  How can neural networks learn to efficiently represent complex and
high-dimensional inputs via local plasticity mechanisms? Classical models of
representation learning assume that input weights are learned via pairwise
Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity
only works under unrealistic requirements on neural dynamics and input
statistics. To overcome these limitations, we derive from first principles a
learning scheme based on voltage-dependent synaptic plasticity rules. Here,
inhibition learns to locally balance excitatory input in individual dendritic
compartments, and thereby can modulate excitatory synaptic plasticity to learn
efficient representations. We demonstrate in simulations that this learning
scheme works robustly even for complex, high-dimensional and correlated inputs,
and with inhibitory transmission delays, where Hebbian-like plasticity fails.
Our results draw a direct connection between dendritic excitatory-inhibitory
balance and voltage-dependent synaptic plasticity as observed in vivo, and
suggest that both are crucial for representation learning.
