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Author Kording, Konrad P. ♦ Tomlinson, Tucker ♦ Ramkumar, Pavan ♦ Miller, Lee E. ♦ Chowdhury, Raeed H. ♦ Fernandes, Hugo L. ♦ Benjamin, Ari S. ♦ Versteeg, Chris
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2018-07-19
Copyright Year ©2018
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Generalized linear model ♦ Neuropsychiatry ♦ Biological psychiatry ♦ Neurosciences ♦ Neural coding ♦ Internal medicine ♦ GLM ♦ Medicine ♦ Machine learning ♦ Tuning curves ♦ Encoding models
Abstract Neuroscience has long focused on finding encoding models that effectively ask “what predicts neural spiking?” and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.
ISSN 16625188
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2018-07-01
e-ISSN 16625188
Journal Frontiers in Computational Neuroscience
Volume Number 12

Source: Directory of Open Access Journals (DOAJ)