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.
Highlights
The dendritic tree of a cortical neuron performs a highly nonlinear transformation on the thousands of inputs it receives from other neurons, sometimes resulting in a markedly sublinear (Longordo et al, 2013) and often in strongly superlinear integration of synaptic inputs (Losonczy and Magee, 2006; Nevian et al, 2007; Branco and Hausser, 2011; Makara and Magee, 2013)
We argue that the same fundamental statistical principle, that correlated information sources require nonlinear integration, accounts for the dendritic nonlinearities of cortical pyramidal neurons
We established a functional link between the statistics of the synaptic inputs impinging on the dendritic tree of a neuron and the way those inputs are integrated within the dendritic tree
Summary
The dendritic tree of a cortical neuron performs a highly nonlinear transformation on the thousands of inputs it receives from other neurons, sometimes resulting in a markedly sublinear (Longordo et al, 2013) and often in strongly superlinear integration of synaptic inputs (Losonczy and Magee, 2006; Nevian et al, 2007; Branco and Hausser, 2011; Makara and Magee, 2013).
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