2013
DOI: 10.1016/j.bandl.2013.02.001
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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

Abstract: Highlights► We simulate emergence of decisions to speak and act in a model of the human brain. ► Action intentions spontaneously emerge due to the reverberation of neuronal noise. ► Spontaneous ignition preferentially occurs in higher-association, multimodal areas. ► Connectivity and learning explain cortical dynamics underlying action decisions. ► Connectivity and learning explain natural emergence of cortical specialisation.

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Cited by 30 publications
(32 citation statements)
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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%
“…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%
“…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%
“…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%