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bioRxiv · 10.1101/235259

Global, multiplexed dendritic computations under in vivo-like conditions

Ujfalussy, B. B.·Lengyel, M.·Branco, T.
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Abstract

Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex spatio-temporal synaptic input patterns. We used the hLN to predict the membrane potential of a detailed biophysical model of a L2/3 pyramidal cell receiving in vivo-like synaptic input and reproducing in vivo dendritic recordings. We found that more than 90% of the somatic response could be captured by linear integration followed a single global non-linearity. Multiplexing inputs into parallel processing channels could improve prediction accuracy by as much as additional layers of local non-linearities. These results provide a data-driven characterisation of a key building block of cortical circuit computations: dendritic integration and the input-output transformation of single neurons during in vivo-like conditions.

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Ujfalussy, B. B., Lengyel, M., Branco, T.. 2017-12-16. Global, multiplexed dendritic computations under in vivo-like conditions. https://doi.org/10.1101/235259

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