Dr. Gianluigi Mongillo (Researcher CNRS)

Vision Institute
Aging in Vision and Action Lab
CNRS – INSERM – Sorbonne University
17, rue Moreau F-75012 Paris, France

email:

 

Research interests: 

I am mostly interested in understanding the collective dynamics of large neuronal networks by using concepts and tools from statistical physics. I am currently focusing on structural synaptic plasticity and its impact on network’s storage capacity.

Short bio: 

I have received the M.Sc. degree in Physics (in 2000) and the Ph.D. degree in Neurophysiology (in 2005) from the University of Rome “La Sapienza”, Rome, Italy. I have been postdoctoral fellow at the GATSBY Unit (UCL, London, UK), at the École Normale Supérieure (Paris, France), at the Hebrew University of Jerusalem (Jerusalem, Israel) and at the École de Neurosciences de Paris Île-de-France (Paris, France). Since 2009, I am research scientist with the CNRS.

Keywords: mean-field theory, balanced networks, learning & memory, synaptic plasticity, models of working memory.

 

CV and Publication List (pdf): [ download ]

Publications

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2020

  1. Lisi M, Mongillo G, Milne G, Dekker T and Gorea A (2020) Discrete confidence levels revealed by sequential decisions. Nature Human Behaviour.

2018

  1. Mongillo G, Rumpel S and Loewenstein Y (2018) Inhibitory connectivity defines the realm of excitatory plasticity. Nature Neuroscience, 21(10):1463-1470.

2017

  1. Mongillo G, Rumpel S and Loewenstein Y (2017) Intrinsic volatility of synaptic connections—a challenge to the synaptic trace theory of memory. Current Opinion in Neurobiology, 46:7-13.
  2. Mongillo G and Loewenstein Y (2017) Neuroscience: Formation of a Percept in the Rat Cortex. Current Biology, 27(11):R423-R425.

2016

  1. Barri A, Wang Y, Hansel D and Mongillo G (2016) Quantifying repetitive transmission at chemical synapses: a generative-model approach. eNeuro:ENEURO-0113.

2015

  1. Tartaglia EM, Brunel N and Mongillo G (2015) Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli. PLoS Computational Biology, 11(2):e1004059.
  2. Tartaglia EM, Mongillo G and Brunel N (2015) On the relationship between persistent delay activity, repetition enhancement and priming. Frontiers in Psychology, 5:1590.
  3. Mongillo G (2015) Working Memory, Models of. In Encyclopedia of Computational Neuroscience, pages 3178-3180, Springer.

2014

  1. Mongillo G, Shteingart H and Loewenstein Y (2014) The misbehavior of reinforcement learning. Proceedings of the IEEE, 102(4):528-541.
  2. Mongillo G, Shteingart H and Loewenstein Y (2014) Race Against the Machine [Further Thoughts]. Proceedings of the IEEE, 102(4):542-543.

2012

  1. Mongillo G, Hansel D and van Vreeswijk C (2012) Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Physical Review Letters, 108(15):158101.
  2. Mongillo G (2012) Hebbian learning. In Encyclopedia of the Sciences of Learning, pages 1417-1419, Springer.

2011

  1. Roxin A, Brunel N, Hansel D, Mongillo G and van Vreeswijk C (2011) On the distribution of firing rates in networks of cortical neurons. Journal of Neuroscience, 31(45):16217-16226.

2008

  1. Mongillo G, Barak O and Tsodyks M (2008) Synaptic theory of working memory. Science, 319(5869):1543-1546.
  2. Mongillo G and Deneve S (2008) Online learning with hidden Markov models. Neural Computation, 20(7):1706-1716.

2006

  1. Romani S, Amit DJ and Mongillo G (2006) Mean-field analysis of selective persistent activity in presence of short-term synaptic depression. Journal of Computational Neuroscience, 20(2):201-217.

2005

  1. Mongillo G, Curti E, Romani S and Amit DJ (2005) Learning in realistic networks of spiking neurons and spike-driven plastic synapses. European Journal of Neuroscience, 21(11):3143-3160.

2004

  1. Curti E, Mongillo G, Camera GLa and Amit DJ (2004) Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories. Neural Computation, 16(12):2597-2637.

2003

  1. Mongillo G, Amit DJ and Brunel N (2003) Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network. European Journal of Neuroscience, 18(7):2011-2024.
  2. Amit DJ and Mongillo G (2003) Spike-driven synaptic dynamics generating working memory states. Neural Computation, 15(3):565-596.
  3. Amit DJ and Mongillo G (2003) Selective delay activity in the cortex: phenomena and interpretation. Cerebral Cortex, 13(11):1139-1150.

2001

  1. Mongillo G and Amit DJ (2001) Oscillations and irregular emission in networks of linear spiking neurons. Journal of Computational Neuroscience, 11(3):249-261.