Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas

Garagnani, Max; Pulvermüller, Friedemann

doi:10.1016/j.bandl.2013.02.001

Cited by 30 publications

(32 citation statements)

References 115 publications

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“…Recent neurocomputational modelling work may provide an explanation for our present findings. An anterior shift of activation together with reduced topographic specificity are in line with predictions of a neurocomputational model of action perception circuits, APCs, carrying word comprehension and verbal memory processes (Garagnani and Pulvermüller, 2013;Pulvermüller and Garagnani, 2014). In this model, the momentary full ignition of an APC corresponds to the recognition and semantic understanding of a single word, whereas the subsequent reverberant activity of the circuit is the material basis of verbal working memory.…”

Section: Semantic Somatotopy and Verbal Working Memory: Towards Neurosupporting

confidence: 79%

Brain correlates of action word memory

Shebani

Carota

Hauk

et al. 2018

Preprint

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When understanding language semantically related to actions, the motor cortex is active and may be sensitive to semantic information, for example about the body-part-relationship of displayed action-related words. Conversely, movements of the hands or feet can impair memory performance for arm- and leg-related action words respectively, suggesting that the role of motor systems extends to verbal working memory. Here, we studied brain correlates of verbal memory load for action-related words using event-related fMRI during the encoding and memory maintenance of word lists. Seventeen participants saw either four identical or four different words from the same category, semantically related to actions typically performed either with the arms or with the legs. After a variable delay of 4-14 seconds, they performed a nonmatching-to-sample task. Hemodynamic activity related to the information load of words at presentation was most prominent in left temporo-occipital and bilateral posterior-parietal areas. In contrast, larger demand on verbal memory maintenance produced greater activation in left premotor and supplementary motor cortex, along with posterior-parietal areas, indicating that verbal memory circuits for action-related words include the cortical action system. Somatotopic memory load effects of arm- and leg-related words were not present at the typical precentral loci where earlier studies had found such word-category differences in reading tasks, although traces of somatotopic semantic mappings were observed at more anterior cortical regions. These results support a neurocomputational model of distributed action-perception circuits (APCs), according to which language understanding is manifest as full ignition of APCs, whereas working memory is realized as reverberant activity gradually receding to multimodal prefrontal and lateral temporal areas.

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Section: Semantic Somatotopy and Verbal Working Memory: Towards Neurosupporting

confidence: 79%

Brain correlates of action word memory

Shebani

Carota

Hauk

et al. 2018

Preprint

Self Cite

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“…The model was constructed so as to reflect a range of properties of the human cortex; the main features included: (1) local (see Figure 1D) and area-specific global inhibitory mechanisms (Braitenberg, 1978b; Yuille and Geiger, 2003); (2) patchy, random and topographic connections, with probability of a synaptic link being established between two cells decreasing with their distance (Kaas, 1997; Braitenberg and Schüz, 1998); (3) presence of uniform noise (simulating spontaneous, baseline neuronal firing) in all network areas at all times (Rolls and Deco, 2010); and (4) Hebbian synaptic plasticity, simulating well-known phenomena of long-term potentiation (LTP) and depression (LTD) (Artola and Singer, 1993). These features are identical to those used in our previous versions of the architecture (Garagnani et al, 2008; Garagnani and Pulvermüller, 2011, 2013, 2016). Excitatory neurons are now modeled as leaky integrate-and-fire cells with adaptation, whereas our previous simulations used a “lumped” or mean-field approach, with each cell representing the average activity of a local pool or cluster of neurons (Wilson and Cowan, 1973; Eggert and van Hemmen, 2000).…”

Section: Methodsmentioning

confidence: 99%

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

Garagnani

Lucchese

Tomasello

et al. 2017

Front. Comput. Neurosci.

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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.

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“…Within-area sparse excitatory links (in gray) to and from e are limited to a (19 × 19) neighborhood (light-colored area); between-area excitatory projections (green and purple arcs) are topographic and target 19 × 19 neighborhoods in other areas (not depicted). Panel B has been adapted from Garagnani and Pulvermüller (2013); panels D and E have been adapted from Cortex, 57, Pulvermüller, F. and Garagnani, M., “From sensorimotor learning to memory cells in prefrontal and temporal association cortex: A neurocomputational study of disembodiment”, pp. 1–21, copyright 2014, with permission from Elsevier.…”

Section: Introductionmentioning

confidence: 99%

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex

2017

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The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory—a specifically human trait providing the foundation for language abilities—but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order “jumping links” between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage.SIGNIFICANCE STATEMENT Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage.

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Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas

Cited by 30 publications

References 115 publications

Brain correlates of action word memory

Brain correlates of action word memory

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

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex

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