Olfactory-Evoked Activity Assay for Larval Zebrafish

Experience, circuit dynamics, and forebrain recruitment in larval zebrafish prey capture

eLife ◽

10.7554/elife.56619 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Claire S Oldfield ◽

Irene Grossrubatscher ◽

Mario Chávez ◽

Adam Hoagland ◽

Alex R Huth ◽

...

Keyword(s):

Prey Capture ◽

Visual Area ◽

Live Prey ◽

Larval Zebrafish ◽

Retinotopic Maps ◽

Trigger Activity ◽

Brain Circuitry ◽

Evoked Activity ◽

And Behavior ◽

Motor Areas

Experience influences behavior, but little is known about how experience is encoded in the brain, and how changes in neural activity are implemented at a network level to improve performance. Here we investigate how differences in experience impact brain circuitry and behavior in larval zebrafish prey capture. We find that experience of live prey compared to inert food increases capture success by boosting capture initiation. In response to live prey, animals with and without prior experience of live prey show activity in visual areas (pretectum and optic tectum) and motor areas (cerebellum and hindbrain), with similar visual area retinotopic maps of prey position. However, prey-experienced animals more readily initiate capture in response to visual area activity and have greater visually-evoked activity in two forebrain areas: the telencephalon and habenula. Consequently, disruption of habenular neurons reduces capture performance in prey-experienced fish. Together, our results suggest that experience of prey strengthens prey-associated visual drive to the forebrain, and that this lowers the threshold for prey-associated visual activity to trigger activity in motor areas, thereby improving capture performance.

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Spontaneous and evoked activity patterns diverge over development

eLife ◽

10.7554/elife.61942 ◽

2021 ◽

Vol 10 ◽

Author(s):

Lilach Avitan ◽

Zac Pujic ◽

Jan Mölter ◽

Shuyu Zhu ◽

Biao Sun ◽

...

Keyword(s):

Spontaneous Activity ◽

Neural Activity ◽

Activity Patterns ◽

Higher Dimension ◽

Immature Brain ◽

Geometric Distance ◽

Larval Zebrafish ◽

Emerging Patterns ◽

Evoked Activity ◽

Bayesian Prior

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies properties refined in similar ways. However both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development evoked activity was of higher dimension than spontaneous activity. Thus spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

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Bayesian inference of neuronal ensembles

10.1101/452557 ◽

2018 ◽

Author(s):

Giovanni Diana ◽

Thomas T. J. Sainsbury ◽

Martin P. Meyer

Keyword(s):

Simultaneous Estimation ◽

Population Activity ◽

Neuronal Ensembles ◽

Larval Zebrafish ◽

Neural Ensembles ◽

Ensemble Structure ◽

Evoked Activity ◽

High Dimensional Datasets ◽

The Brain

AbstractIn many areas of the brain, both spontaneous and stimulus-evoked activity can manifest as synchronous activation of neuronal ensembles. The characterization of ensemble structure and dynamics provides important insights into how brain computations are distributed across neural networks. The proliferation of experimental techniques for recording the activity of neuronal ensembles calls for a comprehensive statistical method to describe, analyze and characterize these high dimensional datasets. Here we introduce a generative model of synchronous activity to describe spontaneously active neural ensembles. Unlike existing methods, our analysis provides a simultaneous estimation of ensemble composition, dynamics and statistical features of these neural populations, including ensemble noise and activity rate. We also introduce ensemble “coherence” as a measure of within-ensemble synchrony. We have used our method to characterize population activity throughout the tectum of larval zebrafish, allowing us to make statistical inference on the spatiotemporal organization of tectal ensembles, their composition and the logic of their interactions. We have also applied our method to functional imaging and neuropixels recordings from the mouse, allowing us to relate the activity of identified ensembles to specific behaviours such as running or changes in pupil diameter.

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