Conference on Cognitive Computational Neuroscience

Animals learn from experience the value of environmental stimuli to guide their behavior. At the core of conditioning lies the capacity to associate a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Reward-modulated associative synaptic plasticity has been successful in explaining conditioning when the neural representations of behavioral stimuli are unmixed. However this assumption is inconsistent with the fact that neurons - particularly in high-level, cognitive areas - display mixed selectivity. Inspired by experimental findings on the associative power of single cortical pyramidal neurons, we propose a computational model that achieves generic pattern-to-pattern mappings at the population level. Our model incorporates a local learning rule operating in compartmentalized neurons, which mirrors the capacity of cortical pyramidal neurons to implement predictive learning through coincidence detection. The model accounts for a wide gamut of conditioning phenomena, offers a reductionist mechanism for causal inference, and produces experimentally testable predictions. In psychological terms, it corresponds to the stimulus substitution component of classical conditioning.