Computational Neuro Science

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Feel free to contribute & open an issue to help the nevous system

Project Charters

1. Neuron Models
2. Neural Population
3. Unsupervised STDP Learning
4. Reward Modulated STDP Learning
5. DoG & Gabor in Convolution
6. Full object detection model based on spiking neural network

DoG & Gabor in Convolution

• Gabor Filter
• Difference of Gaussian
• Feature map
• Detect dominant lines and features, and show improvement and precision through time

Unsupervised STDP Learning

• Implementation Unsupervised STDP Learning
• Plot delta weights caused by STDP learning for two neuron with random excitation
• Generate 10x2 Spiking Networks & learn Two 10th-tuple by each output neuron
• Add an inhibitory neuron to upper network

Full object detection model based on spiking neural network

Model trained and tested on caltech101 dataset

Caltech-101 consists of pictures of objects belonging to 101 classes, plus one background clutter class. Each image is labelled with a single object. Each class contains roughly 40 to 800 images, totalling around 9k images. Images are of variable sizes, with typical edge lengths of 200-300 pixels.

We presents an deep spiking nerual model which consist of 3 layer. for first two layer only STDP learning is used, and for last layer dopamin releases followed by STDP and anti-STDP.

For more documentation see code, documentation will be updated

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class videos and Lecture Notes

Computational Neuroscience Research Lab. (Department of Computer Science, University of Tehran) For more info, please visit cnrl.ut.ac.ir

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