Simulating word learning and high-frequency brain responses to linguistic items in a neurobiologically realistic model of the cortex

I will highlight a neural architecture that we developed to simulate and explain cortical correlates of word learning and semantic grounding in the human brain. The model’s main distinguishing features are (i) to closely replicate connectivity and anatomical structure of left-hemispheric cortical areas known to be relevant for language processing, and (ii) to implement only functional mechanisms that reflect known cellular- and synaptic-level properties of the cortex.
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\nAppropriate “sensorimotor” stimulation of the network (mimicking early stages of word acquisition) leads to the spontaneous formation in the network of model correlates of memory traces for words (i.e., distributed cell-assembly circuits exhibiting non-linear and oscillatory dynamics). I will then show that, without any significant changes, this neural architecture goes a long way towards explaining a range of experimental data and phenomena in language as well as other domains, pointing to a unifying model of cognition based on action-perception circuits whose emergence, dynamics and interactions are grounded in known neuroanatomy and neurobiological mechanisms.