Back to my home page.

Basal Ganglia neural network demonstrations

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).


  • New projects for emergent version 7
    A change in the emergent codebase affected the way that weights are set. If you are using newer versions of emergent (7.0 and later) you should use the projects in this folder, which have been updated to accommodate this change. We will continue to support these projects for the legacy version of the emergent code (v7.0.1)


  • 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 earlier versions of emergent in which original projects were built, below. If you are using 6.2-6.4 and later you should use the projects in this folder, which have been updated to accommodate this change.


    Currently the below projects work well 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).