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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 for original model papers, Collins & Frank, 2013/14 and Wiecki & Frank, 2010, 2013 for recent reviews).
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)
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.
This project contains a simplified Go/NoGo basal ganglia network and steps through the roles of the different structures and their modulation by dopamine. New users should start here.
In depth simulations of Go and NoGo striatal valuation signals and how these are modulated by dopamine manipulations (depletion and medication effects), including differential roles of D1 and D2 receptors, sensitivity to dopamine bursts and pauses, and separable roles of dopamine on both learning and choice incentive (expression of learning).
Simulates the PS task and the observed dissociations on choice accuracy in choose-A and avoid-B conditions, and how these are affected by different medications during learning and expression of learning and choice incentive. These simulations are complementary to the prior ones, which only investigate effects on striatal associations. These simulations are carried out in a four-response network rather than two-alternative choice (see documentation in this and above for explanation).
Simulates adverse effects of dopamine medications on reversal learning, sparing acquisition.
Simulates incremental learning of the challenging and now classical Weather Prediction task, and the effects of dopamine depletion on this learning.