William Bialek (Princeton University) — May 29, 2018
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.
William Bialek is a theoretical biophysicist and a professor at Princeton University and The Graduate Center at CUNY.
Much of his work, at the interface of physics and biology, centers around whether various functions of living beings perform optimally, close to the limits set by basic physical principles. Best known among these is an influential series of studies applying the principles of information theory to the analysis of the neural encoding of information in the nervous system.
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