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Torok Z, Luebbert L, Feldman J, Duffy A, Nevue AA, Wongso S, Mello CV, Fairhall A, Pachter L, Gonzalez WG, Lois C. Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons. bioRxiv 2023:2023.05.17.541057. [PMID: 37292888 PMCID: PMC10245685 DOI: 10.1101/2023.05.17.541057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
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 a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.
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Affiliation(s)
- Zsofia Torok
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | - Jordan Feldman
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | | | | | - Shelyn Wongso
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | | | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology; Pasadena, CA, USA
| | - Walter G. Gonzalez
- Department of Physiology, University of San Francisco; San Francisco, CA, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
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FERRÉ JOHN, ROKEM ARIEL, BUFFALO ELIZABETHA, KUTZ JNATHAN, Fairhall A. Non-Stationary Dynamic Mode Decomposition. bioRxiv 2023:2023.08.08.552333. [PMID: 37609201 PMCID: PMC10441341 DOI: 10.1101/2023.08.08.552333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, DMD methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition (NS-DMD), that generalizes DMD by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.
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Affiliation(s)
- JOHN FERRÉ
- Physics Department, University of Washington, Seattle, Washington 98195, USA
| | - ARIEL ROKEM
- Psychology Department and eScience Institute, University of Washington, Seattle, Washington 98195, USA
| | - ELIZABETH A. BUFFALO
- Department of Physiology and Biophysics, University of Washington School of Medicine, Washington National Primate Research Center, Seattle Washington 98195, USA
| | - J. NATHAN KUTZ
- Applied Mathematics and Electrical and Computer Engineering Department, University of Washington, Seattle, Washington 98195, USA
| | - Adrienne Fairhall
- Physiology and Biophysics Department, University of Washington, Seattle, Washington 98195, USA
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Abstract
Interview with Adrienne Fairhall, who studies the relationship between neuronal circuitry and the functional algorithms of computation at the University of Washington.
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Affiliation(s)
- Adrienne Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195-7290, USA.
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Lagache T, Hanson A, Pérez-Ortega JE, Fairhall A, Yuste R. Tracking calcium dynamics from individual neurons in behaving animals. PLoS Comput Biol 2021; 17:e1009432. [PMID: 34624016 PMCID: PMC8528277 DOI: 10.1371/journal.pcbi.1009432] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/20/2021] [Accepted: 09/08/2021] [Indexed: 12/03/2022] Open
Abstract
Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.
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Affiliation(s)
- Thibault Lagache
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
| | - Alison Hanson
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, New York, United States of America
| | - Jesús E Pérez-Ortega
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Adrienne Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America
- UW Computational Neuroscience Center, University of Washington, Seattle, Washington, United States of America
| | - Rafael Yuste
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
- Donostia International Physics Center, San Sebastian, Spain
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van Breugel F, Riffell J, Fairhall A, Dickinson MH. Mosquitoes Use Vision to Associate Odor Plumes with Thermal Targets. Curr Biol 2015; 25:2123-9. [PMID: 26190071 DOI: 10.1016/j.cub.2015.06.046] [Citation(s) in RCA: 155] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 06/17/2015] [Accepted: 06/19/2015] [Indexed: 01/06/2023]
Abstract
All moving animals, including flies, sharks, and humans, experience a dynamic sensory landscape that is a function of both their trajectory through space and the distribution of stimuli in the environment. This is particularly apparent for mosquitoes, which use a combination of olfactory, visual, and thermal cues to locate hosts. Mosquitoes are thought to detect suitable hosts by the presence of a sparse CO₂ plume, which they track by surging upwind and casting crosswind. Upon approach, local cues such as heat and skin volatiles help them identify a landing site. Recent evidence suggests that thermal attraction is gated by the presence of CO₂, although this conclusion was based experiments in which the actual flight trajectories of the animals were unknown and visual cues were not studied. Using a three-dimensional tracking system, we show that rather than gating heat sensing, the detection of CO₂ actually activates a strong attraction to visual features. This visual reflex guides the mosquitoes to potential hosts where they are close enough to detect thermal cues. By experimentally decoupling the olfactory, visual, and thermal cues, we show that the motor reactions to these stimuli are independently controlled. Given that humans become visible to mosquitoes at a distance of 5-15 m, visual cues play a critical intermediate role in host localization by coupling long-range plume tracking to behaviors that require short-range cues. Rather than direct neural coupling, the separate sensory-motor reflexes are linked as a result of the interaction between the animal's reactions and the spatial structure of the stimuli in the environment.
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Affiliation(s)
- Floris van Breugel
- Division of Biology and Bioengineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA.
| | - Jeff Riffell
- Department of Biology, University of Washington, Box 351800, Seattle, WA 98195, USA
| | - Adrienne Fairhall
- Department of Biophysics, University of Washington, 1705 Northeast Pacific Street, HSB G424, Box 357290, Seattle, WA 98195, USA
| | - Michael H Dickinson
- Division of Biology and Bioengineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA.
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Abstract
Advances in experimental techniques, including behavioral paradigms using rich stimuli under closed loop conditions and the interfacing of neural systems with external inputs and outputs, reveal complex dynamics in the neural code and require a revisiting of standard concepts of representation. High-throughput recording and imaging methods along with the ability to observe and control neuronal subpopulations allow increasingly detailed access to the neural circuitry that subserves neural representations and the computations they support. How do we harness theory to build biologically grounded models of complex neural function?
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Affiliation(s)
- Adrienne Fairhall
- Department of Physiology and Biophysics, University of Washington, 1705 NE Pacific St., HSB G424, Box 357290, Seattle, WA 98195-7290, USA.
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Fairhall A, Shea-Brown E, Barreiro A. Information theoretic approaches to understanding circuit function. Curr Opin Neurobiol 2012; 22:653-9. [PMID: 22795220 DOI: 10.1016/j.conb.2012.06.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Revised: 06/19/2012] [Accepted: 06/22/2012] [Indexed: 11/16/2022]
Abstract
The analysis of stimulus/response patterns using information theoretic approaches requires the full probability distribution of stimuli and response. Recent progress in using information-based tools to understand circuit function has advanced understanding of neural coding at the single cell and population level. In advances over traditional reverse correlation approaches, the determination of receptive fields using information as a metric has allowed novel insights into stimulus representation and transformation. The application of maximum entropy methods to population codes has opened a rich exploration of the internal structure of these codes, revealing stimulus-driven functional connectivity. We speculate about the prospects and limitations of information as a general tool for dissecting neural circuits and relating their structure and function.
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Affiliation(s)
- Adrienne Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195-7290, USA.
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Abstract
The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that relates stimulus to firing probability. In many sensory systems, these two components of the coding strategy are found to adapt to changes in the statistics of the inputs in such a way as to improve information transmission. Here, we show for two simple neuron models how feature selectivity as captured by the spike-triggered average depends on both the parameters of the model and the statistical characteristics of the input.
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Affiliation(s)
- Michael Famulare
- University of Washington, Department of Physics, Seattle, WA 98195-1560, USA.
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Abstract
Adaptation is a hallmark of sensory function. Adapting optimally requires matching the dynamics of adaptation to those of changes in the stimulus distribution. Here we show that the dynamics of adaptation in the responses of mouse retinal ganglion cells depend on stimulus history. We hypothesized that the accumulation of evidence for a change in the stimulus distribution controls the dynamics of adaptation, and developed a model for adaptation as an ongoing inference problem. Guided by predictions of this model, we found that the dynamics of adaptation depend on the discriminability of the change in stimulus distribution and that the retina exploits information contained in properties of the stimulus beyond the mean and variance to adapt more quickly when possible.
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Affiliation(s)
- Barry Wark
- Graduate Program in Neurobiology and Behavior, University of Washington, Seattle, WA 98195, USA
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Abstract
Adaptation occurs in a variety of forms in all sensory systems, motivating the question: what is its purpose? A productive approach has been to hypothesize that adaptation helps neural systems to efficiently encode stimuli whose statistics vary in time. To encode efficiently, a neural system must change its coding strategy, or computation, as the distribution of stimuli changes. Information theoretic methods allow this efficient coding hypothesis to be tested quantitatively. Empirically, adaptive processes occur over a wide range of timescales. On short timescales, underlying mechanisms include the contribution of intrinsic nonlinearities. Over longer timescales, adaptation is often power-law-like, implying the coexistence of multiple timescales in a single adaptive process. Models demonstrate that this can result from mechanisms within a single neuron.
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Affiliation(s)
- Barry Wark
- Graduate Program in Neurobiology and Behavior, University of Washington, United States
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Affiliation(s)
- B A y Arcas
- Scheide Library, Princeton University, Princeton, New Jersey 08544, USA
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