Monday 13 December at 2 p.m. , ACCESSIBLE ONLY VIA ZOOM
Presentation by Andrea Benucci (Riken Center for Brain Science, Japan)
Invited by Valentina Emiliani.
ZOOM ACCESS:
https://cnrs.zoom.us/j/94150327983?pwd=SXhTUjkycGN1emVRUW81bXFaNlBkdz09
ID de réunion : 941 5032 7983
Code secret : SR7JS1
ABSTRACT:
Sensory representation plasticity driven by patterned optogenetics in the mouse cortex.
In this presentation, I will first introduce the main areas of interest of my laboratory at RIKEN Center for Brain Science. Then, I will focus on a recent work where we examined the plasticity of sensory representations in the mouse primary visual cortex. The starting observation for this study is that brain circuits acquire and update computations through the dynamics of recurrently connected neurons. Neuronal connections are plastic but the principles that coordinate cell-to-cell connectivity changes for network-level computations remain largely elusive. Using a bidirectional optical interface, we found that optogenetic stimulation centered on a cortical cell (target cell) could coordinate activity changes across hundreds of surrounding cells, enhancing the population encoding for the feature preferentially encoded by the target cell. These changes were more prominent in cells with weaker sensory responses and affected the spontaneous dynamics with neurons co-tuned with the target being more likely to participate in spontaneous cell assemblies.
Our results introduce a novel technology to remodel the encoding of sensory information in visual cortical networks, and reveal a form of plasticity sensitive to activation of targeted neurons, possibly even a single cell, highlighting a mechanism that balances plasticity and stability of feature representations.
Our results introduce a novel technology to remodel the encoding of sensory information in visual cortical networks, and reveal a form of plasticity sensitive to activation of targeted neurons, possibly even a single cell, highlighting a mechanism that balances plasticity and stability of feature representations.
