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Global and Multiplexed Dendritic Computations under In Vivo-like Conditions
Title / Series / Name
Neuron
Publication Volume
100
Publication Issue
3
Pages
Editors
Keywords
dendritic integration
hierarchical
in vivo-like conditions
input-output transformation
linear
model
model fitting
multiplexed
nonlinear
synaptic input
General Neuroscience
hierarchical
in vivo-like conditions
input-output transformation
linear
model
model fitting
multiplexed
nonlinear
synaptic input
General Neuroscience
URI
https://hdl.handle.net/20.500.14018/28528
Abstract
Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. 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, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions. The input-output transformation of neurons under in vivo conditions is unknown. Ujfalussy et al. use a model-based approach to show that linear integration with a single global dendritic nonlinearity can accurately predict the response of neurons to naturalistic synaptic input patterns.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2018-11-07
Language
ISBN
Identifiers
10.1016/j.neuron.2018.08.032