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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ... Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan. - : arXiv, 2020 BASE Show details 2 Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs ... Zhong, Junpei; Peniak, Martin; Tani, Jun. - : arXiv, 2016 BASE Show details 3 Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan. - 2014 BASE Show details 4 Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan. - : Frontiers Media S.A., 2014 Abstract: The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. Keyword: Neuroscience URL: https://doi.org/10.3389/fnbeh.2014.00022 http://www.ncbi.nlm.nih.gov/pubmed/24550798 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912404 BASE Hide details

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