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Architectures of neuronal circuits
BACKGROUND The human brain contains about 100 billion neurons, each of which makes
thousands of synaptic connections. Although individual neurons can themselves be …
thousands of synaptic connections. Although individual neurons can themselves be …
Mechanistic Interpretability for AI Safety--A Review
Understanding AI systems' inner workings is critical for ensuring value alignment and safety.
This review explores mechanistic interpretability: reverse engineering the computational …
This review explores mechanistic interpretability: reverse engineering the computational …
The tumor microenvironment shows a hierarchy of cell-cell interactions dominated by fibroblasts
The tumor microenvironment (TME) is comprised of non-malignant cells that interact with
each other and with cancer cells, critically impacting cancer biology. The TME is complex …
each other and with cancer cells, critically impacting cancer biology. The TME is complex …
Finding neurons in a haystack: Case studies with sparse probing
Despite rapid adoption and deployment of large language models (LLMs), the internal
computations of these models remain opaque and poorly understood. In this work, we seek …
computations of these models remain opaque and poorly understood. In this work, we seek …
Explainability in graph neural networks: A taxonomic survey
Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …
intelligence tasks. A major limitation of deep models is that they are not amenable to …
On explainability of graph neural networks via subgraph explorations
We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …
which otherwise are considered as black boxes. Existing methods invariably focus on …
Xgnn: Towards model-level explanations of graph neural networks
Graphs neural networks (GNNs) learn node features by aggregating and combining
neighbor information, which have achieved promising performance on many graph tasks …
neighbor information, which have achieved promising performance on many graph tasks …
Predictive coding: a theoretical and experimental review
Predictive coding offers a potentially unifying account of cortical function--postulating that the
core function of the brain is to minimize prediction errors with respect to a generative model …
core function of the brain is to minimize prediction errors with respect to a generative model …
[HTML][HTML] Zoom in: An introduction to circuits
Many important transition points in the history of science have been moments when science
“zoomed in.” At these points, we develop a visualization or tool that allows us to see the …
“zoomed in.” At these points, we develop a visualization or tool that allows us to see the …
[HTML][HTML] How to build the virtual cell with artificial intelligence: Priorities and opportunities
Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …
modeling and simulating their function and behavior. Advances in AI and omics offer …