⭕️ اولین و تخصصی ترین آکادمی یادگیری علوم داده و هوش مصنوعی در ایران
📍تهران، ولنجک، دانشگاه شهید بهشتی، مرکز نوآوری دانشکده ریاضی
🌐 https://loopacademy.ir/
📞 021 - 2591 7391
📮 ارتباط با پشتیبانی در تلگرام:
@loop_support
Post #169
1.25K
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Mohammad Ganjtabesh,
University of Tehran
Title: Bio-inspired Learning of Visual Features in Shallow and Deep Spiking Neural Networks
Abstract: To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway in the cortex, with limited success. In this talk, we show how the association of both biologically inspired network architecture and learning rule significantly improves the models' performance in challenging invariant object recognition problems. In all experiments, we used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. We start with a shallow network, in which neurons in the higher trainable layer are equipped with STDP learning rule and they progressively become selective to intermediate complexity visual features appropriate for object recognition. Then, a deeper model comprising several convolutional (trainable with STDP) and pooling layers will be presented, in which, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Finally, we show how reinforcement learning can be used efficiently to train a deep SNN to perform object recognition in natural images without using any external classifier and the superiority of reward-modulated STDP (R-STDP) over the STDP in extracting discriminative visual features will be discussed.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Mohammad Ganjtabesh,
University of Tehran
Title: Bio-inspired Learning of Visual Features in Shallow and Deep Spiking Neural Networks
Abstract: To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway in the cortex, with limited success. In this talk, we show how the association of both biologically inspired network architecture and learning rule significantly improves the models' performance in challenging invariant object recognition problems. In all experiments, we used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. We start with a shallow network, in which neurons in the higher trainable layer are equipped with STDP learning rule and they progressively become selective to intermediate complexity visual features appropriate for object recognition. Then, a deeper model comprising several convolutional (trainable with STDP) and pooling layers will be presented, in which, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Finally, we show how reinforcement learning can be used efficiently to train a deep SNN to perform object recognition in natural images without using any external classifier and the superiority of reward-modulated STDP (R-STDP) over the STDP in extracting discriminative visual features will be discussed.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
