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In this work, we took the analysis of neural interactions change in M1 of a monkey during the adaptation process for it to complete reach-to-grasp tasks with external perturbation across days. BN model was applied to model and evaluate neural interaction networks from recorded neural spike trains data of each set. Our results showed that for delay period across sets, interaction level of neural network...
Applying a dynamic Bayesian network model can help detect neural interactions and analyze the characteristics of a monkey's motor cortex during reach-to-grasp tasks.
In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond...
In this work, we recorded the spike train data from a rhesus monkey's motor cortex while it was trained to perform reach-to-grasp task at different directions and orientations. The neurons were divided into several classes according to their neuronal activities, and each class of neuron encoded a kind of functional parameter, such as direction-related, orientation-related and so on. Then we applied...
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