We combine (and create) behavioral, computational, electrophysiological, and imaging approaches to reveal how the nervous system uses sensory signals to reshape the patterns of brain and muscle activity that control skilled behavior.

Projects

Spiking Codes for Skilled Motor Control

By analyzing single-unit recordings from neurons and muscle fibers during complex behaviors in songbirds (and, more recently, rodents), we examine how the brain regulates precisely-timed spike patterns to control agile behavior.

Key Papers

Hernandez et al 2022 image
Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries
Hernández DG, Sober SJ, Nemenman I. eLife 11:e68192 (2022) [PDF] [DOI]
Millisecond Spike Timing Codes for Motor Control
Millisecond Spike Timing Codes for Motor Control (review article)
Sober SJ, Sponberg S, Nemenman I, Ting LH. Trends in Neuroscience Oct;41(10):644-648 (2018) [PDF]
Srivastava et al 2016
Motor control by precisely timed spike patterns
Srivastava K, Holmes CM, Vellema M, Pack A, Elemans C, Nemenman I, Sober SJ. Proceedings of the National Academy of Sciences 114(5):1171-1176 (2017) [PDF]
Tang et al image
Millisecond-Scale Motor Encoding in a Cortical Vocal Area
Tang C, Srivastava K, Chehayeb D, Nemenman I, and Sober SJ. PLoS Biology DOI: 10.1371/journal.pbio.1002018 (2014) [PDF] [DOI]

Advanced Technology for Motor Neurophysiology

By developing advanced nanofabrication tools for manufacturing ultra-dense, ultra-flexible electrode arrays, we are creating a new class of electromyography (EMG) electrodes capable of recording large populations of single-unit recordings from muscle fibers during behavior.

Key Papers

Chung et al 2023 figure 1
Myomatrix arrays for high-definition muscle recording
Chung B, Zia M, Thomas KA, Michaels JA, Jacob A, Pack A, Williams MJ, Nagapudi K, Teng LH, Arrambide E, Ouellette L, Oey N, Gibbs R, Anschutz P, Lu J, Wu Y, Kashefi M, Oya T, Kersten R, Mosberger AC, O'Connell S, Wang R, Marques H, Mendes AR, Lenschow C,. ELife 12:RP88551 (2023) [PDF] [DOI]
Fabrication and Characterization of 3D Multi Electrode
Fabrication and Characterization of 3D Multi-Electrode Array on Flexible Substrate for In Vivo EMG Recording from Expiratory Muscle of Songbird
Zia M, Chung B, Sober SJ, Bakir M. Proceedings IEEE International Electron Devices Meeting San Francisco, CA (2018) [PDF]
Srivastava et al 2016
Motor control by precisely timed spike patterns
Srivastava K, Holmes CM, Vellema M, Pack A, Elemans C, Nemenman I, Sober SJ. Proceedings of the National Academy of Sciences 114(5):1171-1176 (2017) [PDF]

Neuroanatomical and Neuromodulatory Mechanisms of Motor Plasticity

By combining advanced neuroanatomical methods with behavioral and pharmacological manipulations, we investigate the neural mechanisms of learning.

Key Papers

McGregor et al 2022 Fig 1
Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain
McGregor JN, Grassler AL, Jaffe PI, Jacob AL, Brainard MS, Sober SJ. eLife 11:e75691 (2022) [PDF] [DOI]
Nicholson et al 2018
Thalamostriatal and cerebellothalamic pathways in a songbird, the Bengalese finch
Nicholson D, Roberts T, Sober SJ. Journal of Comparative Neurology 526(9):1550-1570 (2018) [PDF] [DOI]
Hoffman et al image
Vocal generalization depends on gesture identity and sequence
Hoffmann LA, Sober SJ. J. Neuroscience 16;34(16):5564-74 (2014) [PDF]

Variability, Bayesian Inference, and Sensorimotor Learning

By combining long-term manipulations of auditory feedback with mathematical models of sensorimotor learning, we reveal the computational principles underlying vocal learning.

Key Papers

Zhou et al Figure 1
Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds
Zhou B, Hofmann D, Pinkoviezky I, Sober SJ, Nemenman I. Proceedings of the National Academy of Sciences 115(36):E8538-E8546 (2018) [PDF]
Kelly and Sober 2014
A simple computational principle predicts vocal adaptation dynamics across age and error size
Kelly CW, Sober SJ. Frontiers in Integrative Neuroscience doi: 10.3389/fnint.2014.00075 (2014) [PDF] [DOI]

Support

Our research projects are generously supported by the following federal and private funding agencies:

NIH logo

simons logo

NSF logo

NIH Brain Initiative logo

Azrieli logo

Trainees in the lab have been well supported by both internal and external funding sources:

Emory GDDBS logo

CNTP logo

NSF GRFP logo

NIH F31 logo

HHMI Gilliam logo