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.
