Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

DOI10.1162/NECO_a_00744zbMath1414.92087OpenAlexW1930031410WikidataQ36722855 ScholiaQ36722855MaRDI QIDQ5380286

Loren M. Frank, Daniel F. Liu, Xinyi Deng, K. G. Kay, Uri T. Eden

Publication date: 4 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00744

zbMATH Keywords

spatial locationhippocampal neural activitypoint process decoding algorithmspike waveforms

Mathematics Subject Classification ID

Neural biology (92C20) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (3)

A common goodness-of-fit framework for neural population models using marked point process time-rescaling ⋮ Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus ⋮ Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling

Uses Software

• fda (R)
• PRMLT

Cites Work

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• An Introduction to the Theory of Point Processes
• Estimating a State-Space Model from Point Process Observations
• Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
• Spike Train Decoding Without Spike Sorting

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