Cortical plasticity is one of the main features that enable our capability to learn and adapt in our environment. Indeed, the cerebral cortex has the ability to URLanize itself through two distinct forms of plasticity: the structural plasticity that creates (sprouting) or cuts (pruning) synaptic connections between neurons, and the synaptic plasticity that modifies the synaptic connections strength. These mechanisms are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. ... To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion (convergence) and inter-modal activation (divergence). ... In this paper, we build a brain-inspired neural system based on the Reentry principles, using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of both convergence and divergence mechanisms in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. Finally, we implement our system on the Iterative Grid, a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by our model , where the system's topology is not fixed by the user but learned along the system's experience through URLanization.
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex URLanizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence) . Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM , where the system's topology is not fixed by the user but learned along the system's experience through URLanization.
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capability
ability
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has the ability to URLanize itself through two distinct forms of plasticity: the structural plasticity that creates (sprouting) or cuts (pruning) synaptic connections between neurons, and the synaptic plasticity that modifies the synaptic connections strength. These mechanisms
URLanizes itself through structural and synaptic plasticity mechanisms that
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...
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To model such a behavior, Edelman and Damasio proposed respectively the Reentry and the Convergence Divergence Zone frameworks where bi-directional neural communications can lead to both multimodal fusion
In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts
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and inter-modal activation
. Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated
D
...
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In this paper, we build
In this work, we propose the Reentrant Self-Organizing Map (ReSOM),
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Reentry principles,
reentry theory
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both convergence and divergence mechanisms
the ReSOM
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Finally, we implement our system on the Iterative Grid,
The proposed model is implemented on
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our model
the ReSOM