Dimensionality in biological networks
There is an avalanche of new data on the brain’s activity, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes. For what networks does each case occur? Our contribution to the answer is a new graphical framework, based on "motif cumulants," that links tractable statistical properties of network connectivity with the dimension of the activity that they produce. In tandem, we study how features of connectivity and dynamics that impact dimension arise as networks learn to perform basic tasks. I’ll describe where we have succeeded, where we have failed, and the many avenues that remain.