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Journal ArticleDOI

Computer assisted unit data acquisition/reduction

Richard J Radna, +1 more
- 01 Feb 1978 - 
- Vol. 44, Iss: 2, pp 239-242
TLDR
This system digitally processes uninterrupted, continuous unit data with attention to waveform detail with a great economy of the user's time because the computer performs all data processing.
About
This article is published in Electroencephalography and Clinical Neurophysiology.The article was published on 1978-02-01. It has received 9 citations till now. The article focuses on the topics: Data acquisition & Computer graphics.

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Journal ArticleDOI

Vagal elicitation of respiratory-type and other unit responses in basal limbic structures of squirrel monkeys.

TL;DR: Basal limbic structures (insula, amygdala, hippocampus and surrounding areas) were explored for unit responses to vagal volleys in awake, sitting, squirrel monkeys and it can be concluded that the discharge of respiratory-type units is not dependent on olfactory inputs.
Journal ArticleDOI

Spike separation in multiunit records: a multivariate analysis of spike descriptive parameters.

TL;DR: Variables describing unit discharges from multiunit extracellular records were treated, using multivariate statistical analysis for further automatic spike separation, and three parameters turned out to be sufficient for spike discrimination.
Journal ArticleDOI

Single unit components of the hypothalamic multiunit electrical activity associated with the central signal generator that directs the pulsatile secretion of gonadotropic hormones

TL;DR: The results indicate that the MUA volleys associated with the activity of the gonadotropin-releasing hormone pulse generator represent the simultaneous increase in firing rate of some individual hypothalamic neurons and the decrease in the frequency of others.
Journal ArticleDOI

Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW

TL;DR: An algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions is presented, confirming that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high- Speed digital records.