8 citations · 18 across the 7 of their papers we have counts for
8 papers
Synaptic spine head morphodynamics from graph grammar rules for actin dynamics
Matthew Hur, Thomas Bartol, Padmini Rangamani +2
There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synap…
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions. Our method estimates an effective probability distr…
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of…
MCell-R: A particle-resolution network-free spatial modeling framework
Jose-Juan Tapia, Ali Sinan Saglam, Jacob Czech +4
Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed…
Spatial Stochastic Modeling with MCell and CellBlender
Sanjana Gupta, Jacob Czech, Robert Kuczewski +4
This chapter provides a brief introduction to the theory and practice of spatial stochastic simulations. It begins with an overview of different methods available for biochemical s…
Reference Type Logic Variables in Constraint-logic Object-oriented Programming
Jan C. Dageförde
Constraint-logic object-oriented programming, for example using Muli, facilitates the integrated development of business software that occasionally involves finding solutions to co…
Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
Oliver K. Ernst, Thomas Bartol, Terrence Sejnowski +1
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant. We propose a machine learning approach t…
Spatial and Temporal Correlates of Vesicular Release at Hippocampal Synapses
Suhita Nadkarni, Thomas Bartol, Terrence Sejnowski +1
We develop a spatially explicit biophysical model of the hippocampal CA3-CA1 presynaptic bouton to study local calcium dynamics leading to vesicle fusion. A kinetic model with two…