Collision Selective Visual Neural Network Inspired by Neurons in - PowerPoint PPT Presentation

Jan 09, 2023 •234 likes •409 views

Collision Selective Visual Neural Network Inspired by Neurons in Juvenile Locusts Qinbing Fu and Shigang Yue Computational Intelligence Lab School of Computer Science supported by EU FP7-IRSES Project EYE2E (269118) and LIVCODE (295151)

Collision Selective Visual Neural Network Inspired by Neurons in Juvenile Locusts Qinbing Fu and Shigang Yue Computational Intelligence Lab School of Computer Science supported by EU FP7-IRSES Project EYE2E (269118) and LIVCODE (295151)
Research Interests • Neuroscience: Neural Networks (SNN), Visual Pathway (ON/OFF) • Computer Vision: Bio-inspired Visual NN (LGMDs, EMDs, DSNs, STMDs) – Motion Detectors inspired by insects • Robotic Application (Colias) • Machine Learning (CNN, DL)
Motivations • To simulate the juvenile locusts on the ground responding to the predators from the sky • To realize particular collision selectivity, btw solve the defects of LGMD1 computational models. • A robust and cheap looming objects detector
LGMDs Morphology
Network
Core • Low-level Image Feature Processing • Differential Image • No object recognition, scene analysis, even edge detection • A Biased ON and OFF Dual-Channel • ON – luminance increments, OFF – luminance decrements • Separate signal flows, filter, then fuse • A game race between Excitatory and Inhibitory flows • Only sensitive to Light-to-Dark luminance change • Highly Non-linear Signal Processing • Outputs – Firing Rates of Exponential Distribution
EXPs Simulated Stimulus
EXPs Simulated Stimulus
Recorded Stimulus
Colias Tests What is Colias seeing? In Arena Watch Videos
Colias Feedback Continue……
Continue……
Distance to Detect Collision
Conclusion • Perform well in arena with many obstacles • Able to detect looming objects against bright background selectively while not responding to objects in dark background • Practical for ground mobile robots and Cheap cost
Future Work • ON/OFF for Directional Motion Detectors • Data Training • SNN
Thank you!

Recommend

Neural network representations and visual processing in brains Katja Seeliger [ www.ccnlab.net |

Neural network representations and visual processing in brains Katja Seeliger [ www.ccnlab.net | Marcel van Gerven ] Neural network representations and visual processing in brains Computational Cognitive Neuroscience Lab Develop computational

405 views • 21 slides

Neural Networks Oskar Taubert (SCC) SCC 1 15.01.2020 Oskar Taubert - Neural Networks SCC

Neural Networks Oskar Taubert (SCC) SCC 1 15.01.2020 Oskar Taubert - Neural Networks SCC www.kit.edu KIT The Research University in the Helmholtz Association Neural Network Concept (very) remotely brain inspired computational system

479 views • 25 slides

Neural Networks (Reading: Kuncheva Section 2.5) Introduction Inspired by Biology But as used in

Neural Networks (Reading: Kuncheva Section 2.5) Introduction Inspired by Biology But as used in pattern recognition research, have little relation with real neural systems (studied in neurology and neuroscience ) Kuncheva: the literature on

528 views • 25 slides

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural IR tasks Neural IR architecture Feature Representations Neural IR query auto completion Neural IR query suggestion Neural IR document

1.48k views • 18 slides

Slides by Nolan Dey Motivation Neural networks are often treated as a black box Network

Slides by Nolan Dey Motivation Neural networks are often treated as a black box Network dissection attempts to describe what features individual neurons are focusing on Network Dissection 1. Identify a broad set of human-labeled visual

562 views • 13 slides

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class Recurrent Neural Network Cell Recurrent Neural Networks (RNNs) Bi-Directional Recurrent Neural Networks (Bi-RNNs) Multiple-layer /

583 views • 47 slides

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

793 views • 21 slides

10-Collision Response Collision Response Collision Response [Moore and Wilhelms 88]:

10-Collision Response Collision Response Collision Response [Moore and Wilhelms 88]: automatic suggestions about the motions of objects immediately following a collision; animation systems using dynamical simulation inherently must respond

767 views • 53 slides

Principles of neural network design Francois Belletti, CS294 RISE Human brains as metaphors of

Principles of neural network design Francois Belletti, CS294 RISE Human brains as metaphors of statistical models Biological analogies Machine learning instantiations The visual cortex of mammals Deep convolutional neural networks

561 views • 28 slides

Selective Attention for Context-aware Neural Machine Translation Sameen Maruf , Andr e F. T.

Selective Attention for Context-aware Neural Machine Translation Sameen Maruf , Andr e F. T. Martins , Gholamreza Haffari Faculty of Information Technology, Monash University, Australia Unbabel & Instituto de

1.01k views • 88 slides

Visual cortex as a general-purpose information-processing device Dr. James A. Bednar Institute

Visual cortex as a general-purpose information-processing device Dr. James A. Bednar Institute for Adaptive and Neural Computation The University of Edinburgh Bio-inspired Vision How can we learn from biology about building a robust,

478 views • 36 slides

Artificial Neural Networks By: Kodi Neumiller Overview What is an artificial neural network

Artificial Neural Networks By: Kodi Neumiller Overview What is an artificial neural network Perceptron Feed forward Backpropagation Learning rate/momentum What is a neural network? Artificial Human Neurons Each

888 views • 12 slides

Same, Same But Different Recovering Neural Network Quantization Error Through Weight

Same, Same But Different Recovering Neural Network Quantization Error Through Weight Factorization Eldad Meller ICML 2019 Neural Network Quantization Quantization of Neural Networks is needed for efficient inference Quantization adds

482 views • 9 slides

Brain inspired Deep Learning Architectures Alex Movila Conventional artificial neural networks

Brain inspired Deep Learning Architectures Alex Movila Conventional artificial neural networks Inspired by the biological brain Benchmarked on tasks solved by the biological brain ...but compute in a fundamentally different

741 views • 71 slides

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics Activation Functions Stochastic Gradient Descent (SGD) Regularization (Dropout) Training Tips and Tricks Neural Network (NN)

828 views • 45 slides

Graph Neural Network Fang Yuanqiang, 2019/05/18 Graph Neural Network Why GNN? Preliminary

Graph Neural Network Fang Yuanqiang, 2019/05/18 Graph Neural Network Why GNN? Preliminary Fixed graph Vanilla Spectral Graph ConvNets ChebyNet GCN CayleyNet Multiple graphs Variable graph

817 views • 46 slides

Neural network applications ALVINN (Pomerleau, mid 1990s) Autonomous Land Vehicle in Neural

Neural network applications ALVINN (Pomerleau, mid 1990s) Autonomous Land Vehicle in Neural Network To date: Sharp Straight Sharp Left Ahead Right Neural networks: what are they 30 Output Units Backpropagation: efficient

346 views • 15 slides

Neural Networks for Machine Learning Lecture 2a An overview of the main types of neural network

Neural Networks for Machine Learning Lecture 2a An overview of the main types of neural network architecture Geoffrey Hinton with Nitish Srivastava Kevin Swersky Feed-forward neural networks These are the commonest type of neural

534 views • 32 slides

Similarity of Neural Network Representations Revisited Simon Kornblith, Mohammad Norouzi,

Similarity of Neural Network Representations Revisited Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geofgrey Hinton Motivation We need tools to understand trained neural networks Neural network training involves interactions between an

588 views • 16 slides

Lecture 2: Deeper Neural Network 1 Objective In the second lecture, you will see How to

Lecture 2: Deeper Neural Network 1 Objective In the second lecture, you will see How to deepen your neural network to learn more complex functions Several techniques to fasten your learning process The most popular artificial

423 views • 40 slides

Neural network architectures for image captioning By Emily Kern Given a set of images and

Neural network architectures for image captioning By Emily Kern Given a set of images and accompanying human-generated captions, can we train a neural network to predict captions for new images? What is a neural network? A computer system

523 views • 16 slides

Neural Topological SLAM for Visual Navigation CVPR-2020 Webpage:

Neural Topological SLAM for Visual Navigation CVPR-2020 Webpage: https://devendrachaplot.github.io/projects/Neural-Topological-SLAM Abhinav Ruslan Devendra Singh Saurabh Gupta Salakhutdinov Gupta Chaplot Semantic Priors and

821 views • 55 slides

Unsupervised clustering with growing self-organizing neural network A comparison with non-neural

Unsupervised clustering with growing self-organizing neural network A comparison with non-neural approach Martin Hynar, Michal Burda, Jana Sarmanov a Department of Computer Science, V SB Technical University of Ostrava, 17.

473 views • 24 slides

Collision Detection Part 2. Narrow Phase Collision Detection The Narrow Phase Exact collision

Collision Detection Part 2. Narrow Phase Collision Detection The Narrow Phase Exact collision detection: Where precisely have objects touched, where to apply forces, torques? Collision Response The following is an equation for Rigid Body

1.06k views • 79 slides