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

"... 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. ... "

Model Type: Realistic Network

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

Receptors: GabaA; AMPA; NMDA

Transmitters: Gaba; Glutamate

Model Concept(s): Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing

Simulation Environment: NEURON

Implementer(s): Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Chadderdon, George [gchadder3 at gmail.com]

References:

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 computation. 25 [PubMed]