Embedder with binary sparse distributed representation.
Spiking neural network framework implementing classification layers based on first-spike coding, event-driven processing, and supervised STDP training.
Pythorch implementation of Winner-Take-All Autoencoder
Stereo-image depth reconstruction with different matching costs and matching algorithms in Python using Numpy and Numba
A neuroscientific sequence learning model on spiking neural networks with winner-take-all circuits and lateral inhibition. Written using the NEST neural simulator and custom neuron/synapse models.
GUI for winner-take-all clustering implementation
Iterative winners-take-all algorithm
Lab experiments of Soft Computing Techniques
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