Mario Dipoppa

Assistant Professor, Neurobiology, University of California Los Angeles

UC Profile
Theoretical & Computational Neuroscience laboratory

Dr. Mario Dipoppa seeks to understand the neural mechanisms underlying cortical brain functions. He obtained his Ph.D. at Pierre and Marie Curie University where he developed neural circuit models underlying working memory, under the guidance of Boris Gutkin. He then joined as a postdoc in the laboratory of Kenneth Harris and Matteo Carandini at University College London and was the recipient of the Marie Curie Fellowship. As a postdoc, Dr. Dipoppa combined large-scale neural recordings and computational models to study the mouse visual system. He then served as an Associate Research Scientist at the Center for Theoretical Neuroscience of Columbia University advised by Ken Miller. There, he combined deep learning with dynamical systems methods to study fundamental properties of visual computations. Dr. Dipoppa’s computational neuroscience laboratory continues to investigate how neural networks and dynamics in the cerebral cortex give rise to neural computation. Despite the complexity of their operations, cortical circuits are stereotypical which may underlie common computations. To discover the governing principles of these canonical circuits, Dr. Dipoppa’s laboratory combines state-of-the-art approaches, including biologically realistic neural networks, artificial (deep and recurrent) neural networks, and encoding and decoding models.

Interests

Machine Learning, Computational Neuroscience, Neural Networks, Neural Dynamics, Neural Coding

Publications

  1. Tring E, Dipoppa M, Ringach DL. On the contrast response function of adapted neural populations.. Journal of neurophysiology, 2024.
  2. Tring E, Dipoppa M, Ringach DL. A power law describes the magnitude of adaptation in neural populations of primary visual cortex.. Nature communications, 2023.
  3. Tring E, Dipoppa M, Ringach DL. On the contrast response function of adapted neural populations.. bioRxiv : the preprint server for biology, 2023.
  4. Tring E, Dipoppa M, Ringach DL. A power law of cortical adaptation.. bioRxiv : the preprint server for biology, 2023.
  5. Bugeon S, Duffield J, Dipoppa M, Ritoux A, Prankerd I, Nicoloutsopoulos D, Orme D, Shinn M, Peng H, Forrest H, Viduolyte A, Reddy CB, Isogai Y, Carandini M, Harris KD. A transcriptomic axis predicts state modulation of cortical interneurons.. Nature, 2022.
  6. Schmidt ERE, Zhao HT, Park JM, Dipoppa M, Monsalve-Mercado MM, Dahan JB, Rodgers CC, Lejeune A, Hillman EMC, Miller KD, Bruno RM, Polleux F. A human-specific modifier of cortical connectivity and circuit function.. Nature, 2021.
  7. Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel JP, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, International Brain Laboratory, Cunningham JP, Paninski L. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.. PLoS computational biology, 2021.
  8. Keller AJ, Dipoppa M, Roth MM, Caudill MS, Ingrosso A, Miller KD, Scanziani M. A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex.. Neuron, 2020.
  9. Dipoppa M, Ranson A, Krumin M, Pachitariu M, Carandini M, Harris KD. Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex.. Neuron, 2018.
  10. Dipoppa M, Szwed M, Gutkin BS. Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony.. Advances in cognitive psychology, 2016.
  11. Pérez-Schuster V, Kulkarni A, Nouvian M, Romano SA, Lygdas K, Jouary A, Dipoppa M, Pietri T, Haudrechy M, Candat V, Boulanger-Weill J, Hakim V, Sumbre G. Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish.. Cell reports, 2016.
  12. Pérez-Schuster V, Kulkarni A, Nouvian M, Romano SA, Lygdas K, Jouary A, Dipoppa M, Pietri T, Haudrechy M, Candat V, Boulanger-Weill J, Hakim V, Sumbre G. Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish.. Cell reports, 2016.
  13. Dipoppa M, Gutkin BS. Correlations in background activity control persistent state stability and allow execution of working memory tasks.. Frontiers in computational neuroscience, 2013.
  14. Dipoppa M, Gutkin BS. Flexible frequency control of cortical oscillations enables computations required for working memory.. Proceedings of the National Academy of Sciences of the United States of America, 2013.
  15. Deslippe J, Dipoppa M, Prendergast D, Moutinho MV, Capaz RB, Louie SG. Electron-hole interaction in carbon nanotubes: novel screening and exciton excitation spectra.. Nano letters, 2009.