From sounds to words: a neurocomputational model of adaptation, inhibition and memory processes in auditory change detection.

Most animals detect sudden changes in trains of repeated stimuli but only some can learn a wide range of sensory patterns and recognise them later, a skill crucial for the evolutionary success of higher mammals. Here we use a neural model mimicking the cortical anatomy of sensory and motor areas and their connections to explain brain activity indexing auditory change and memory access. Our simulations indicate that while neuronal adaptation and local inhibition of cortical activity can explain aspects of change detection as observed when a repeated unfamiliar sound changes in frequency, the brain dynamics elicited by auditory stimulation with well-known patterns (such as meaningful words) cannot be accounted for on the basis of adaptation and inhibition alone. Specifically, we show that the stronger brain responses observed to familiar stimuli in passive oddball tasks are best explained in terms of activation of memory circuits that emerged in the cortex during the learning of these stimuli. Such memory circuits, and the activation enhancement they entail, are absent for unfamiliar stimuli. The model illustrates how basic neurobiological mechanisms, including neuronal adaptation, lateral inhibition, and Hebbian learning, underlie neuronal assembly formation and dynamics, and differentially contribute to the brain's major change detection response, the mismatch negativity.