Search Results for author:
Found 7 papers, 5 papers with code
Sparse convolutional coding for neuronal assembly detection
Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing.
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing.
DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging
In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings.
Obtaining Causal Information by Merging Datasets with MAXENT
Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders.
Causal Inference Through the Structural Causal Marginal Problem
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
Beyond Single-Feature Importance with ICECREAM
Which set of features was responsible for a certain output of a machine learning model?
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking.
