Machine-learning analysis of Voltage-sensitive dye imaging (VSDI)

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We propose to explore independent components of a rich neuroimaging technique (VSDI) in order to disentangle key neural computations on the cortex topography.


Voltage-sensitive dye imaging (VSDI) allows to measure neuronal activity on the cortical surface, with an excellent compromise between spatial resolution, temporal resolution and large field of view. As a consequence, this technique provides a unique access to the neuronal population response at the mesoscopic scale. Our team has collected a large number of recordings in many awake and anesthetized monkeys, in spontaneous conditions, i.e. without any particular experimental stimulation, but also in response to various visual stimuli. During this internship, we propose to develop machine learning methods for better apprehending these complex signals. Building on previous work, we would like to use the idea that this signal could be represented as a mixture of independent signals. Using state-of-the-art deep learning tools for unsupervised learning, we hope to develop unprecedented tool to better benefit from the potential of this unique signal. In particular we wish to study the patterns emerging from this algorithm, in cortical space and in time.

Desired profile

The candidate will need to have a reasonable knowledge of machine learning tools, as well as an interest in the neuroscientific aspects of the subject.
Application Dead line: December 1, 2023

Host institution

The internship will take place at the Timone neuroscience institute in Marseille, and will use existing data to feed the machine learning algorithm. It will be co-supervised by Laurent Perrinet and Frédéric Chavane within the NeOpTo team.

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