7 papers
Direct dependencies between neurons explain activity
Christopher W. Lynn
Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. Yet experiments demonstrate that some neurons ar…
Quantifying the compressibility of the human brain
Nicholas J. Weaver, Joshua I. Faskowitz, Richard F. Betzel +1
In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations -- and how many -- are nee…
Neural subspaces, minimax entropy, and mean-field theory for networks of neurons
Luca Di Carlo, Francesca Mignacco, Christopher W. Lynn +1
Recent advances in experimental techniques enable the simultaneous recording of activity from thousands of neurons in the brain, presenting both an opportunity and a challenge: to…
Coarse-graining dynamics to maximize irreversibility
Qiwei Yu, Matthew P. Leighton, Christopher W. Lynn
In many far-from-equilibrium biological systems, energy injected by irreversible processes at microscopic scales propagates to larger scales to fulfill important biological functio…
Minimax entropy: The statistical physics of optimal models
David P. Carcamo, Nicholas J. Weaver, Purushottam D. Dixit +1
When constructing models of the world, we aim for optimal compressions: models that include as few details as possible while remaining as accurate as possible. But which details --…
Extended mean-field theories for networks of real neurons
Luca Di Carlo, Francesca Mignacco, Christopher W. Lynn +1
If the behavior of a system with many degrees of freedom can be captured by a small number of collective variables, then plausibly there is an underlying mean-field theory. We show…