3 citations · 4 across the 11 of their papers we have counts for
11 papers
Emergence of criticality in models of real neurons
David P. Carcamo, Christopher W. Lynn
Critical systems sit near boundaries between qualitatively distinct behaviors. When inferring models of neural activity, this proximity to criticality is thought to require the pre…
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…
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…
Statistical physics of large-scale neural activity with loops
David P. Carcamo, Christopher W. Lynn
As experiments advance to record from tens of thousands of neurons, statistical physics provides a framework for understanding how collective activity emerges from networks of fine…
Exact minimax entropy models of large-scale neuronal activity
Christopher W. Lynn, Qiwei Yu, Rich Pang +2
In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamen…
Exactly solvable statistical physics models for large neuronal populations
Christopher W. Lynn, Qiwei Yu, Rich Pang +2
Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for popul…
Spontaneous brain activity emerges from pairwise interactions in the larval zebrafish brain
Richard E. Rosch, Dominic R. W. Burrows, Christopher W. Lynn +1
Brain activity is characterized by brain-wide spatiotemporal patterns which emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to…