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Résumé |
In many systems we can describe emergent macroscopic behaviors, quantitatively, using
models that are much simpler than the underlying microscopic interactions; we understand
the success of this simplification through the renormalization group. Could similar
simplifications succeed in complex biological systems? I will discuss explicit coarse-graining
procedures, analogous to real-space and momentum space RG, that we apply to experimental
data on the electrical activity in large populations of neurons in the mouse hippocampus.
Probability distributions of coarse-grained variables seem to approach a fixed non-Gaussian
form, and we see evidence of power-law dependencies in both static and dynamic quantities
as we vary the coarse-graining scale over two decades. Taken together, these results suggest
that the collective behavior of the network is described by a non-trivial fixed point. |
