328 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
Science Concierge: A fast content-based recommendation system for scientific publications
Finding relevant publications is important for scientists who have to cope
with exponentially increasing numbers of scholarly material. Algorithms can
help with this task as they help for music, movie, and product recommendations.
However, we know little about the performance of these algorithms with
scholarly material. Here, we develop an algorithm, and an accompanying Python
library, that implements a recommendation system based on the content of
articles. Design principles are to adapt to new content, provide near-real time
suggestions, and be open source. We tested the library on 15K posters from the
Society of Neuroscience Conference 2015. Human curated topics are used to cross
validate parameters in the algorithm and produce a similarity metric that
maximally correlates with human judgments. We show that our algorithm
significantly outperformed suggestions based on keywords. The work presented
here promises to make the exploration of scholarly material faster and more
accurate.Comment: 12 pages, 5 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
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