Dr. Elisa Tartaglia (Postdoc UPMC)
Aging in Vision and Action Lab
CNRS – INSERM – University Pierre&Marie Curie
17, rue Moreau F-75012 Paris, France
A physicist by training (Università di Roma La Sapienza, with Prof. Daniel Amit), I got my PhD in the laboratory of psychophysics at the Brain and Mind Institute of the EPFL, with Prof. Michael H. Herzog, where my main research topic was human perceptual learning, which I investigated from both a theoretical and an empirical point of view. During the past 4 years, two of which were sponsored by the Perspective researcher fellowship of the Swiss National Science Foundation, I have been working as a PostDoc under the supervision of Prof. Nicolas Brunel in between the University of Paris Descartes, the University of Chicago, and the Italian Institute of Technology. There, I have been learning the essential computational and analytical techniques of modeling spiking neurons through the simulation of simple circuits as well as large neuronal networks.
I am interested in understanding how behaviour emerges from the dynamics of large assemblies of interacting neurons. Analytical tools as mean field techniques, associated with large scale simulations of spiking neurons have often provided a plausible mechanistic explanation of the neural correlates of diverse cognitive behaviours, e.g. working memory. These models are supported by in vivo neurophysiological cell recordings collected on, among others, non-human primates. A big challenge and one i am currently focusing on, consists in using such biologically realistic models to reproduce the much richer phenomenology exposed through psychophysics experiments on human observers and ultimately to provide insights on how high cognitive functions arise.
Keywords: neural networks dynamics; spiking neurons; memory; learning; visual perception.
CV and Publication List (pdf): [ download ]
- Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons. Scientific Reports, 7(1). (2017)
- What to choose next? A paradigm for testing human sequential decision making. Frontiers in Psychology, 8:312. (2017)
- Linking perceptual learning with identical stimuli to imagery perceptual learning. Journal of Vision (in press). (2015)
- Human and machine learning in non-markovian decision making. PLoS ONE, 10(4):e0123105. (2015)
- Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli. PLoS Comput. Biol., 11(2):e1004059. (2015)
- On the relationship between persistent delay activity, repetition enhancement and priming. Front Psychol, 5. (2014)
- New Percepts via Mental Imagery?. Front Psychol, 3. (2012)
- Perceptual learning of motion discrimination by mental imagery. J Vis, 12(6). (2012)
- Human perceptual learning by mental imagery. Curr. Biol., 19(24):2081-2085. (2009)
- Anesthesia prevents auditory perceptual learning. Anesthesiology, 111(5):1010-1015. (2009)
- Perceptual learning with Chevrons requires a minimal number of trials, transfers to untrained directions, but does not require sleep. Vision Res., 49(16):2087-2094. (2009)
- Perceptual learning and roving: Stimulus types and overlapping neural populations. Vision Res., 49(11):1420-1427. (2009)
- Modeling perceptual learning: Why mice do not play backgammon. Learn Percept, 1(1):155-163. (2009)