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Basal Ganglia neural network demonstrations, older versions of emergent

These 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 for original model papers, Collins & Frank, 2013/14 and Wiecki & Frank, 2010, 2013 for recent reviews).


  • projects for emergent version 6.2-6.4
    A change in the emergent codebase affected the way that inhibitory projection weights were scaled relative to excitatory weights, compared to yet earlier versions of emergent in which original projects were built, below. If you are using 6.2-6.4 you should use the projects in this folder, which were updated to accommodate this change.


    Currently the below projects work well for earlier versions, up to emergent version 6.1: a change in code-base for 6.2 alters the dynamics of BG functioning (see above). Most of the below projects have been updated to accommodate this, so just click on the link above if you are using recent version of emergent. For the others that you do not see in that directory, please install version 6.1 (or earlier) and use the projects below.


  • Previous simulations for emergent version 6.1 and earlier.

  • Previous simulations without inhibitory interneurons (emergent version 6.1 and earlier). Earlier demonstrations of the majority of above effects in a model where striatal activity is controlled with a k-winner-take-all mathematical approximation. This model is functional, but some manipulations are not possible with it (e.g., selective manipulations of D2 receptors); see technical notes in other projects.



    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).