scholarly journals Automated long-term recording and analysis of neural activity in behaving animals

2015 ◽  
Author(s):  
Ashesh K. Dhawale ◽  
Rajesh Poddar ◽  
Evi Kopelowitz ◽  
Valentin Normand ◽  
Steffen B. E. Wolff ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Unit Activity ◽  
Neural Activity ◽  
Single Unit ◽  
Single Units ◽  
Neural Dynamics ◽  
Single Unit Activity ◽  
Freely Moving ◽  
And Behavior

SummaryAddressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons during experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving animals. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.HighlightsWe record neural activity and behavior in rodents continuously (24/7) over monthsAn automated spike-sorting method isolates and tracks single units over many weeksNeural dynamics and motor representations are highly stable over long timescalesNeurons cluster into functional groups based on their activity in different stateseTOC BlurbDhawale et al. describe experimental infrastructure for recording neural activity and behavior continuously over months in freely moving rodents. A fully automated spike-sorting algorithm allows single units to be tracked over weeks of recording. Recordings from motor cortex and striatum revealed a remarkable long-term stability in both single unit activity and network dynamics.

scholarly journals Automated long-term recording and analysis of neural activity in behaving animals

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ashesh K Dhawale ◽  
Rajesh Poddar ◽  
Steffen BE Wolff ◽  
Valentin A Normand ◽  
Evi Kopelowitz ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Large Datasets ◽  
Neural Dynamics ◽  
Single Unit Activity ◽  
Firing Rates ◽  
Freely Moving ◽  
Automated Platform ◽  
And Behavior

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

A method for long-term artifact-free recording of single unit activity in freely moving, eating and drinking animals

1983 ◽  
Vol 7 (1) ◽  
pp. 43-47 ◽  
Author(s):  
Kazuo Sasaki ◽  
Taketoshi Ono ◽  
Hitoo Nishino ◽  
Masaji Fukuda ◽  
Ken-Ichiro Muramoto
Keyword(s):  
Unit Activity ◽  
Single Unit ◽  
Single Unit Activity ◽  
Freely Moving

scholarly journals Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics

2015 ◽  
Vol 114 (3) ◽  
pp. 1500-1512 ◽  
Author(s):  
Sagi Perel ◽  
Patrick T. Sadtler ◽  
Emily R. Oby ◽  
Stephen I. Ryu ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Local Field ◽  
Unit Activity ◽  
Single Unit ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Local Field Potentials ◽  
Single Unit Activity ◽  
Field Potentials ◽  
Threshold Crossing

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the “beta band” (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.

Single unit activity of dopaminergic neurons in freely moving cats

Life Sciences ◽  
1981 ◽  
Vol 29 (14) ◽  
pp. 1435-1442 ◽  
Author(s):  
George F. Steinfels ◽  
James Heym ◽  
Barry L. Jacobs
Keyword(s):  
Unit Activity ◽  
Dopaminergic Neurons ◽  
Single Unit ◽  
Single Unit Activity ◽  
Freely Moving ◽  
Freely Moving Cats
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