Topic: spatio-temporal spiking motifs

Timing is essential for neural processing, but evidence for such temporal precision is still lacking. We have developed a theoretical model of representation based on spatio-temporal spiking motifs. Our goal is to develop a self-supervised learning method for optimal detection of such motifs in neurobiological data.To detect such motifs, we have extended the K-Means algorithm to process temporal data using a convolutional operator. A second pooling layer ensures that only one motif is used per time step. The results were improved by ensuring that the detected motifs are equiprobably activated