Multi-layer unsupervised learning in a spiking convolutional neural network

Tavanaei, Amirhossein; Maida, Anthony S.

doi:10.1109/ijcnn.2017.7966099

Cited by 49 publications

(36 citation statements)

References 28 publications

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“…However, they still lead to bimodal distributions. Contrarily, [26], [27] claim that, by incorporating the weight dependency in an inversely proportional manner, stable unimodal distributions are obtained. Never-theless, their stability results from a complex temporal LTP-LTD balance, and it is not theoretically guaranteed.…”

Section: Synaptic Plasticitymentioning

confidence: 99%

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

Paredes-Vallés

Scheper

Croon

2020

IEEE Trans. Pattern Anal. Mach. Intell.

127

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The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems, namely feature extraction and local and global motion perception. Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer. The proposed solution is validated using synthetic and real event sequences. Along with this paper, we provide the cuSNN library, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at https://github.com/tudelft/cuSNN. Index Terms-Event-based vision, feature extraction, motion detection, neural nets, neuromorphic computing, unsupervised learning ! • The authors are with the Department of Control and Simulation (Micro Air Vehicle

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Section: Synaptic Plasticitymentioning

confidence: 99%

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

Paredes-Vallés

Scheper

Croon

2020

IEEE Trans. Pattern Anal. Mach. Intell.

127

View full text Add to dashboard Cite

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“…This leads to a better classification accuracy with a smaller number of neurons. Another unsupervised spiking network was presented by Tavanaei and Maida [23]. In contrast to our network, their network consists of four layers.…”

Section: Discussionmentioning

confidence: 99%

“…This is less biologically plausible than the used learning rules in our approach. Nonetheless, the complex structure of the network from Tavanaei and Maida (2017) [23] and of the Kheradpisheh et al (2017) [22] network is an evidence for the possibility of unsupervised STDP learning rules in a multi-layer network.…”

Section: Discussionmentioning

confidence: 99%

A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement

Larisch¹,

Teichmann²,

Hamker³

2018

Lecture Notes in Computer Science

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In real world scenarios, objects are often partially occluded. This requires a robustness for object recognition against these perturbations. Convolutional networks have shown good performances in classification tasks. The learned convolutional filters seem similar to receptive fields of simple cells found in the primary visual cortex. Alternatively, spiking neural networks are more biological plausible. We developed a two layer spiking network, trained on natural scenes with a biologically plausible learning rule. It is compared to two deep convolutional neural networks using a classification task of stepwise pixel erasement on MNIST. In comparison to these networks the spiking approach achieves good accuracy and robustness.

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“…For this purpose, a new model was proposed using MNIST dataset. This approach reaches an accuracy of 98% and loss range 0.1% to 8.5% [20]. In Germany, a traffic sign recognition model of CNN is suggested.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

Siddique

Sakib

Siddique

2019

2019 5th International Conference on Advances in Electrical Engineering (ICAEE)

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In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones, etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layers and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

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Multi-layer unsupervised learning in a spiking convolutional neural network

Cited by 49 publications

References 28 publications

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

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