title: Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.
abstract: Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary.|MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Lendület Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary.
authors.forename: Balázs B
authors.initials: BB
authors.lastname: Ujfalussy
authors.affiliation: Lendület Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, United States.
authors.forename: Judit K
authors.initials: JK
authors.lastname: Makara
authors.affiliation: MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.|Wolfson Institute for Biomedical Research, University College London, London, United Kingdom.
authors.forename: Tiago
authors.initials: T
authors.lastname: Branco
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Department of Cognitive Science, Central European University, Budapest, Hungary.
authors.forename: Máté
authors.initials: M
authors.lastname: Lengyel
mesh_terms: D000200:Action Potentials; D000818:Animals; D003712:Dendrites / Q000502:physiology*; D018698:Glutamic Acid / Q000378:metabolism; D008959:Models, Neurological; D017966:Pyramidal Cells / Q000502:physiology*; D017207:Rats, Sprague-Dawley; D066191:Sensorimotor Cortex / Q000166:cytology* / Q000502:physiology
chemical_list: D018698:Glutamic Acid
keywords: adaptation; computation; cortex; dendrite; human; mouse; neuroscience; rat; statistics