Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits
- University of Cambridge, United Kingdom
- Institute of Experimental Medicine, Hungary
- MRC Laboratory of Molecular Biology, United Kingdom
- Cited 12
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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 formalises 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.
Article and author information
Author details
Ethics
Animal experimentation: Hippocampal experiments were conducted according to methods approved by the Janelia Farm Institutional Animal Care and Use Committee and 26 the Animal Care and Use Committee (ACUC) of the Institute of Experimental Medicine, Hungarian Academy of 27 Sciences, and in accordance with 86/609/EEC/2 and DIRECTIVE 2010/63/EU Directives of the EU. Neocortical experiments were performed in strict accordance with guidelines of the Wolfson Institute for Biomedical Research and with the national guidelines.
Reviewing Editor
- Frances K Skinner, University Health Network, Canada
Publication history
- Received: July 14, 2015
- Accepted: December 23, 2015
- Accepted Manuscript published: December 24, 2015 (version 1)
- Version of Record published: March 31, 2016 (version 2)
Copyright
© 2015, Ujfalussy et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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