The Memory, Error and Learning (MEL) group

We use behavioral experiments, brain electrophysiology and non-invasive brain stimulation in humans to identify the neural computations of memory and learning

- News

- Research

Decoding working memory and replay from MEG signal

The spatiotemporal dynamic of information processing is visible using machine-learning decoding of high-temporal resolution neural signal from M/EEG. Using this approach, we identified a neural signature of content selection and characterize differentiated spatiotemporal constraints for subprocesses of working memory. In a context of learning, this approach allows us to test whether the brain is replaying silently what it just learned, as a replay mechanism to consolidate the new skill.

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Behavioral dynamics of learning

We are investigating the behavioral short-scale dynamics of learning. During a learning session, some types of learning are evidenced during short rest periods and others during the practice itself. For example, statistical learning occurs during the practice itself while more complex rule learning occurs during short-breaks. Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations.

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Learning from errors

Learning through trial and error is required for daily living (“Experience is simply the name we give our mistakes” – Oscar Wilde), whether it be for a child learning to write or a stroke patient learning to walk again. Behavioral computational models provide a framework for understanding the impact of error on human learning. They pose that the brain continuously updates an error-sensitivity signal controlling how much is learned from past errors. Using machine-learning decoding of brain activity during learning tasks, we will identify the neural representation of error-sensitivity. This project will provide crucially missing information about the brain computations during learning.

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Decoding working memory and replay from MEG signal

The spatiotemporal dynamic of information processing is visible using machine-learning decoding of high-temporal resolution neural signal from M/EEG. Using this approach, we identified a neural signature of content selection and characterize differentiated spatiotemporal constraints for subprocesses of working memory. In a context of learning, this approach allows us to test whether the brain is replaying silently what it just learned, as a replay mechanism to consolidate the new skill.

Read More

Behavioral dynamics of learning

We are investigating the behavioral short-scale dynamics of learning. During a learning session, some types of learning are evidenced during short rest periods and others during the practice itself. For example, statistical learning occurs during the practice itself while more complex rule learning occurs during short-breaks. Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations.

Read More

Learning from errors

Learning through trial and error is required for daily living (“Experience is simply the name we give our mistakes” – Oscar Wilde), whether it be for a child learning to write or a stroke patient learning to walk again. Behavioral computational models provide a framework for understanding the impact of error on human learning. They pose that the brain continuously updates an error-sensitivity signal controlling how much is learned from past errors. Using machine-learning decoding of brain activity during learning tasks, we will identify the neural representation of error-sensitivity. This project will provide crucially missing information about the brain computations during learning.

Read More

- The team

My research aims at identifying neural computations crucial for learning in large-scale neural dynamics and at designing innovative ways to modulate and improve learning abilities. I am mainly using machine-learning techniques applied to magnetoencephalography (MEG) and psychophysics. I have also experience on brain stimulation and diffusion imaging.

Download my CV:

Szonja WEIGL

Coumarane Tirou

Alumni

Szonja WEIGL

Coumarane Tirou

Alumni

Join us

Job offer 

Type: Postdoc
Expected start: 01/02/2022
Duration: 24 months
Location: Lyon
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We are continuously looking for motivated and talented students.

If you think that your CV could match the group activities, don’t hesitate to contact us.

Full Publication List

Manipulation of pre-target activity on the right frontal eye field enhances conscious visual perception in humans
Chanes Lorena, Chica Ana B, Quentin Romain, Valero-cabré Antoni
Plos One (2012)
Fronto-tectal white matter connectivity mediates facilitatory effects of non-invasive neurostimulation on visual detection
Quentin Romain, Chanes Lorena, Migliaccio Raffaella, Valabrègue Romain, Valero-cabré Antoni
Neuroimage (2013)
Author response. oscillation and synchrony entrainment: a new breadth for focal non-invasive neurostimulation in the cognitive neurosciences
Valero-cabré Antoni, Quentin Romain, Vernet Marine, Chanes Lorena
The Journal Of Neuroscience: The Official Journal Of The Society For Neuroscience (2013)
Frontal eye field, where art thou? anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations
Vernet Marine, Quentin Romain, Chanes Lorena, Mitsumasu Andres, Valero-cabré Antoni
Fronto-parietal anatomical connections influence the modulation of conscious visual perception by high-beta frontal oscillatory activity
Quentin Romain, Chanes Lorena, Vernet Marine, Valero-cabré Antoni
Cerebral Cortex (2015)
Visual contrast sensitivity improvement by right frontal high-beta activity is mediated by contrast gain mechanisms and influenced by fronto-parietal white matter microstructure
Quentin Romain, Elkin Frankston Seth, Vernet Marine, Toba Monica N., Bartolomeo Paolo, Chanes Lorena, Valero-cabré Antoni
Cerebral Cortex (2016)
Cortico-thalamic disconnection in a patient with supernumerary phantom limb
Bourlon Clémence, Urbanski Marika, Quentin Romain, Duret Christophe, Bardinet Eric, Bartolomeo Paolo, Bourgeois Alexia
mne-bids: organizing electrophysiological data into the bids format and facilitating their analysis
Appelhoff Stefan, Sanderson Matthew, Brooks Teon L., Vliet Marijn Van, Quentin Romain, Holdgraf Chris, Chaumon Maximilien, Mikulan Ezequiel, Tavabi Kambiz, Höchenberger Richard, Welke Dominik, Brunner Clemens, Rockhill Alexander P., Larson Eric, Gramfort Alexandre, Jas Mainak