Neural network model of on-off units in the fly visual system: simulations of dynamic behavior

1998 ◽  
Vol 78 (5) ◽  
pp. 399-412 ◽  
Author(s):  
Mert Sarikaya ◽  
Wenxia Wang ◽  
Haluk Öğmen
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Dynamic Behavior ◽  
Network Model ◽  
Neural Network Model

Neural Network Model of the Visual System: Binding Form and Motion

1996 ◽  
Vol 9 (8) ◽  
pp. 1417-1427 ◽  
Author(s):  
MASAYUKI KIKUCHI ◽  
KUNIHIKO FUKUSHIMA
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Network Model ◽  
Neural Network Model

scholarly journals The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

2016 ◽  
Vol 27 (1) ◽  
pp. 29-51
Author(s):  
Juan M. Galeazzi ◽  
Joaquín Navajas ◽  
Bedeho M. W. Mender ◽  
Rodrigo Quian Quiroga ◽  
Loredana Minini ◽  
...  
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Network Model ◽  
Neural Network Model ◽  
Receptive Fields ◽  
Visual Development

scholarly journals Neural Network Model of Dynamical Grouping Process in the Human Visual System.

1999 ◽  
Vol 53 (1) ◽  
pp. 135-141
Author(s):  
Shin Kobayashi ◽  
Shigeru Okabayashi ◽  
Isao Horiba ◽  
Noboru Sugie ◽  
Hiroaki Kudo ◽  
...  
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Network Model ◽  
Neural Network Model ◽  
Human Visual System

scholarly journals CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex

2021 ◽  
Author(s):  
Jianghong Shi ◽  
Bryan Tripp ◽  
Eric Shea-Brown ◽  
Stefan Mihalas ◽  
Michael Buice
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Visual System ◽  
Convolutional Neural Network ◽  
Mouse Brain ◽  
Network Model ◽  
Neural Network Model ◽  
Structural Parameters ◽  
Neural Representation ◽  
Mouse Visual Cortex

Convolutional neural networks trained on object recognition derive inspiration from the neural architecture of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the deep hierarchical organization of primates, the visual system of the mouse has a shallower arrangement. Since mice and primates are both capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a novel framework for building a biologically constrained convolutional neural network model of the mouse visual cortex. The architecture and structural parameters of the network are derived from experimental measurements, specifically the 100-micrometer resolution interareal connectome, the estimates of numbers of neurons in each area and cortical layer, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. Using a well-studied image classification task as our working example, we demonstrate the computational capability of this mouse-sized network. Given its relatively small size, MouseNet achieves roughly 2/3rds the performance level on ImageNet as VGG16. In combination with the large scale Allen Brain Observatory Visual Coding dataset, we use representational similarity analysis to quantify the extent to which MouseNet recapitulates the neural representation in mouse visual cortex. Importantly, we provide evidence that optimizing for task performance does not improve similarity to the corresponding biological system beyond a certain point. We demonstrate that the distributions of some physiological quantities are closer to the observed distributions in the mouse brain after task training. We encourage the use of the MouseNet architecture by making the code freely available.

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