ModelDB: Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)

Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)

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Accession:183014

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.
References:
1 . Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW (2015) Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot 9:13 [PubMed]
2 . Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Front Neurosci 10:28 [PubMed]

Model Information (Click on a link to find other models with that property)

Model Type:	Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):	Neocortex M1 L5B pyramidal pyramidal tract cell; Neocortex M1 L2/6 pyramidal intratelencephalic cell; Neocortex M1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neostriatum fast spiking interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s):	GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):	Gaba; Glutamate;
Simulation Environment:	NEURON; Python (web link to model);
Model Concept(s):	Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing; Motor control; Touch;
Implementer(s):	Neymotin, Sam [samn at neurosim.downstate.edu]; Dura, Salvador [ salvadordura at gmail.com];