Assembling coherence in neural populations

Experimental breakthroughs are yielding an unprecedented view of the brain's connectivity and of its coherent dynamics ---and a major challenge is to understand how the former leads to the latter. In our approach, we use graphical and point process methods to isolate the contribution of successively more-complex network features to coherent spiking. Next, we show how network features can be efficiently combined, yielding a set of low-order graph statistics we name "motif cumulants." These can be sampled experimentally, and appear to contain the necessary information to predict overall levels of coherence in a neural population. We close by asking what features of this coherence matter most --and least-- for the neural "coding" of information. This is joint work with Yu Hu, James Trousdale, Kresimir Josic, and Joel Zylberberg.