Abstract
Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned “word” forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful “word” and novel, senseless “pseudoword” patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience.
Highlights
Experimental evidence suggests that the cortex stores knowledge about meaningful, well-known familiar items as distributed memory circuits, that is, strongly interlinked neuronal ensembles of hundreds or thousands of neurons whose members may be spread across distant areas of cortex
The training of the network led to the spontaneous formation of cell assembly circuits analogous to those obtained in previous versions of the architecture (Garagnani and Pulvermüller, 2016; Tomasello et al, 2016), that is, sets of strongly and reciprocally connected cells linking together correlated patterns of “sensorimotor” activity
Visual observation of the network responses during presentation of learned “word” and novel “pseudoword” items to the model correlate of primary auditory cortex indicated that both types of stimuli induced oscillatory phenomena, manifest in the form of “pulses” or waves of activity propagating across the network
Summary
Experimental evidence suggests that the cortex stores knowledge about meaningful, well-known familiar items (such as objects, faces, and words) as distributed memory circuits, that is, strongly interlinked neuronal ensembles of hundreds or thousands of neurons whose members may be spread across distant areas of cortex. The reactivation of such a cell assembly (CA) circuit sparked by the perception of the corresponding sensory item is hypothesized to induce waves of reverberant activity within the corresponding circuit (Hebb, 1949), measurable as correlated firing activity. Only responses to the coherent stimuli have been found to induce synchronous oscillations across neurons located in different cortical hemispheres (Supp et al, 2005, 2007)
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