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 […]

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 […]