A neuron receives thousands of signals on its branching processes — the dendrites. Some signals push the cell toward firing (excitation), others hold it back (inhibition). For a long time, it was thought that the neuron simply adds up all the 'fors' and 'againsts.' But in real dendrites, inhibition works differently: it can not subtract but divide the excitatory signal. This is like a volume knob that attenuates sound proportionally, rather than reducing it by a fixed amount.

This mechanism is called shunting inhibition. It occurs when an inhibitory input opens channels that make the membrane 'leaky' to current right at that spot. As a result, the excitatory signal is weakened not by a constant but by a factor. The authors built DendriNet — a trainable model that can switch between summation and division on different branches of the dendritic tree. They tested whether such division helps a neuronal population read out encoded information better.

An important detail emerged. Division pays off when the inhibitory signal is reliable and targets that part of the excitation that varies with noise, not with the signal of interest. If disturbances are strong or inhibition is unstable, the simple sum starts to work better. In experiments on mouse visual cortex recordings, the advantage of division over summation was noticeable only with narrow readout of the signal. With broad readout, it disappeared or even turned into a loss. So dendritic architecture is not always an advantage per se, but a tool that is good in the skilled hands of reliable data.