activity
20092024
most citedThe Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity

217 citations · 321 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.dis-nn2024

Glassy dynamics near the interpolation transition in deep recurrent networks

John Hertz, Joanna Tyrcha

We examine learning dynamics in deep recurrent networks, focusing on the behavior near the boundary in the depth-width plane separating under- from over-parametrized networks, know…

physics.bio-ph2018★ 3 cited

Stochastic activation in a genetic switch model

John Hertz, Joanna Tyrcha, Alvaro Correales

We study a biological autoregulation process, involving a protein that enhances its own transcription, in a parameter region where bistability would be present in the absence of fl…

cond-mat.dis-nn2014★ 19 cited

Belief-Propagation and replicas for inference and learning in a kinetic Ising model with hidden spins

Claudia Battistin, John Hertz, Joanna Tyrcha +1

We propose a new algorithm for inferring the state of hidden spins and reconstructing the connections in a synchronous kinetic Ising model, given the observed history. Focusing on…

cond-mat.dis-nn2013★ 23 cited

Network Inference with Hidden Units

Joanna Tyrcha, John Hertz

We derive learning rules for finding the connections between units in stochastic dynamical networks from the recorded history of a ``visible'' subset of the units. We consider two…

q-bio.QM2012★ 56 cited

The Effect of Nonstationarity on Models Inferred from Neural Data

Joanna Tyrcha, Yasser Roudi, Matteo Marsili +1

Neurons subject to a common non-stationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interac…

q-bio.QM2011★ 3 cited

Ising Models for Inferring Network Structure From Spike Data

John Hertz, Yasser Roudi, Joanna Tyrcha

Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of.…