A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

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.

A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity

“…Incorporating significant biological detail into networks may be essential for obtaining a better understanding of the complex cortical mechanisms underlying semantic processing. Indeed, recent modeling results suggest that large-scale synchronous spiking within cell assembly circuits, also observed here, may be important for the binding of form to meaning during word learning and comprehension (Garagnani et al, 2017 ).…”

“…The model replicates a range of important anatomical and physiological features of the human brain (e.g., Garagnani et al, 2008 , 2017 ; Tomasello et al, 2017 ). As follow a summary of the six neurobiological principles incorporated in the neural network model: Neurophysiological dynamics of spiking pyramidal cells including temporal summation of inputs, threshold-based spiking, nonlinear transformation of membrane potentials into neuronal outputs, and adaptation (Connors et al, 1982 ; Matthews, 2001 ); Synaptic modification by way of Hebbian-type learning, including the two biological mechanisms of long-term potentiation (LTP) and long-term depression (LTD) (Artola and Singer, 1993 ); Area-specific global regulation mechanisms and local lateral inhibition (global and local inhibition) (Braitenberg, 1978 ; Yuille and Geiger, 2003 ); Within-area connectivity: a sparse, random and initially weak connectivity was implemented locally, along with a neighborhood bias toward close-by links (Kaas, 1997 ; Braitenberg and Schüz, 1998 ); Between-area connectivity based on neurophysiological principles and motivated by neuroanatomical evidence; and Uncorrelated white noise was constant present in all neurons during all stages of learning and retrieval with additional noise added to the stimulus patterns to mimic uncorrelated input conditions (Rolls and Deco, 2010 ).…”

“…Additionally, non-adjacent “jumping” links were included within the superior or inferior temporal and superior or inferior frontal cortices (blue arrows). The neuroanatomical evidence motivated by studies using diffusion tensor and diffusion-weighted imaging (DTI/DWI) in humans and non-humans primates are reported in Table 2 and described in previous study (Garagnani et al, 2017 ).…”

Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning

“…www.nature.com/scientificreports www.nature.com/scientificreports/ and single-area model structure are specified in more detail in the Methods section under 'Structure and function of the spiking neuron model' and in previous publications 16,55 .…”

“…Prior to the training, each network was initialised with all the synaptic links (between-and within-areas) connecting single cells established at random (see Methods section under 'Structure and function of the spiking neuron model'). Similar to previous simulation studies [16][17][18]55 , word-meaning acquisition was then simulated under the impact of repeated sensorimotor pattern presentations to the primary areas of the network. Each network instance used 12 different sets of sensorimotor word patterns representing six objectand six action-related words.…”