Global and Multiplexed Dendritic Computations under In Vivo-like Conditions

• Published in: Neuron (Cambridge, Mass.) Mass.), 2018-11, Vol.100 (3), p.579-592.e5
• Main Authors: Ujfalussy, Balázs B., Makara, Judit K., Lengyel, Máté, Branco, Tiago
• Format: Article
• Language: English
• Subjects: Animals; Charitable foundations; Cortex; Dendrites; Dendrites - physiology; dendritic integration; hierarchical; Humans; input-output transformation; in vivo-like conditions; linear; Linear Models; Membrane potential; Membrane Potentials - physiology; model fitting; Models, Neurological; multiplexed; Nerve Net - cytology; Nerve Net - physiology; Neurons; nonlinear; Nonlinear systems; Scholarships & fellowships; synaptic input
• ISSN: 0896-6273; 1097-4199
• DOI: 10.1016/j.neuron.2018.08.032

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. [Display omitted] •Understanding integration of complex synaptic inputs requires a model-based approach•Hierarchical LN models accurately predict the responses of multiple cell types•Linear subunits with a global dendritic nonlinearity achieve 90% prediction accuracy•Analyses reveal a novel motif: multiplexing inputs into parallel processing channels 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.