Simulating word learning, language-attention interactions, and spontaneous emergence of intentions to speak in a neuroanatomically grounded model of the left perisylvian cortex.

I will describe a neural-network architecture that we developed to simulate and explain, at cortical level, word learning and language processes as they are believed to occur in motor and sensory primary, secondary and higher association areas of the left frontal and temporal lobes of the human brain. The model was built to closely reflect known anatomy and neurobiological features of the corresponding cortices, including sparse and patchy connectivity, Hebbian synaptic-plasticity, spontaneous neuronal firing. We simulated early stages of word learning by repeatedly confronting the network with correlated patterns of sensorimotor input; as a result of learning, memory traces for words spontaneously emerged in it, consisting of distributed, strongly connected perception action circuits (or Hebbian “cell assemblies”) that exhibited complex, non-linear dynamics.

In the second part I will attempt to show how the cortical distribution, functional behaviour and competitive interactions of these action-perception circuits, along with the underlying network’s connectivity structure, can go a long way in explaining a body of experimental data and phenomena. By way of example, I will describe the model’s mechanistic accounts of brain indexes of auditory change detection, neurophysiological responses to familiar words and unknown lexical items, the complex interactions of language and attention, and, finally, the emergence and cortical topography of neural processes underlying the spontaneous formation of an intention to speak. I will conclude by arguing for an approach to neuroscience research based on the theory-driven application of experimental methods in conjunction with, and grounded upon, biologically realistic neurocomputational modelling.