The Poe lab investigates the mechanisms by which sleep traits serve learning and memory consolidation. Memories are encoded by the pattern of synaptic connections between neurons. We employ tetrode recording and optogenetic techniques in learning animals to see how neural patterns underlying learning are reactivated during sleep, and how activity during sleep influences the neural memory code. Both strengthening and weakening of synapses is important to the process of sculpting a network when we make new memories and integrate them into old schema. Results from our studies suggest that while synaptic strengthening can be efficiently accomplished during the waking learning process, the synaptic weakening part of memory integration requires conditions unique to sleep. The absence of noradrenaline during sleep spindles and REM sleep as well as the low levels of serotonin during REM sleep allow the brain to integrate new memories and to refresh and renew old synapses so that we are ready to build new associations the next waking period. Memory difficulties involved in post-traumatic stress disorder, Schizophrenia, Alzheimer’s disease and even autism involve abnormalities in the sleep-dependent memory consolidation process that my lab studies. Keywords: Sleep, learning and memory, PTSD, memory consolidation, reconsolidation, REM sleep, sleep spindles, Norepinephrine, LTP, depotentiation, reversal learning, optogenetics, electrophysiology, tetrode recordings, hippocampus, prefrontal cortex.
Mario Dipoppa is an Assistant Professor in computational neuroscience in the Department of Neurobiology at the David Geffen School of Medicine at UCLA. Dr. 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.
- Computational Neuroscience
- Machine Learning
- Neural Networks
- Neural Coding
- Neural Dynamics
Tring E, Dipoppa M, Ringach DL (2023), A power law of cortical adaptation, bioRxiv, 548134
Di Santo S, Dipoppa M, Keller AJ, Roth MM, Scanziani M, Miller KD (2022), Unifying model for three forms of contextual modulation including feedback input from higher visual areas, bioRxiv, 493753.
Bugeon S, Duffield J, Dipoppa M, Prankerd I, Ritoux A, Nicolotsopoulos D, Orme D, Shinn M, Peng H, Forrest H, Viduolyte A, Reddy CB, Isogai Y, Carandini M, Harris KD (2022), A transcriptomic axis predicts state modulation of cortical interneurons, Nature, 607: 330-338.
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 (2021), A human-specific modifier of cortical circuit connectivity and function improves behavioral performance, Nature, 599: 640-644. (*contributed equally)
Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel J-P, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, Cunningham J, Paninski L (2021), Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders, PLOS Comput. Bio., 17: e1009439.
Keller AJ*, Dipoppa M*, Roth MM*, Caudill M*, Ingrosso A, Miller KD, Scanziani M (2020), A disinhibitory circuit for contextual modulation in primary visual cortex, Neuron, 108: 1-13. (*co-first authors)
Minni S*, Ji-An L*, Moskovitz T, Lindsay G, Miller KD, Dipoppa M, Yang GR (2019), Understanding the functional and structural differences across excitatory and inhibitory neurons, BioRxiv, 680439. (*co-first authors)
Dipoppa M, Ranson A, Krumin M, Pachitariu M, Carandini M, Harris KD (2018), Vision and locomotion shape the interactions between neuron types in mouse visual cortex, Neuron, 98: 602-615.
Pachitariu M, Stringer C, Dipoppa M, Schröder S, Rossi LF, Dalgleish H, Carandini M, Harris KD (2017), Suite2p: beyond 10,000 neurons with standard two-photon microscopy, bioRxiv, 061507.
Dipoppa M, Szwed M, Gutkin BS (2016), Controlling working memory operations by selective gating: role of oscillations and synchrony, Adv. Cogn. Psychol., 12: 209-232.
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 (2016), Sustained rhythmic brain activity underlies visual motion perception in zebrafish, Cell Reports, 17: 1098-1112.
Dipoppa M and Gutkin BS (2013), Flexible frequency control of cortical oscillations enables computations required for working memory, Proc. Natl. Acad. Sci. USA, 110: 12828-12833.
Dipoppa M and Gutkin BS (2013), Correlations in background activity control persistent state stability and allow execution of working memory tasks, Front. Comput. Neurosci. 7.
Dipoppa M, Krupa M, Torcini A, Gutkin BS (2012), Splay states in finite pulse-coupled networks of excitable neurons, SIAM J. Appl. Dyn. Syst. 11: 864–894.
Deslippe J, Dipoppa M, Prendergast D, Moutinho MV, Capaz RB, Louie SG (2009), Electron-Hole Interaction in Carbon Nanotubes: Novel Screening and Exciton Excitation Spectra, Nano Letters, 9: 1330-1334.