Phd
State-Dependent Computations in Recurrent Spiking Networks / Y. Yerlaut / Paris
State-Dependent Computations in Recurrent Spiking Networks
The flexible nature of information processing is a remarkable feature of cognition in the healthy brain. Contextual information from either internal (arousal, mood, …) or external (sensory) variables can indeed greatly affect the outcome of diverse cortical processes (perception, decisions, …). However, despite extensive research, our understanding of how contextual signals modulate cortical information processing remains elusive.
In this project, the candidate should use theoretical analysis of spiking network dynamics to investigate how modulations of recurrent neural activity can act as a functional “switch” between different modes of information processing.
Depending on her/his interest, the candidate could also benefit from our mixed experimental/theoretical research laboratory to test modelling predictions using cutting-edge neurophysiological techniques (two-photon imaging, high-densitiy electrophysiology, optogenetics, chemogenetics, …) during visual processing in the mouse cortex.
Contact: Yann Zerlaut, PhD Junior Professor of Computational Neuroscience Sorbonne Université, Paris Brain Institute (ICM) Phone: +33 (0)1 57 27 45 48 Webpage: yzerlaut.github.io/ Laboratory: therebolalab.org/
The flexible nature of information processing is a remarkable feature of cognition in the healthy brain. Contextual information from either internal (arousal, mood, …) or external (sensory) variables can indeed greatly affect the outcome of diverse cortical processes (perception, decisions, …). However, despite extensive research, our understanding of how contextual signals modulate cortical information processing remains elusive.
In this project, the candidate should use theoretical analysis of spiking network dynamics to investigate how modulations of recurrent neural activity can act as a functional “switch” between different modes of information processing.
Depending on her/his interest, the candidate could also benefit from our mixed experimental/theoretical research laboratory to test modelling predictions using cutting-edge neurophysiological techniques (two-photon imaging, high-densitiy electrophysiology, optogenetics, chemogenetics, …) during visual processing in the mouse cortex.
Contact: Yann Zerlaut, PhD Junior Professor of Computational Neuroscience Sorbonne Université, Paris Brain Institute (ICM) Phone: +33 (0)1 57 27 45 48 Webpage: yzerlaut.github.io/ Laboratory: therebolalab.org/
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