Visually Guided Behavior and Optogenetically Induced Learning in Head-Fixed Flies Exploring a Virtual Landscape

Haberkern, Hannah; Basnak, Melanie A.; Ahanonu, Biafra; Schauder, David M.; Cohen, Jack B.; Bolstad, Mark; Bruns, Christopher; Jayaraman, Vivek

doi:10.1016/j.cub.2019.04.033

Cited by 61 publications

(62 citation statements)

References 90 publications

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“…Compared to pre-stimulus epochs, overall behavioral responses displayed an initial period of slowing (spanning from 0 to 120ms after stimulus offset), followed by increased turning (120ms to 280ms) ( Figure 3E). These dynamics are consistent with known local search behaviors (Haberkern et al 2019;Corfas et al 2019). Despite these population-level patterns, we noted a surprising amount of behavioral heterogeneity amongst optogenetically activated flies, such that the behavior of some optogenetically activated flies overlapped with that of controls ( Figure 3F; Figure S3G).…”

Section: Detecting Behavioral Changes Due To Optogenetic Manipulationsupporting

confidence: 86%

“…Briefly, this method treats spaces and their component points as a set of landmarks for pairwise comparison. To do so, pairs of behavior spaces are scaled to be similar sizes then shifted and rotated to possess the same position and orientation in space 18 . The distance between corresponding points in the two adjusted landmark sets is then calculated using Procrustes distance (reported here as Root Mean Square Error; RMSE).…”

Section: Analyzing Correlated Random Walk Parameter Spacementioning

confidence: 99%

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Flexible analysis of animal behavior via time-resolved manifold embedding

York

Carreira-Rosario

Giocomo

et al. 2020

Preprint

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Understanding 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.

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Section: Detecting Behavioral Changes Due To Optogenetic Manipulationsupporting

confidence: 86%

Section: Analyzing Correlated Random Walk Parameter Spacementioning

confidence: 99%

Flexible analysis of animal behavior via time-resolved manifold embedding

York

Carreira-Rosario

Giocomo

et al. 2020

Preprint

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“…Spatial learning has been studied in tethered flies moving on a treadmill in two-dimensional environments filled with geometrical objects projected on a visual display (14). The same technology has been used to record the neural activity of mice exploring a virtual space (15).…”

Section: Introductionmentioning

confidence: 99%

PiVR: an affordable and versatile closed-loop platform to study unrestrained sensorimotor behavior

Tadres

Louis

2019

Preprint

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Tools enabling closed-loop experiments are crucial to delineate causal relationships between the activity of genetically-labeled neurons and specific behaviors. We developed the Raspberry Pi Virtual Reality system (PiVR) to conduct closed-loop optogenetic stimulation of neural functions in unrestrained animals. PiVR is an experimental platform that operates at hightemporal resolution (>50 Hz) with low latencies (~10 ms), while being affordable (<$500) and easy to build (<6 hours). This tool was designed to be accessible to a wide public, from highschool students to professional researchers studying systems neuroscience. We illustrate the functionality of PiVR by focusing on sensory navigation in response to gradients of chemicals (chemotaxis) and light (phototaxis). We show how Drosophila flies perform negative chemotaxis by modulating their locomotor speed to avoid locations associated with optogenetically-evoked bitter taste. In Drosophila larvae, we use innate positive chemotaxis to compare orientation behavior elicited by real-and virtual-odor gradients with static shapes as well as by turbulent virtual-odor plumes. Finally, we examine how positive phototaxis emerges in zebrafish larvae from the modulation of turning maneuvers during the ascent of virtual white-light gradients. Besides its application to study chemotaxis and phototaxis, PiVR is a versatile tool designed to bolster efforts to map and to functionally characterize neural circuits.

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“…The ability to use memory to return to specific locations for foraging is advantageous for survival. Although recent reports have demonstrated that the fruit flies Drosophila melanogaster are capable of visual cue-driven place learning and idiothetic path integration [1][2][3][4], the depth and flexibility of Drosophila's ability to solve spatial tasks and the underlying neural substrate and genetic basis have not been extensively explored. Here, we show that Drosophila can remember a reward-baited location through reinforcement learning and do so quickly and without requiring vision.…”

mentioning

confidence: 99%

Learning a Spatial Task by Trial and Error in Drosophila

et al. 2019

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Learning a Spatial Task by Trial and Error in Drosophila Highlights d Flies possess ''reward'' neurons with properties similar to those of the vertebrate MFB d Closed-loop stimulation of these neurons enables flies to learn a spatial task d Flies' learning is influenced both by vision and non-visual features in the arena d Learning is enabled by specific DA and ChAT neurons as well as MB and CX in the brain

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Visually Guided Behavior and Optogenetically Induced Learning in Head-Fixed Flies Exploring a Virtual Landscape

Cited by 61 publications

References 90 publications

Flexible analysis of animal behavior via time-resolved manifold embedding

Flexible analysis of animal behavior via time-resolved manifold embedding

PiVR: an affordable and versatile closed-loop platform to study unrestrained sensorimotor behavior

Learning a Spatial Task by Trial and Error in Drosophila

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