ICNCE 2026

Deep neural networks (DNNs) outperform cortical network models in behavioural performance. DNNs also outperform cortical neuron models in terms of their statistical response properties. Yet, the alignment of DNNs with cortical circuits, cortical structures, and synaptic plasticity is rather limited. I present a functional bottom-up model of cortical pyramidal neurons and cortical circuits that is inspired by self- and cross-attention networks in artificial intelligence (AI). I show how cortical layer 2/3 pyramidal neurons can sustain a key-value memory that is queried by cortical layer 5 neurons. The AI-inspired self-attention is implemented as a gain modulation in cortical pyramidal neurons. Crucially, synapses in the cortical key-value memory are frozen, so that the network is learned purely online, without need of backpropagation through time (BPTT). I show how this online plasticity, in principle,could be implemented via dedicated cortical error neurons. I also show how reward-prediction errors (RPEs), calculated in basal ganglia, help in task-switching. Via gain modulation, the RPEs increase the signal-to-noise ratio in the cortical networks. RPEs further gate the recruitment of hippocampal memories and their inclusion in the cortical key-value memories. Overall, the model aligns well with cortical structures involved in cognition, and offers a mechanistic explanation of inference and learning.