MicrocircuitDB: Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)

Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)

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Accession:150245

"... We developed a model of sensory and motor neocortex consisting of 704 spiking model-neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a 2-joint virtual arm to reach to a fixed target. ... "
Reference:
1 . Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW (2013) Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex. Neural Comput 25:3263-93 [PubMed]

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Model Type:	Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):	Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) 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;
Model Concept(s):	Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing;
Implementer(s):	Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Chadderdon, George [gchadder3 at gmail.com];