1
|
Tostado-Marcos P, Arneodo EM, Ostrowski L, Brown DE, Perez XA, Kadwory A, Stanwicks LL, Alothman A, Gentner TQ, Gilja V. Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior. ARXIV 2024:arXiv:2407.06244v1. [PMID: 39040642 PMCID: PMC11261980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA.
Collapse
Affiliation(s)
- Pablo Tostado-Marcos
- Department of Bioengineering
- Department of Electrical and Computer Engineering
- Department of Psychology
| | | | - Lauren Ostrowski
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Daril E Brown
- Department of Electrical and Computer Engineering
- Department of Psychology
| | | | - Adam Kadwory
- Department of Electrical and Computer Engineering
| | - Lauren L Stanwicks
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | | | - Timothy Q Gentner
- Department of Psychology
- Department of Neurobiology
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| |
Collapse
|
2
|
Sadras N, Pesaran B, Shanechi MM. Event detection and classification from multimodal time series with application to neural data. J Neural Eng 2024; 21:026049. [PMID: 38513289 DOI: 10.1088/1741-2552/ad3678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/21/2024] [Indexed: 03/23/2024]
Abstract
The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.
Collapse
Affiliation(s)
- Nitin Sadras
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, and the Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
| |
Collapse
|
3
|
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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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.
Collapse
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
| |
Collapse
|
4
|
Zhang Y, Zhou L, Zuo J, Wang S, Meng W. Analogies of human speech and bird song: From vocal learning behavior to its neural basis. Front Psychol 2023; 14:1100969. [PMID: 36910811 PMCID: PMC9992734 DOI: 10.3389/fpsyg.2023.1100969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
Vocal learning is a complex acquired social behavior that has been found only in very few animals. The process of animal vocal learning requires the participation of sensorimotor function. By accepting external auditory input and cooperating with repeated vocal imitation practice, a stable pattern of vocal information output is eventually formed. In parallel evolutionary branches, humans and songbirds share striking similarities in vocal learning behavior. For example, their vocal learning processes involve auditory feedback, complex syntactic structures, and sensitive periods. At the same time, they have evolved the hierarchical structure of special forebrain regions related to vocal motor control and vocal learning, which are organized and closely associated to the auditory cortex. By comparing the location, function, genome, and transcriptome of vocal learning-related brain regions, it was confirmed that songbird singing and human language-related neural control pathways have certain analogy. These common characteristics make songbirds an ideal animal model for studying the neural mechanisms of vocal learning behavior. The neural process of human language learning may be explained through similar neural mechanisms, and it can provide important insights for the treatment of language disorders.
Collapse
Affiliation(s)
- Yutao Zhang
- Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Lifang Zhou
- Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Jiachun Zuo
- Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Songhua Wang
- Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| | - Wei Meng
- Jiangxi Key Laboratory of Organic Chemistry, Jiangxi Science and Technology Normal University, Nanchang, China
| |
Collapse
|
5
|
Hahn LA, Balakhonov D, Lundqvist M, Nieder A, Rose J. Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows. Prog Neurobiol 2022; 219:102372. [DOI: 10.1016/j.pneurobio.2022.102372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/21/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
|