  The cortex learns to make associations between stimuli and spiking activity
which supports behaviour. It does this by adjusting synaptic weights. The
complexity of these transformations implies that synapses have to change
without access to the full error information, a problem typically referred to
as "credit-assignment". However, it remains unknown how the cortex solves this
problem. We propose that a combination of plasticity rules, 1) Hebbian, 2)
acetylcholine-dependent and 3) noradrenaline-dependent excitatory plasticity,
together with 4) inhibitory plasticity restoring E/I balance, effectively
solves the credit assignment problem. We derive conditions under-which a neuron
model can learn a number of associations approaching its theoretical capacity.
We confirm our predictions regarding acetylcholine-dependent and inhibitory
plasticity by reanalysing experimental data. Our work suggests that detailed
cortical E/I balance reduces the dimensionality of the problem of associating
inputs with outputs, thereby allowing imperfect "supervision" by
neuromodulatory systems to guide learning effectively.
