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Basal Ganglia neural network demonstrationsThese projects are downloadable for use with the emergent neural simulator. Documentation is contained within each project. It is strongly suggested that before diving into these BG network simulations, first familiarize yourself with the emergent simulation package (both the software and the theoretical fundamentals, including neuronal and plasticity equations). It will also be helpful to read the more detailed description of the computational models and associated biology in the published modeling papers (see Frank, 2005, 2006, Collins & Frank 2013, Wiecki & Frank 2013, and Franklin & Frank 2015 for original model papers).
A change in the emergent codebase from earlier versions (6) affected the way that weights are set. We will continue to support these projects for the legacy version of the emergent code (v7). Note that these projects are not available for the newest versions of emergent 8 (given the various other changes that were made). To run these, download the LTS emergent7 package on the emergent site.
The models are implemented in the emergent neural simulator (Aisa et al., 2008) using a middle ground between biophysically detailed neurons and highly abstract connectionist units. Physiological properties of neuronal types in different BG nuclei are simulated by adjusting conductances and equilibrium potentials of neurons. Synaptic weights are adjusted using pure reinforcement learning as a function of changes in simulated dopamine levels and their effects on striatal postsynaptic targets. (see Frank, 2006 for a table of specific parameters and relation to BG function).