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Sequential and efficient neural-population coding of complex task information

Neuron(2022)

Cited 24|Views9
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Abstract
Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/ updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task -specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time -dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.
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Key words
mouse posterior cortex,complex decision making behavior,neural sequences,neural population coding,efficient coding
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