JOURNAL ARTICLE

Nonlinear Network Time-Series Forecasting Using Redundancy Reduction.

Pramod Lakshmi NarasimhaM.T. ManryChanghua Yu

Year: 2005 Journal:   The Florida AI Research Society Vol: 11 (8)Pages: 834-835

Abstract

The relationship between the level of expression of tetrodotoxin-sensitive (TTX-S) and tetrodotoxin-resistant (TTX-R) sodium currents and the occurrence of two kinetically different ATP-induced currents in rat dorsal root ganglion neurons was studied. ATP evoked two distinct types of currents, one with fast activation and desensitization (I-fast) and the other with slow and persistent development (I-slow). In all TTX-S cells which expressed solely TTX-S sodium currents ATP evoked I-fast. The other cells expressed a variable proportion of TTX-S and TTX-R sodium currents. Only 15% of these TTX-R+S cells responded to ATP with I-fast. I-slow was evoked in both cell types but the magnitude of response was much greater in TTX-R+S cells. This result suggests that a different array of ion channels is equipped in different types of sensory neurons.

Keywords:
Redundancy (engineering) Uncompressed video Computer science Time series Series (stratigraphy) Nonlinear system Reduction (mathematics) Data mining Algorithm Artificial intelligence Machine learning Mathematics

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Topics

Energy Load and Power Forecasting
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Time Series Analysis and Forecasting
Physical Sciences →  Computer Science →  Signal Processing
Neural Networks and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence

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