Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing

Hinaut, Xavier; Lance, Florian; Droin, Colas; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

doi:10.1016/j.bandl.2015.08.002

Cited by 26 publications

(27 citation statements)

References 72 publications

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“…One of the earliest, most influential connectionist models of memory (McClelland and Rumelhart, 1985), for example, was able to account for basic differences in repetition priming of spoken words and pseudowords (a word being represented as a distributed pattern of activity across a layer of units). Nowadays, a new generation of large-scale neural-network models are being increasingly used in the study of memory and language processes (e.g., Wennekers et al, 2006; Herman et al, 2013; Pulvermüller and Garagnani, 2014; Hinaut et al, 2015; Rolls and Deco, 2015), which are able to elucidate the underlying brain mechanisms on the basis of neurobiologically realistic learning and anatomical connectivity, and explain neuroimaging data (Husain et al, 2004; Pulvermüller et al, 2014; Garagnani and Pulvermüller, 2016). However, to date, a neuromechanistic account directly linking the different high-frequency neurophysiological responses induced by familiar word and unknown pseudoword stimuli to corresponding differential oscillatory behavior of underlying large-scale neuronal populations is still missing.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…outputs) are trained to linearly extract information from the high-dimensional non-linear dynamics of the reservoir. Several authors have taken advantage of this paradigm to model cortical areas such as PFC 42,43,44,45 because most of the connections are not trained, especially the recurrrent ones. Another reason to use reservoir for PFC modelling is because PFC also hosts high-dimensional non-linear dynamics [10].…”

Section: Discussionmentioning

confidence: 99%

A Robust Model of Gated Working Memory

Strock

Hinaut

Rougier

2019

Preprint

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Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network. We introduce a robust yet simple reservoir model of gated working memory with instantaneous updates. The model is able to store an arbitrary real value at random time over an extended period of time. The dynamics of the model is a line attractor that learns to exploit reentry and a nonlinearity during the training phase using only a few representative values. A deeper study of the model shows that there is actually a large range of hyper parameters for which the results hold (number of neurons, sparsity, global weight scaling, etc.) such that any large enough population, mixing excitatory and inhibitory neurons can quickly learn to realize such gated working memory. This suggests this property could be an implicit property of any random population, that can be acquired through learning. Furthermore, considering working memory to be a physically open but functionally closed system, we give account on some counter-intuitive electrophysiological recordings.

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“…Likewise, within our larger iCub framework, recurrent “reservoir” networks based on cortico-striatal neuroanatomy provided a learning capability for comprehension of grammatical constructions in the service of cooperative interaction (Hinaut and Dominey, 2013; Hinaut et al, 2014, 2015). The use of a mental work space (that we refer to as the OPC) allows the contents of the ABM in the form of action rules to be used to predict the outcome of one's own and other's actions and beliefs.…”

Section: Discussionmentioning

confidence: 99%

“…From the perspective of the studies reviewed below, the answer to this question is specified by the architecture in Figure 2. That is, the robot has a perceptual system to observe actions and a motor system to perform actions (Lallée et al, 2011), a language system to understand and produce simple language related to action (Dominey and Boucher, 2005b; Hinaut and Dominey, 2013; Hinaut et al, 2014, 2015), and the ABM infrastructure described above. While these are minimal requirements, one can also consider that these could be embedded as part of a more complete system (Lallee and Verschure, 2015).…”

Section: Toward Neisser's Four Levels Of Selfmentioning

confidence: 99%

The Role of Autobiographical Memory in the Development of a Robot Self

Pointeau

Dominey

2017

Front. Neurorobot.

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This article briefly reviews research in cognitive development concerning the nature of the human self. It then reviews research in developmental robotics that has attempted to retrace parts of the developmental trajectory of the self. This should be of interest to developmental psychologists, and researchers in developmental robotics. As a point of departure, one of the most characteristic aspects of human social interaction is cooperation—the process of entering into a joint enterprise to achieve a common goal. Fundamental to this ability to cooperate is the underlying ability to enter into, and engage in, a self-other relation. This suggests that if we intend for robots to cooperate with humans, then to some extent robots must engage in these self-other relations, and hence they must have some aspect of a self. Decades of research in human cognitive development indicate that the self is not fully present from the outset, but rather that it is developed in a usage-based fashion, that is, through engaging with the world, including the physical world and the social world of animate intentional agents. In an effort to characterize the self, Ulric Neisser noted that self is not unitary, and he thus proposed five types of self-knowledge that correspond to five distinct components of self: ecological, interpersonal, conceptual, temporally extended, and private. He emphasized the ecological nature of each of these levels, how they are developed through the engagement of the developing child with the physical and interpersonal worlds. Crucially, development of the self has been shown to rely on the child's autobiographical memory. From the developmental robotics perspective, this suggests that in principal it would be possible to develop certain aspects of self in a robot cognitive system where the robot is engaged in the physical and social world, equipped with an autobiographical memory system. We review a series of developmental robotics studies that make progress in this enterprise. We conclude with a summary of the properties that are required for the development of these different levels of self, and we identify topics for future research.

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Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing

Cited by 26 publications

References 72 publications

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

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

A Robust Model of Gated Working Memory

The Role of Autobiographical Memory in the Development of a Robot Self

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