scholarly journals Validation of the Minimum-Error Method for Estimating Model Parameters from Neural Spike Train Data

2015 ◽  
Vol 19 (4) ◽  
pp. 111-114
Author(s):  
Huu Hoang ◽  
Isao T. Tokuda
Keyword(s):  
Spike Train ◽  
Model Parameters ◽  
Minimum Error ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Spike Train Data ◽  
Error Method

An integrative analysis platform for multiple neural spike train data

2008 ◽  
Vol 172 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Yu Huang ◽  
Xiangning Li ◽  
Yanling Li ◽  
Qingwei Xu ◽  
Qiang Lu ◽  
...  
Keyword(s):  
Spike Train ◽  
Integrative Analysis ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Spike Train Data ◽  
Analysis Platform

scholarly journals Introduction to neural spike train data for phase-amplitude analysis

2014 ◽  
Vol 8 (2) ◽  
pp. 1759-1768 ◽  
Author(s):  
Wei Wu ◽  
Nicholas G. Hatsopoulos ◽  
Anuj Srivastava
Keyword(s):  
Spike Train ◽  
Amplitude Analysis ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Spike Train Data ◽  
Phase Amplitude

Multiple neural spike train data analysis: state-of-the-art and future challenges

2004 ◽  
Vol 7 (5) ◽  
pp. 456-461 ◽  
Author(s):  
Emery N Brown ◽  
Robert E Kass ◽  
Partha P Mitra
Keyword(s):  
Data Analysis ◽  
Spike Train ◽  
State Of The Art ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Future Challenges ◽  
Spike Train Data

The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis

2002 ◽  
Vol 14 (2) ◽  
pp. 325-346 ◽  
Author(s):  
Emery N. Brown ◽  
Riccardo Barbieri ◽  
Valérie Ventura ◽  
Robert E. Kass ◽  
Loren M. Frank
Keyword(s):  
Probability Theory ◽  
Data Analysis ◽  
Spike Train ◽  
Point Process ◽  
Goodness Of Fit ◽  
Spike Trains ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Neural Spike Trains ◽  
Spike Train Data

Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments.We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

Causal pattern recovery from neural spike train data using the Snap Shot Score

2009 ◽  
Vol 29 (1-2) ◽  
pp. 231-252 ◽  
Author(s):  
Christoph Echtermeyer ◽  
Tom V. Smulders ◽  
V. Anne Smith
Keyword(s):  
Spike Train ◽  
Neural Spike ◽  
Neural Spike Train ◽  
Spike Train Data
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