Blind Separation of Spatio-Temporal Synfire Sources and Visualization of Neural Cliques

A dominating paradigm in neuroscience attributes components of perception and behavior to synchronous spatio-temporal activities of subsets of neurons within neural networks – the so-called Synfire chains. Synfire chains cohere to generate neural cliques within the simultaneously active Synfires. The present study is concerned with blind separation of Synfire activities and identification of neural cliques. Assuming stationarity and, to a first approximation, linearity, we extend the Blind Source Separation (BSS) technique to the spatio-temporal domain, to deal with dynamic signals, and apply it on our analysis of neural networks. To demonstrate the concept of dynamic BSS, we first apply it in a relatively simple physical example of separation of dynamic reflections. In the latter case, the physics of the problem is well understood and the assumptions of linearity and stationarity are valid. In the case of neural networks, the problem is much more involved, and the assumptions of linearity and stationarity are not fully justified. Nevertheless, we demonstrate that the basic approach yields interesting insight into the function of complex neural activity. To better approximate the function of neural activity, we also investigate the effect of non-linearities on blind separation of neural activity