The study of Place Cells, hippocampal neurons tuned to spatial locations in the environment,
is central to elucidate how the brain encodes and retrieves spatial information.
Advances in genetic and imaging technologies have allowed to keep track of
the dynamics of large ensembles of Place Cells across multiple days in mice. Because
spatial information is processed at the population level, novel recording techniques
such as in-vivo calcium imaging have the potential to unveil the mechanisms underlying
the ...
The study of Place Cells, hippocampal neurons tuned to spatial locations in the environment,
is central to elucidate how the brain encodes and retrieves spatial information.
Advances in genetic and imaging technologies have allowed to keep track of
the dynamics of large ensembles of Place Cells across multiple days in mice. Because
spatial information is processed at the population level, novel recording techniques
such as in-vivo calcium imaging have the potential to unveil the mechanisms underlying
the dynamics of place coding. However, with new recording paradigms comes
the need to standardize and optimize the processing and first analysis stages of the
data. In this methodological and data analysis project I will present my work on
building a pipeline to process, extract, filter, track and analyze Place Cells from existing
calcium imaging recordings in a linear-track setup. Using the resulting data,
I will take a decoding approach and present some tentative results on task Place
Cell turnover and on the relation between predictive-accuracy and noise correlations,
with the aim to provide a transversal view, from the first processing stages to
the neural and behavioral analysis, of the challenges and strengths of using calcium
imaging data of behaving animals.
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