Recording Neuronal Spike Activity During Transcranial Direct Current Stimulation1

2015 ◽  
Vol 9 (2) ◽  
Author(s):  
Nathaniel J. Faber ◽  
Filippo Agnesi ◽  
Matthew D. Johnson
Keyword(s):  
Direct Current ◽  
Spike Activity ◽  
Neuronal Spike

Neuronal spike activity in the nucleus accumbens of behaving rats during ethanol self-administration

1999 ◽  
Vol 817 (1-2) ◽  
pp. 172-184 ◽  
Author(s):  
Patricia H Janak ◽  
Jing-Yu Chang ◽  
Donald J Woodward
Keyword(s):  
Nucleus Accumbens ◽  
Spike Activity ◽  
Self Administration ◽  
Neuronal Spike

scholarly journals Parallel Optical Control of Spatiotemporal Neuronal Spike Activity Using High-Speed Digital Light Processing

2011 ◽  
Vol 5 ◽  
Author(s):  
Jason Jerome ◽  
Robert C. Foehring ◽  
William E. Armstrong ◽  
William J. Spain ◽  
Detlef H. Heck
Keyword(s):  
Spike Activity ◽  
High Speed ◽  
Optical Control ◽  
Digital Light Processing ◽  
Neuronal Spike

A 4-parameter discriminator of neuronal spike activity

1976 ◽  
Vol 14 (6) ◽  
pp. 636-643 ◽  
Author(s):  
P. Zalud ◽  
M. Indra ◽  
V. Straka ◽  
I. Krekule
Keyword(s):  
Spike Activity ◽  
Neuronal Spike

scholarly journals Relationships between spike-free local field potentials and spike timing in human temporal cortex

2012 ◽  
Vol 107 (7) ◽  
pp. 1808-1821 ◽  
Author(s):  
Stavros Zanos ◽  
Theodoros P. Zanos ◽  
Vasilis Z. Marmarelis ◽  
George A. Ojemann ◽  
Eberhard E. Fetz
Keyword(s):  
Local Field ◽  
Spike Activity ◽  
Cell Activity ◽  
Temporal Cortex ◽  
Local Field Potentials ◽  
Spike Timing ◽  
Synaptic Activity ◽  
Linear Component ◽  
Field Potentials ◽  
Neuronal Spike

Intracortical recordings comprise both fast events, action potentials (APs), and slower events, known as local field potentials (LFPs). Although it is believed that LFPs mostly reflect local synaptic activity, it is unclear which of their signal components are most closely related to synaptic potentials and would therefore be causally related to the occurrence of individual APs. This issue is complicated by the significant contribution from AP waveforms, especially at higher LFP frequencies. In recordings of single-cell activity and LFPs from the human temporal cortex, we computed quantitative, nonlinear, causal dynamic models for the prediction of AP timing from LFPs, at millisecond resolution, before and after removing AP contributions to the LFP. In many cases, the timing of a significant number of single APs could be predicted from spike-free LFPs at different frequencies. Not surprisingly, model performance was superior when spikes were not removed. Cells whose activity was predicted by the spike-free LFP models generally fell into one of two groups: in the first group, neuronal spike activity was associated with specific phases of low LFP frequencies, lower spike activity at high LFP frequencies, and a stronger linear component in the spike-LFP model; in the second group, neuronal spike activity was associated with larger amplitude of high LFP frequencies, less frequent phase locking, and a stronger nonlinear model component. Spike timing in the first group was better predicted by the sign and level of the LFP preceding the spike, whereas spike timing in the second group was better predicted by LFP power during a certain time window before the spike.

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