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Data
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Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
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We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
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Neocortex M1 L5B pyramidal pyramidal tract GLU cell Show
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Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell Show
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Neocortex M1 interneuron basket PV GABA cell Show
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Neocortex fast spiking (FS) interneuron Show
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Neostriatum fast spiking interneuron Show
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Neocortex spiking regular (RS) neuron Show
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Neocortex spiking low threshold (LTS) neuron Show
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Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW (2015) Show
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Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Show
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arm2dms_modeldb
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Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org] Show
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Dura, Salvador [ salvadordura at gmail.com] Show
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Motor control
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