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Chaotic mimic robots (Buscarino et al.)
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This model implements a bio-inspired control system based on mirror neurons on a babble robot, in order to analyze if it can imitate the trajectory of the other chaotically driven robot. The simulation results show that the observer robot is able to synchronize its trajectory to that of the observed robot and mirror neuron-like properties can be found in the neurons of the trained network.
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Development of Goal-Specificity in Mirror Neurons (Thill - Svensson - Ziemke)
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A model that gives a biologically plausible mechanism for the development of goal-specificity within some mirror neurons.
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Dual-route Imitation Model (Demiris - Hayes)
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This model combines two architectures (one each for passive and active imitation modeling) to generate a model that can imitate, as well as acquire, a variety of movements including unknown, partially known, and fully known sequences of movements.
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Goal-related action and mirroring via neuronal chains in the parietal lobe (Chersi - Ferrari - Fogassi)
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This model simulates basic motor actions (grasping for eating or grasping for placement) and the observation/recognition of those actions using groups of neurons organized into chains within the inferior parietal lobe (IPL).
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HAMMER - Hierarchical Attentive Multiple Models for Execution and Recognition of Actions (Demiris - Khadhouri)
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The HAMMER model has dual roles of competitively selecting and executing an action as well as perceiving the action when being performed by a demonstrator. It uses a series of inverse models which output commands to achieve a set of goals as well as forward models to predict the next state of the controlled system. The prediction is then verified at the next time step and fed back into the inverse model to tune its confidence parameters. That way, the inverse model can be constantly tuned to produce more accurate output results, and thus the overall system will also produce more accurate results. A top-down control of attention is also integrated using one or a combination of two different methods: Round-Robin and Winner-Take-All in order to decrease computation and increase performance.
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Model: Goal-Specificity in Mirror Neurons Development (Thill - Svensson - Ziemke)
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This computational model is based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed.
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Neural Mechanisms and Models Underlying Joint Action (Chersi)
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To describe a biologically constraint neural network model of the motor and mirror systems during joint action. In this model, motor sequences are encoded as independent neuronal chains that represent concatenations of elementary motor acts leading to a specific goal.
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Neuronal Action Chains (Chersi - Ferrari - Fogassi)
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A biologically inspired neural network architecture that models mechanisms of motor sequence execution and recognition.
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Neuronal Chains for Actions in the Inferior Parietal Lobe (Chersi - Ferrari - Fogassi)
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A biologically inspired neural network architecture that models mechanisms of motor sequences execution and recognition. The paper also proposes a mechanism for the formation of new neural chains by linking together in a sequential manner neurons that represent subsequent motor acts, thus producing goal-directed sequences.
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Predictive Coding for Action Understanding in the Mirror Neuron System (Friston - Mattout - Kilner)
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A model of the mirror-neuron system (MNS) implementing a Bayes-optimal perception of actions emitted both by oneself or others. The premise of this article is that mirror neurons emerge naturally in any agent that acts on its environment to avoid surprising events. The use of predictive coding in conjunction with the MNS allows the model to predict the actions of self or others using proprioceptive signals. Ultimately, this supports the formulation of action as active inference.
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Schema Design and Implementation of the Grasp-Related Mirror Neuron System (Oztop - Arbib)
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Use neural network to learn and recognize simulated hand states and interactions with perceived affordance.
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