scholarly journals In Vivo Recording of Single-Unit Activity during Singing in Zebra Finches

2014 ◽  
Vol 2014 (12) ◽  
pp. pdb.prot084624 ◽  
Author(s):  
Tatsuo S. Okubo ◽  
Emily L. Mackevicius ◽  
Michale S. Fee
Keyword(s):  
Unit Activity ◽  
Single Unit ◽  
Zebra Finches ◽  
Single Unit Activity

scholarly journals Effects of Methamphetamine on Single Unit Activity in Rat Medial Prefrontal Cortex In Vivo

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Jinhwa Jang ◽  
Hee-Jin Ha ◽  
Yun Bok Kim ◽  
Young-Ki Chung ◽  
Min Whan Jung
Keyword(s):  
Prefrontal Cortex ◽  
Firing Rate ◽  
Medial Prefrontal Cortex ◽  
Unit Activity ◽  
Single Unit ◽  
Discharge Rate ◽  
Rate Change ◽  
Psychotic Symptoms ◽  
Single Unit Activity

To investigate how neuronal activity in the prefrontal cortex changes in an animal model of schizophrenia, we recorded single unit activity in the medial prefrontal cortex of urethane-anesthetized and awake rats following methamphetamine (MA) administration. Systemic MA injection (4 mg/kg, IP) induced inconsistent changes, that is, both enhancement and reduction, in unit discharge rate, with a subset of neurons transiently (<30 min) elevating their activities. The direction of firing rate change was poorly predicted by the mean firing rate or the degree of burst firing during the baseline period. Also, simultaneously recorded units showed opposite directions of firing rate change, indicating that recording location is a poor predictor of the direction of firing rate change. These results raise the possibility that systemic MA injection induces random bidirectional changes in prefrontal cortical unit activity, which may underlie some of MA-induced psychotic symptoms.

scholarly journals Efficient sorting of single-unit activity from midbrain cells using KiloSort is as accurate as manual sorting

2018 ◽  
Author(s):  
Madeleine Allen ◽  
Tara Chowdhury ◽  
Meredyth Wegener ◽  
Bita Moghaddam
Keyword(s):  
Unit Activity ◽  
Background Noise ◽  
Single Unit ◽  
Principal Component ◽  
Substantia Nigra Pars Compacta ◽  
Single Unit Activity ◽  
Manual Sorting ◽  
Human Operators ◽  
Multiple Cells

AbstractExtracting single-unit activity from in vivo extracellular neural electrophysiology data requires sorting spikes from background noise and action potentials from multiple cells in order to identify the activity of individual neurons. Typically this has been achieved by algorithms that employ principal component analyses followed by manual allocation of spikes to individual clusters based on visual inspection of the waveform shape. This method of manual sorting can give varying results between human operators and is highly time-consuming, especially in recordings with higher levels of background noise. To address these problems, automatic sorting algorithms have begun to gain popularity as viable methods for sorting electrophysiological data, although little is known about the use of these algorithms with neural data from midbrain recordings. KiloSort is a relatively new algorithm that automatically clusters raw data which can then be manually curated. In this report, we compare results of manually-sorted and KiloSort-processed recordings from the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). Sorting with KiloSort required substantially less time to complete, while yielding comparable and consistent results. We conclude that the use of KiloSort to identify single units from multi-channel recording in the VTA and SNc is accurate and efficient.

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