, Ashley Kleinhans, Eduardo Matallanas, Ben Selby, Bryan P. Tripp
2014 Frontiers in Computational Neuroscience  
We considered superquadrics because they occupy a role in robotics that is similar to AIP, in that superquadric fits are derived from visual input and used for grasp planning.  ...  We modeled shape tuning in visual AIP neurons and its relationship with curvature and gradient information from the caudal intraparietal area (CIP).  ...  INTRODUCTION The macaque anterior intraparietal area (AIP) receives input from the visual cortex, and is involved in visually guided grasping.  ... 
doi:10.3389/fncom.2014.00132 pmid:25386134 pmcid:PMC4209868 fatcat:kmtawwyctrhmrau2yauj5gd4za

Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?

Andrea De Cesarei, Shari Cavicchi, Giampaolo Cristadoro, Marco Lippi
2021 Cognitive Science  
Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear.  ...  Here, we investigated this issue by engaging humans and CNNs in a two-class visual categorization task.  ...  Studies which directly compared visual representations in humans and CNNs indicated a similarity between the hierarchical analysis of visual information in hidden CNN layers and the sequence of stages  ... 
doi:10.1111/cogs.13009 pmid:34170027 fatcat:hezuyjitzzgi5ji7vjchc722oe
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