Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity

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.