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Found 13 papers, 2 papers with code
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses
Using this framework, we rederive existing dimensionality reduction methods including the deep variational information bottleneck and variational auto-encoders.
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning
Remarkably, regularized CCA can identify low-dimensional weak covarying structures even when the number of samples is much smaller than the dimensionality of the data, which is a regime challenging for all dimensionality reduction methods.
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck
The Symmetric Information Bottleneck (SIB), an extension of the more familiar Information Bottleneck, is a dimensionality reduction technique that simultaneously compresses two random variables to preserve information between their compressed versions.
Inferring Local Structure from Pairwise Correlations
To construct models of large, multivariate complex systems, such as those in biology, one needs to constrain which variables are allowed to interact.
Neural criticality from effective latent variables
More recently, power laws were also observed in neural populations in the mouse under an activity coarse-graining procedure, and they were explained as a consequence of the neural activity being coupled to multiple latent dynamical variables.
Intrinsic Motivation in Dynamical Control Systems
Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation.
Physical limit to concentration sensing in a changing environment
Cells adapt to changing environments by sensing ligand concentrations using specific receptors.
Receptor crosstalk improves concentration sensing of multiple ligands
Crosstalk is often thought of as a deviation from optimal specific recognition, as the binding of non-cognate ligands can interfere with the detection of the receptor's cognate ligand, possibly leading to a false triggering of a downstream signaling pathway.
Automated, predictive, and interpretable inference of C. elegans escape dynamics
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature.
Stereotypical escape behavior in Caenorhabditis elegans allows quantification of nociceptive stimuli levels
We test the nociception of ibuprofen-treated worms and a TRPV (transient receptor potential) mutant, and we show that the perception of thermal nociception for the ibuprofen treated worms is lower than the wild-type.
Quantitative Methods Neurons and Cognition
Automated adaptive inference of coarse-grained dynamical models in systems biology
Such adaptive models lead to accurate predictions even when microscopic details of the studied systems are unknown due to insufficient data.
Director Field Model of the Primary Visual Cortex for Contour Detection
We aim to build the simplest possible model capable of detecting long, noisy contours in a cluttered visual scene.
Entropy and inference, revisited
We study properties of popular near-uniform (Dirichlet) priors for learning undersampled probability distributions on discrete nonmetric spaces and show that they lead to disastrous results.
Data Analysis, Statistics and Probability
