Joint Peri Stimulus Time Histogram (JPSTH)

2021 ◽  
pp. 1-4
Author(s):  
George L. Gerstein
Keyword(s):  
Stimulus Time

Time

2018 ◽  
Author(s):  
Bruno and
Keyword(s):  
Temporal Coherence ◽  
The Other ◽  
Sensory Receptors ◽  
Traditional Notion ◽  
Physical Energy ◽  
Multisensory Interactions ◽  
The Senses ◽  
Perception Of Time ◽  
Temporal Extent ◽  
Stimulus Time

Within the traditional notion of the senses, the perception of time is especially puzzling. There is no specific physical energy carrying information about time, and hence no sensory receptors can transduce a ‘temporal stimulus.’ Time-related properties of events can instead be shown to emerge from specific perceptual processes involving multisensory interactions. In this chapter, we will examine five such properties: the awareness that two events occur at the same time (simultaneity) or one after the other (succession); the coherent time-stamping of events despite inaccuracies and imprecisions in coding simultaneity and succession (temporal coherence); the awareness of the temporal extent occupied by events (duration); the organization of events in regular temporal units (rhythm).

Reassessment of parameters for applying motor-unit triggered stimuli in peri-stimulus time histograms

2003 ◽  
Vol 990 (1-2) ◽  
pp. 8-19 ◽  
Author(s):  
Junichi Ushiba ◽  
Yasuhiro Kagamihara ◽  
Yoshihisa Masakado
Keyword(s):  
Motor Unit ◽  
Stimulus Time

How to Compare Two Quantities? A Computational Model of Flutter Discrimination

2007 ◽  
Vol 19 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Tom Verguts
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Model ◽  
Neural Network Model ◽  
Assignment Problem ◽  
Time Window ◽  
Cognitive Domains ◽  
Supervised Training ◽  
Comparison Problem ◽  
Stimulus Time

A task that has been intensively studied at the neural level is f lutter discrimination. I argue that f lutter discrimination entails a combination of a temporal assignment problem and a quantity comparison problem, and propose a neural network model of how these problems are solved. The network combines unsupervised and one-layer supervised training. The unsupervised part clusters input features (stimulus + time window) and the supervised part categorizes the resulting clusters. After training, the model shows a good fit with both neural and behavioral properties. New predictions are outlined and links with other cognitive domains are pointed out.

Statistical test for peri-stimulus time histograms in assessing motor neuron activity

2002 ◽  
Vol 40 (4) ◽  
pp. 462-468 ◽  
Author(s):  
J. Ushiba ◽  
Y. Tomita ◽  
Y. Masakado ◽  
Y. Komune ◽  
Y. Muraoka
Keyword(s):  
Motor Neuron ◽  
Statistical Test ◽  
Neuron Activity ◽  
Stimulus Time

The kinetics of the BOLD response depend on inter-stimulus time

NeuroImage ◽  
2005 ◽  
Vol 27 (4) ◽  
pp. 817-823 ◽  
Author(s):  
Kimberlee D. McClure ◽  
Samuel M. McClure ◽  
Marlene C. Richter ◽  
Wolfgang Richter
Keyword(s):  
Bold Response ◽  
Kinetics Of ◽  
Stimulus Time
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