63 research outputs found
Quantifying the role of neurons for behavior is a mediation question
Many systems neuroscientists want to understand neurons in terms of
mediation; we want to understand how neurons are involved in the causal chain
from stimulus to behavior. Unfortunately, most tools are inappropriate for that
while our language takes mediation for granted. Here we discuss the contrast
between our conceptual drive towards mediation and the difficulty of obtaining
meaningful evidence.Comment: 4 pages, 2 figure
Learning domain-specific causal discovery from time series
Causal discovery (CD) from time-varying data is important in neuroscience,
medicine, and machine learning. Techniques for CD encompass randomized
experiments, which are generally unbiased but expensive, and algorithms such as
Granger causality, conditional-independence-based, structural-equation-based,
and score-based methods that are only accurate under strong assumptions made by
human designers. However, as demonstrated in other areas of machine learning,
human expertise is often not entirely accurate and tends to be outperformed in
domains with abundant data. In this study, we examine whether we can enhance
domain-specific causal discovery for time series using a data-driven approach.
Our findings indicate that this procedure significantly outperforms
human-designed, domain-agnostic causal discovery methods, such as Mutual
Information, VAR-LiNGAM, and Granger Causality on the MOS 6502 microprocessor,
the NetSim fMRI dataset, and the Dream3 gene dataset. We argue that, when
feasible, the causality field should consider a supervised approach in which
domain-specific CD procedures are learned from extensive datasets with known
causal relationships, rather than being designed by human specialists. Our
findings promise a new approach toward improving CD in neural and medical data
and for the broader machine learning community.Comment: 16 main pages, 7 figures. Accepted by TML
Quantifying How Staining Methods Bias Measurements of Neuron Morphologies
The process through which neurons are labeled is a key methodological choice in measuring neuron morphology. However, little is known about how this choice may bias measurements. To quantify this bias we compare the extracted morphology of neurons collected from the same rodent species, experimental condition, gender distribution, age distribution, brain region and putative cell type, but obtained with 19 distinct staining methods. We found strong biases on measured features of morphology. These were largest in features related to the coverage of the dendritic tree (e.g., the total dendritic tree length). Understanding measurement biases is crucial for interpreting morphological data
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