LPNet: Retina Inspired Neural Network for Object Detection and Recognition

Cao, Jie; Bao, Chun; Hao, Qun; Yang, Cheng; Chen, Chenglin

doi:10.3390/electronics10222883

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Our resizing (i.e., downsampling) of the images to be smaller during training acted as a proxy for low-pass filtering. We believe that future work could investigate other appropriate low-pass filters and ecologically-relevant pixel-level transformations to apply to the original image or video stream [ 64 , 65 ].…”

Section: Discussionmentioning

confidence: 99%

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Nayebi,

Kong,

Zhuang

et al. 2023

PLoS Comput Biol

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Studies of the mouse visual system have revealed a variety of visual brain areas that are thought to support a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex, and how it supports a range of behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex and identifying key structural and functional principles underlying that model’s success. Structurally, we find that a comparatively shallow network structure with a low-resolution input is optimal for modeling mouse visual cortex. Our main finding is functional—that models trained with task-agnostic, self-supervised objective functions based on the concept of contrastive embeddings are much better matches to mouse cortex, than models trained on supervised objectives or alternative self-supervised methods. This result is very much unlike in primates where prior work showed that the two were roughly equivalent, naturally leading us to ask the question of why these self-supervised objectives are better matches than supervised ones in mouse. To this end, we show that the self-supervised, contrastive objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse’s limited resources to create a light-weight, general-purpose visual system—in contrast to the deep, high-resolution, and more categorization-dominated visual system of primates.

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Section: Discussionmentioning

confidence: 99%

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Nayebi,

Kong,

Zhuang

et al. 2023

PLoS Comput Biol

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“…Kumar et al [14] proposed an automatic system to detect the existence of face masks using deep learning and image processing algorithms. Cao et al [15] presented an improved network-inspired NN to solve object rotation problems.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Deep Neural Network Model for Fingerprint Classification

Mahmoud¹,

Awad²,

Behery³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving 99% accuracy then it stops and outputs the result. Moreover, this paper proposes an end-to-end methodology for recognizing a person's identity from the input fingerprint image based on a residual convolutional neural network. It is a complete system and is fully automated whether in the features extraction stage or the classification stage. Our goal is to automate this fingerprint recognition system because the more automatic the system is, the more time and effort it saves. Our model also allows users to react by inputting the initial values of these parameters. Then, the model updates itself until it finds the optimal values for the parameters and achieves the best accuracy. Another advantage of our algorithm is that it can recognize people from their thumb and other fingers and its ability to recognize distorted samples. Our algorithm achieved 99.75% accuracy on the public fingerprint dataset (SOCOFing). This is the best accuracy compared with other models.

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Section: Imagenet Categorization Accuracymentioning

confidence: 99%

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Nayebi

Kong

Zhuang

et al. 2021

Preprint

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Task-optimized deep convolutional neural networks are the most quantitatively accurate models of the primate ventral visual stream. However, such networks are implausible as a model of the mouse visual system because mouse visual cortex has a known shallower hierarchy and the supervised objectives these networks are typically trained with are likely neither ethologically relevant in content nor in quantity. Here we develop shallow network architectures that are more consistent with anatomical and physiological studies of mouse visual cortex than current models. We demonstrate that hierarchically shallow architectures trained using contrastive objective functions applied to visual-acuity-adapted images achieve neural prediction performance that exceed those of the same architectures trained in a supervised manner and result in the most quantitatively accurate models of the mouse visual system. Moreover, these models' neural predictivity significantly surpasses those of supervised, deep architectures that are known to correspond well to the primate ventral visual stream. Finally, we derive a novel measure of inter-animal consistency, and show that the best models closely match this quantity across visual areas. Taken together, our results suggest that contrastive objectives operating on shallow architectures with ethologically-motivated image transformations may be a biologically-plausible computational theory of visual coding in mice.

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LPNet: Retina Inspired Neural Network for Object Detection and Recognition

Cited by 4 publications

References 45 publications

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

An Automatic Deep Neural Network Model for Fingerprint Classification

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

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