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.

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 TREBLE: a generalizable framework for high-throughput behavioral analysis

2020 ◽  
Author(s):  
Ryan A. York ◽  
Lisa M. Giocomo ◽  
Thomas R. Clandinin
Keyword(s):  
Neural Activity ◽  
Large Datasets ◽  
Complex Structures ◽  
Medial Entorhinal Cortex ◽  
Time Resolved ◽  
Behavioral Dynamics ◽  
Flexible Framework ◽  
Freely Moving ◽  
Using Data ◽  
And Behavior

AbstractUnderstanding the relationships between neural activity and behavior represents a critical challenge, one that requires generalizable statistical tools that can capture complex structures within large datasets. We developed Time-REsolved BehavioraL Embedding (TREBLE), a flexible method for analyzing behavioral data from freely moving animals. Using data from synthetic trajectories, fruit flies, and mice we show how TREBLE can capture both continuous and discrete behavioral dynamics, can uncover behavioral variation across individuals, and can detect the effects of optogenetic perturbation in an unbiased fashion. By applying TREBLE to the freely moving mouse, and medial entorhinal cortex (MEC) recordings, we show that nearly all MEC neurons encode information relevant to specific movement patterns, expanding our understanding of how navigation is related to the execution of locomotion. Thus, TREBLE provides a flexible framework for describing the structure of complex behaviors and their relationships to neural activity.

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 Simultaneous optogenetic manipulation and calcium imaging in freely movingC. elegans

2013 ◽  
Author(s):  
Frederick B. Shipley ◽  
Christopher M. Clark ◽  
Mark J. Alkema ◽  
Andrew M. Leifer
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Neural Circuits ◽  
Neural Response ◽  
Sensory Stimuli ◽  
Systems Neuroscience ◽  
C Elegans ◽  
Fundamental Goal ◽  
Freely Moving ◽  
Interneuron Activity

A fundamental goal of systems neuroscience is to probe the dynamics of neural activity that drive behavior. Here we present an instrument to simultaneously manipulate neural activity via Channelrhodopsin, monitor neural response via GCaMP3, and observes behavior in freely moving C. elegans. We use the instrument to directly observe the relation between sensory stimuli, interneuron activity and locomotion in the mechanosensory circuit. Now published as: Front Neural Circuits 8:28, doi:10.3389/fncir.2014.00028

Continuous long‐term recording and triggering of brain neurovascular activity and behavior in freely moving rodents

2021 ◽  
Author(s):  
Dmitri N Yousef Yengej ◽  
Isabella Ferando ◽  
Gayane Kechechyan ◽  
Sinifunanya E Nwaobi ◽  
Shrayes Raman ◽  
...  
Keyword(s):  
Freely Moving ◽  
And Behavior

scholarly journals Auditory-induced neural dynamics in sensory-motor circuitry predict learned temporal and sequential statistics of birdsong

2016 ◽  
Vol 113 (34) ◽  
pp. 9641-9646 ◽  
Author(s):  
Kristofer E. Bouchard ◽  
Michael S. Brainard
Keyword(s):  
Neural Activity ◽  
Neural Dynamics ◽  
Maximal Response ◽  
Sequential Structure ◽  
Sensory Motor ◽  
Future Events ◽  
Song Production ◽  
Probabilistic Sequences ◽  
Stimulus Offset ◽  
And Behavior

Predicting future events is a critical computation for both perception and behavior. Despite the essential nature of this computation, there are few studies demonstrating neural activity that predicts specific events in learned, probabilistic sequences. Here, we test the hypotheses that the dynamics of internally generated neural activity are predictive of future events and are structured by the learned temporal–sequential statistics of those events. We recorded neural activity in Bengalese finch sensory-motor area HVC in response to playback of sequences from individuals’ songs, and examined the neural activity that continued after stimulus offset. We found that the strength of response to a syllable in the sequence depended on the delay at which that syllable was played, with a maximal response when the delay matched the intersyllable gap normally present for that specific syllable during song production. Furthermore, poststimulus neural activity induced by sequence playback resembled the neural response to the next syllable in the sequence when that syllable was predictable, but not when the next syllable was uncertain. Our results demonstrate that the dynamics of internally generated HVC neural activity are predictive of the learned temporal–sequential structure of produced song and that the strength of this prediction is modulated by uncertainty.

scholarly journals Chronically implanted Neuropixels probes enable high-yield recordings in freely moving mice

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Ashley L Juavinett ◽  
George Bekheet ◽  
Anne K Churchland
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
High Yield ◽  
High Signal ◽  
Signal To Noise ◽  
Novel Device ◽  
Approaches To Study ◽  
Freely Moving

The advent of high-yield electrophysiology using Neuropixels probes is now enabling researchers to simultaneously record hundreds of neurons with remarkably high signal to noise. However, these probes have not been well-suited to use in freely moving mice. It is critical to study neural activity in unrestricted animals for many reasons, such as leveraging ethological approaches to study neural circuits. We designed and implemented a novel device that allows Neuropixels probes to be customized for chronically implanted experiments in freely moving mice. We demonstrate the ease and utility of this approach in recording hundreds of neurons during an ethological behavior across weeks of experiments. We provide the technical drawings and procedures for other researchers to do the same. Importantly, our approach enables researchers to explant and reuse these valuable probes, a transformative step which has not been established for recordings with any type of chronically-implanted probe.

scholarly journals Predicting natural behavior from whole-brain neural dynamics

2018 ◽  
Author(s):  
Monika Scholz ◽  
Ashley N Linder ◽  
Francesco Randi ◽  
Anuj K Sharma ◽  
Xinwei Yu ◽  
...  
Keyword(s):  
Linear Combination ◽  
Linear Model ◽  
Neural Activity ◽  
Single Neuron ◽  
Neural Dynamics ◽  
Fictive Locomotion ◽  
Natural Behavior ◽  
C Elegans ◽  
Predicting Behavior ◽  
Freely Moving

AbstractWe record calcium activity from the majority of head neurons in freely moving C. elegans to reveal where and how natural behavior is encoded in a compact brain. We find that a sparse subset of neurons distributed throughout the head encode locomotion. A linear combination of these neurons’ activity predicts the animal’s velocity and body curvature and is sufficient to infer its posture. This sparse linear model outperforms single neuron or PCA models at predicting behavior. Among neurons important for the prediction are well-known locomotory neurons, such as AVA, as well as neurons not traditionally associated with locomotion. We compare neural activity of the same animal during unrestrained movement and during immobilization and find large differences between brain-wide neural dynamics during real and fictive locomotion.One Sentence SummaryC. elegans behavior is predicted from neural activity.

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