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.
Model Type: Realistic Network
Cell Type(s): Neocortex M1 L5B pyramidal pyramidal tract GLU cell; Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex M1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neostriatum fast spiking interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron
Model Concept(s): Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing; Motor control; Touch
Simulation Environment: NEURON; Python (web link to model)
Implementer(s): Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Dura, Salvador [ salvadordura at gmail.com]
References:
Dura-Bernal S et al. (2015). Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Frontiers in neurorobotics. 9 [PubMed]
Dura-Bernal S et al. (2016). Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Frontiers in neuroscience. 10 [PubMed]