The comparison of long-term decoding performance between low and high frequency local field potentials in macaque monkeys

2014 ◽  
Author(s):  
Yue Li ◽  
Dong Wang ◽  
Qiaosheng Zhang ◽  
Yiwen Wang ◽  
Shaomin Zhang ◽  
...  
Keyword(s):  
Local Field ◽  
High Frequency ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Macaque Monkeys

scholarly journals Ictal onset patterns of local field potentials, high frequency oscillations, and unit activity in human mesial temporal lobe epilepsy

Epilepsia ◽  
2015 ◽  
Vol 57 (1) ◽  
pp. 111-121 ◽  
Author(s):  
Shennan Aibel Weiss ◽  
Catalina Alvarado-Rojas ◽  
Anatol Bragin ◽  
Eric Behnke ◽  
Tony Fields ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Local Field ◽  
Unit Activity ◽  
High Frequency ◽  
Local Field Potentials ◽  
Mesial Temporal Lobe Epilepsy ◽  
Field Potentials ◽  
High Frequency Oscillations ◽  
Mesial Temporal Lobe

scholarly journals Performance Monitoring Local Field Potentials in the Medial Frontal Cortex of Primates: Anterior Cingulate Cortex

2008 ◽  
Vol 99 (2) ◽  
pp. 759-772 ◽  
Author(s):  
Erik E. Emeric ◽  
Joshua W. Brown ◽  
Melanie Leslie ◽  
Pierre Pouget ◽  
Veit Stuphorn ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Local Field ◽  
Performance Monitoring ◽  
Local Field Potentials ◽  
Cingulate Cortex ◽  
Negative Polarity ◽  
Event Related Potential ◽  
Anterior Cingulate ◽  
Field Potentials ◽  
Macaque Monkeys

We describe intracranial local field potentials (LFP) recorded in the anterior cingulate cortex (ACC) of macaque monkeys performing a saccade countermanding task. The most prominent feature at ∼70% of sites was greater negative polarity after errors than after rewarded correct trials. This negative polarity was also evoked in unrewarded correct trials. The LFP evoked by the visual target was much less polarized, and the weak presaccadic modulation was insufficient to control the initiation of saccades. When saccades were cancelled, LFP modulation decreased slightly with the magnitude of response conflict that corresponds to the coactivation of gaze-shifting and -holding neurons estimated from the probability of canceling. However, response time adjustments on subsequent trials were not correlated with LFP polarity on individual trials. The results provide clear evidence that error- and feedback-related, but not conflict-related, signals are carried by the LFP in the macaque ACC. Finding performance monitoring field potentials in the ACC of macaque monkeys establishes a bridge between event-related potential and functional brain-imaging studies in humans and neurophysiology studies in non-human primates.

scholarly journals Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson’s Disease

2016 ◽  
Vol 6 (4) ◽  
pp. 57 ◽  
Author(s):  
Sara Hanrahan ◽  
Joshua Nedrud ◽  
Bradley Davidson ◽  
Sierra Farris ◽  
Monique Giroux ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Local Field ◽  
Local Field Potentials ◽  
Field Potentials

scholarly journals Semisupervised definition of hippocampal ripples

2020 ◽  
Author(s):  
Yusuke Watanabe ◽  
Mami Okada ◽  
Yuji Ikegaya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Field ◽  
High Frequency ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Animal Movement ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Weakly Supervised

AbstractHippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials, and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the “ripples” could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. In addition, local field potentials are often influenced by myoelectric noise arising from animal movement, making it difficult to distinguish ripples from high-frequency noises. To overcome these problems, we extracted ripple candidates under few constraints and labeled them as binary or stochastic “true” or “false” ripples using Gaussian mixed model clustering and a deep convolutional neural network in a weakly supervised fashion. Our automatic method separated ripples and myoelectric noise and was able to detect ripples even when the animals were moving. Moreover, we confirmed that a convolutional neural network was able to detect ripples defined by our method. Leave-one-animal-out cross-validation estimated the area under the precision-recall curve for ripple detection to be 0.72. Finally, our model establishes an appropriate threshold for the ripple magnitude in the case of the conventional detection of ripples.

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