Electrical activity of the visual cortex under conditions of change in the levels of monoamines in the brain of animals

1991 ◽  
Vol 21 (1) ◽  
pp. 29-34
Author(s):  
V. V. Borob'ev ◽  
A. A. Gal'chenko ◽  
O. N. Deryugina
Keyword(s):  
Visual Cortex ◽  
Electrical Activity ◽  
The Brain

Oxygenation of the cat primary visual cortex

1999 ◽  
Vol 86 (5) ◽  
pp. 1490-1496 ◽  
Author(s):  
Lissa B. Padnick ◽  
Robert A. Linsenmeier ◽  
Thomas K. Goldstick
Keyword(s):  
Cerebrospinal Fluid ◽  
Visual Cortex ◽  
Electrical Activity ◽  
Primary Visual Cortex ◽  
Tissue Oxygenation ◽  
Fluid Pressure ◽  
Cerebrospinal Fluid Pressure ◽  
The Brain

Tissue [Formula: see text] was measured in the primary visual cortex of anesthetized, artificially ventilated normovolemic cats to examine tissue oxygenation with respect to depth. The method utilized 1) a chamber designed to maintain cerebrospinal fluid pressure and prevent ambient[Formula: see text] from influencing the brain, 2) a microelectrode capable of recording electrical activity as well as local[Formula: see text], and 3) recordings primarily during electrode withdrawal from the cortex rather than during penetrations. Local peaks in the [Formula: see text] profiles were consistent with the presence of numerous vessels. Excluding the superficial 200 μm of the cortex, in which the ambient[Formula: see text] may have influenced tissue[Formula: see text], there was a slight decrease (4.9 Torr/mm cortex) in [Formula: see text] as a function of depth. After all depths and cats were weighted equally, the average [Formula: see text] in six cats was 12.8 Torr, with approximately one-half of the values being ≤10 Torr. The kurtosis of the [Formula: see text] histogram, with all depths and cats weighted equally, was 3.61, and the skewness was 1.70.

Behavior and 40-C/Sec. Electrical Activity in the Brain

1966 ◽  
Vol 19 (3_suppl) ◽  
pp. 1333-1334 ◽  
Author(s):  
Daniel E. Sheer ◽  
Netta W. Grandstaff ◽  
Vernon A. Benignus
Keyword(s):  
Visual Cortex ◽  
Electrical Activity ◽  
Behavioral Response ◽  
The Brain

A 40-c/sec. rhythmic electrical activity occurs in various rhinencephalic structures, auditory and visual cortex of the cat with the acquisition of a behavioral response during learning.

Fit Vs Faint: Assessing Work Causation in “Idiopathic Fall” Cases

2014 ◽  
Vol 19 (5) ◽  
pp. 3-12
Author(s):  
Lorne Direnfeld ◽  
David B. Torrey ◽  
Jim Black ◽  
LuAnn Haley ◽  
Christopher R. Brigham
Keyword(s):  
Blood Flow ◽  
Electrical Activity ◽  
Loss Of Consciousness ◽  
Brain Electrical Activity ◽  
Medical Terminology ◽  
Scientific Analysis ◽  
Medical Causation ◽  
The Individual ◽  
The Brain ◽  
Current Science

Abstract When an individual falls due to a nonwork-related episode of dizziness, hits their head and sustains injury, do workers’ compensation laws consider such injuries to be compensable? Bearing in mind that each state makes its own laws, the answer depends on what caused the loss of consciousness, and the second asks specifically what happened in the fall that caused the injury? The first question speaks to medical causation, which applies scientific analysis to determine the cause of the problem. The second question addresses legal causation: Under what factual circumstances are injuries of this type potentially covered under the law? Much nuance attends this analysis. The authors discuss idiopathic falls, which in this context means “unique to the individual” as opposed to “of unknown cause,” which is the familiar medical terminology. The article presents three detailed case studies that describe falls that had their genesis in episodes of loss of consciousness, followed by analyses by lawyer or judge authors who address the issue of compensability, including three scenarios from Arizona, California, and Pennsylvania. A medical (scientific) analysis must be thorough and must determine the facts regarding the fall and what occurred: Was the fall due to a fit (eg, a seizure with loss of consciousness attributable to anormal brain electrical activity) or a faint (eg, loss of consciousness attributable to a decrease in blood flow to the brain? The evaluator should be able to fully explain the basis for the conclusions, including references to current science.

The Electrical Activity of the Brain

1954 ◽  
Vol 190 (6) ◽  
pp. 54-63 ◽  
Author(s):  
W. Grey Walter
Keyword(s):  
Electrical Activity ◽  
The Brain

scholarly journals Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3345
Author(s):  
Enrico Zero ◽  
Chiara Bersani ◽  
Roberto Sacile
Keyword(s):  
Electrical Activity ◽  
Electrical Stimulus ◽  
Head Movements ◽  
Brain Electrical Activity ◽  
Output Function ◽  
Input Output ◽  
Eeg Signals ◽  
Head Turning ◽  
Identification Technique ◽  
The Brain

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

scholarly journals Decoding the future from past experience: learning shapes predictions in early visual cortex

2015 ◽  
Vol 113 (9) ◽  
pp. 3159-3171 ◽  
Author(s):  
Caroline D. B. Luft ◽  
Alan Meeson ◽  
Andrew E. Welchman ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sequence Structure ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Temporal Structures ◽  
Future Events ◽  
Sensory Predictions ◽  
Actual Stimulus ◽  
The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

Electrical activity of the brain: Mechanisms and interpretation

1983 ◽  
Vol 26 (9) ◽  
pp. 801-828 ◽  
Author(s):  
S M Osovets ◽  
D A Ginzburg ◽  
V S Gurfinkel' ◽  
L P Zenkov ◽  
L P Latash ◽  
...  
Keyword(s):  
Electrical Activity ◽  
The Brain
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