Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)

"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons. The large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ..."

Model Type: Realistic Network

Cell Type(s): Abstract Izhikevich neuron

Receptors: GabaA; GabaB; NMDA; AMPA

Transmitters: Glutamate; Gaba

Model Concept(s): Learning; Reinforcement Learning; Reward-modulated STDP; STDP; Motor control; Sensory processing

Simulation Environment: NEURON; Python

Implementer(s): Dura-Bernal, Salvador [salvadordura at]; Kerr, Cliff [cliffk at]


Lytton WW et al. (2017). Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue). 61(2/3)

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