A spiral attractor network drives rhythmic locomotion

Bruno, Angela M.; Frost, William N.; Humphries, Mark D.

doi:10.1101/104562

Cited by 13 publications

(14 citation statements)

References 73 publications

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“…We delineated a neighborhood around each point in behavior space (see Methods) and determined the amount of time between visits to each neighborhood. This was then represented as the proportion of all neighborhoods that were revisited (Bruno et al 2017). The distribution of these values was consistent across replicates within a given window size but varied across sizes ( Figure 1I).…”

Section: Calibrating Treblementioning

confidence: 78%

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: Calibrating Treblementioning

confidence: 78%

Flexible analysis of animal behavior via time-resolved manifold embedding

York

Carreira-Rosario

Giocomo

et al. 2020

Preprint

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“…(B) Temporal evolution of brain states during correct versus incorrect trials lie on separate cyclic manifolds, visualized in a 2D map; single trials (thin lines) and trial averages (thick lines); arrow: direction of temporal evolution. Manifold separation measured with Hausdorff distance (Bruno et al, 2017), ***p < 0.0001, Wilcoxon rank sum test. (C) Performance-dependent bifurcation of brain states before turn initiation.…”

Section: Data and Software Availabilitymentioning

confidence: 99%

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level

Lin

Manley

Helmreich

et al. 2020

Cell

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Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations towards mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (>10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.

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“…However, some key differences have to be noted: for example, in large spiking cortical networks, neurons with similar anatomical or functional features can be represented in great quantities, but in single trials the participation of individual neurons is often sparse and variable; both have substantial impact on the properties of the underlying population code [ 53 ]. Even in larger invertebrates, for example in an Aplysia motor ganglion, rhythmic motor patterns arise as an emergent property of neuronal populations [ 50 ]. Unlike in cortex, in the compacted worm brain, most neuronal classes are represented only by two bilaterally symmetric members and their recruitment to the neuronal ensemble is very reliable in single trials.…”

Section: Putting Behaviour First: Towards a New Understanding Of Sensmentioning

confidence: 99%

Sensorimotor integration in Caenorhabditis elegans : a reappraisal towards dynamic and distributed computations

Kaplan

Nichols

Zimmer

2018

Phil. Trans. R. Soc. B

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The nematode Caenorhabditis elegans is a tractable model system to study locomotion, sensory navigation and decision-making. In its natural habitat, it is thought to navigate complex multisensory environments in order to find food and mating partners, while avoiding threats like predators or toxic environments. While research in past decades has shed much light on the functions and mechanisms of selected sensory neurons, we are just at the brink of understanding how sensory information is integrated by interneuron circuits for action selection in the worm. Recent technological advances have enabled whole-brain Ca2+ imaging and Ca2+ imaging of neuronal activity in freely moving worms. A common principle emerging across multiple studies is that most interneuron activities are tightly coupled to the worm's instantaneous behaviour; notably, these observations encompass neurons receiving direct sensory neuron inputs. The new findings suggest that in the C. elegans brain, sensory and motor representations are integrated already at the uppermost sensory processing layers. Moreover, these results challenge a perhaps more intuitive view of sequential feed-forward sensory pathways that converge onto premotor interneurons and motor neurons. We propose that sensorimotor integration occurs rather in a distributed dynamical fashion. In this perspective article, we will explore this view, discuss the challenges and implications of these discoveries on the interpretation and design of neural activity experiments, and discuss possible functions. Furthermore, we will discuss the broader context of similar findings in fruit flies and rodents, which suggest generalizable principles that can be learnt from this amenable nematode model organism.This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

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A spiral attractor network drives rhythmic locomotion

Cited by 13 publications

References 73 publications

Flexible analysis of animal behavior via time-resolved manifold embedding

Flexible analysis of animal behavior via time-resolved manifold embedding

Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level

Sensorimotor integration in Caenorhabditis elegans : a reappraisal towards dynamic and distributed computations

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