Spiking Neural Network (SNN) with PyTorch: towards bridging the gap between deep learning and the human brain

I think I’ve discovered something amazing: Hebbian learning naturally takes place during the backpropagation of SNNs. Backpropagation in Spiking Neural Networks (SNNs) engenders Spike-Timing-Dependent Plasticity (STDP)-like Hebbian learning behavior. So: – At first I simply thought “hey, what about coding a Spiking Neural Network using an automatic differentiation framework?” Here it is. – Then I […]

How to Grow Neat Software Architecture out of Jupyter Notebooks

Have you ever been in the situation where you’ve got Jupyter notebooks (iPython notebooks) so huge that you were feeling stuck in your code? Or even worse: have you ever found yourself duplicating your notebook to do changes, and then ending up with lots of badly named notebooks? Well, we’ve all been here if using notebooks long enough. So how should we code with notebooks?

LSTMs for Human Activity Recognition

See the GitHub project here. Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories: – WALKING, – WALKING_UPSTAIRS, – WALKING_DOWNSTAIRS, – SITTING, – STANDING, – LAYING. Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no […]