"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ..."
Model Type: Realistic Network
Region(s) or Organism(s): Neocortex
Cell Type(s): Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron
Transmitters: Dopamine; Gaba; Glutamate
Model Concept(s): Simplified Models; Synaptic Plasticity; Long-term Synaptic Plasticity; Reinforcement Learning; Reward-modulated STDP
Simulation Environment: NEURON
Implementer(s): Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Chadderdon, George [gchadder3 at gmail.com]
References:
Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW. (2012). Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex. PloS one. 7 [PubMed]