Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation

Wang, Bo; Torok, Zsofia; Duffy, Alison; Bell, David G.; Wongso, Shelyn; Velho, Tarciso A. F.; Fairhall, Adrienne L.; Lois, Carlos

doi:10.1038/s41593-024-01630-6

Cited by 3 publications

References 75 publications

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Resilience of A Learned Motor Behavior After Chronic Disruption of Inhibitory Circuits

Torok

Luebbert

Feldman

et al. 2023

Preprint

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Maintaining motor skills is crucial for an animal's survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced inhibitory neurons, which altered brain activity and severely perturbed a complex learned behavior for around two months, after which it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics resulting from chronic inhibition loss, while subsequent recovery of the behavior occurred despite partial normalization of brain activity. Single-cell RNA sequencing revealed that chronic silencing of interneurons leads to elevated levels of microglia and MHC I. These experiments demonstrate that the adult brain can overcome extended periods of drastic abnormal activity. The reactivation of mechanisms employed during learning, including offline neuronal dynamics and upregulation of MHC I and microglia, could facilitate the recovery process following perturbation of the adult brain. These findings indicate that some forms of brain plasticity may persist in a dormant state in the adult brain, until they are recruited for circuit restoration.

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Resilience of A Learned Motor Behavior After Chronic Disruption of Inhibitory Circuits

Torok

Luebbert

Feldman

et al. 2023

Preprint

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Discovering plasticity rules that organize and maintain neural circuits

Bell,

Duffy,

Fairhall

2024

Preprint

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Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence- generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Oja's rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

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Analysis methods for large-scale neuronal recordings

Stringer,

Pachitariu

2024

Science

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Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

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Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation

Abstract: Zso a Torok caltech https://orcid.org/0000-0001-6298-9940 alison duffy university of wahsington david bell university of washington shelyn wongso caltech tarciso velho caltech

Cited by 3 publications

References 75 publications

Resilience of A Learned Motor Behavior After Chronic Disruption of Inhibitory Circuits

Resilience of A Learned Motor Behavior After Chronic Disruption of Inhibitory Circuits

Discovering plasticity rules that organize and maintain neural circuits

Analysis methods for large-scale neuronal recordings

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