Françoise Lecaignard

I address the challenge of understanding perception, a core brain function that relies on sensory information processing and is altered in several clinical conditions. I develop a mechanistic approach based on generative models at both the physiological and cognitive levels. My theoretical framework is predictive coding, which casts the brain’s information processing as perceptual inference and learning. I test these hypotheses in the auditory modality, at the cortical level. I use human electrophysiology (EEG, MEG) together with advanced computational modeling to capture the dynamics of the underlying mechanisms.

I have recently started developing a multiscale framework to investigate predictive coding principles across scales, in particular how neural networks, from interconnected neurons to whole-brain networks, adapt to environmental changes by continuously learning and predicting their future inputs. This project aims to relate electrophysiology at microscopic (in vitro neuronal cultures, spiking neural networks) and macroscopic (non-invasive EEG and MEG) levels to provide a finer-grained description of these processes and to address the challenge of bridging scales. 

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I also have a keen interest in brain imaging techniques such as magnetoencephalography (MEG) and electroencephalography (EEG), and in advanced modeling approaches that can extract even more information from these signals.

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I work in Lyon as an INSERM research engineer at the CRNL in the team of Mathilde Bonnefond and Jérémie Mattout (COPHY).

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Keywords

electrophysiology, perception, auditory processing, mismatch negativity, perceptual learning, predictive coding, dynamic causal modeling, bayesian inference

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ORCID     0000-0002-5057-4788

idHAL       francoise-lecaignard

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