Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis

DOI10.1162/NECO_a_00684zbMath1414.92122OpenAlexW2067982677WikidataQ30868412 ScholiaQ30868412MaRDI QIDQ5380181

Michael J. Black, Carlos E. Vargas-Irwin, Jonas B. Zimmermann, John P. Donoghue, David M. Brandman

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_00684

zbMATH Keywords

neural spiking patternssingle neuron data analysisspike train similarity space

Mathematics Subject Classification ID

Neural biology (92C20) Biomedical imaging and signal processing (92C55)

Related Items (3)

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models ⋮ Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression ⋮ Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps

Uses Software

• t-SNE

Cites Work

• Unnamed Item
• Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter
• Metric-space analysis of spike trains: theory, algorithms and application
• Population Coding and the Labeling Problem: Extrinsic Versus Intrinsic Representations

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