27 research outputs found
Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?
Several theories of early sensory processing suggest that it whitens sensory
stimuli. Here, we test three key predictions of the whitening theory using
recordings from 152 ganglion cells in salamander retina responding to natural
movies. We confirm the previous finding that firing rates of ganglion cells are
less correlated compared to natural scenes, although significant correlations
remain. We show that while the power spectrum of ganglion cells decays less
steeply than that of natural scenes, it is not completely flattened. Finally,
we find evidence that only the top principal components of the visual stimulus
are transmitted.Comment: 2016 Asilomar Conference on Signals, Systems and Computer
Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data
Development and homeostasis in multicellular systems both require exquisite
control over spatial molecular pattern formation and maintenance. Advances in
spatially-resolved and high-throughput molecular imaging methods such as
multiplexed immunofluorescence and spatial transcriptomics (ST) provide
exciting new opportunities to augment our fundamental understanding of these
processes in health and disease. The large and complex datasets resulting from
these techniques, particularly ST, have led to rapid development of innovative
machine learning (ML) tools primarily based on deep learning techniques. These
ML tools are now increasingly featured in integrated experimental and
computational workflows to disentangle signals from noise in complex biological
systems. However, it can be difficult to understand and balance the different
implicit assumptions and methodologies of a rapidly expanding toolbox of
analytical tools in ST. To address this, we summarize major ST analysis goals
that ML can help address and current analysis trends. We also describe four
major data science concepts and related heuristics that can help guide
practitioners in their choices of the right tools for the right biological
questions
Definitions, methods, and applications in interpretable machine learning.
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods