Back to my home page.

Basal Ganglia neural network demonstrations, emergent v 7

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, Collins & Frank 2013, Wiecki & Frank 2013, and Franklin & Frank 2015 for original model papers).

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 emergent7.01 package on the emergent site.

  • Start here - network dynamics, gating, dopamine modulations of learning curves.
    This project contains a simplified Go/NoGo basal ganglia network and steps through the roles of the different structures and their modulation by dopamine, with basic replications of effects of Parkinson's and medications on reinforcement learning in rich (mostly rewarding) and lean (mostly punishing) environments. New users should start here.

  • Probabilistic selection task .
    This project is similar to above but implements the Probabilistic Selection task with transfer phase. Or use this project for more detailed investigations.

  • Probabilistic selection (PS) task simulations, tremor oscillations, various dopamine manipulations .
    In depth simulations of recorded 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). This simulation complements the above one by exposing the neural mechanisms that generate the behavioral effects.c

  • Weather Prediction task (probabilistic classification) simulations .
    Simulates incremental learning of the challenging and now classical Weather Prediction task, and the effects of dopamine depletion on this learning. It also includes the subthalamic nucleus (STN) and has a simple demonstration of tremor-like oscillations that emerge with dopamine depletion.

  • Task-set structured learning, hierarchical corticostriatal circuit . From Collins & Frank, 2013, Psychological Review. Includes two-stage cascaded BG loop circuit enabling hierarchical control of action selection and learning by generating task-set structure, generalizable to novel situations. The model selects among four different motor actions, and at the higher level, three possible task-sets, and simultaneously learns to create (or re-use) abstract task-sets while also learning the particular response mappings given the selected task-set, using pure reinforcement learning. This matlab script can be used for more detailed analysis of model output showing transfer, and here is an example mat file. Similarly, for more detailed analysis of a case in which there is incentive to clustering task-sets around context during initial learning, please use this matlab script. The computations of this model were linked to those of a higher level "C-TS" (context task-set) model based on a non-parametric Bayesian approach to clustering task-sets using a Chinese Restaurant Process. Here is a single zip file including simulations from the C-TS model in matlab.

  • Computational model of inhibitory control in prefrontal-basal ganglia circuits . From Wiecki & Frank, 2013, Psychological Review. Includes simulations of selective response inhibition tasks such as antisaccade and Simon task, and the global response inhibition stop-signal task. Captures various patterns of electrophysiology observed in striatum, frontal eye fields, subthalamic nucleus, superior colliculus, and elsewhere documented in such tasks, and their relation to behavioral accuracy and RT distributions. The script linked above includes a README file and Python code which calls emergent neural software and analyzes the output.

  • Role of cholinergic interneurons in adaptive reinforcement learning . From Franklin & Frank, 2015, eLife. Shows how feedback circuit between medium spiny neurons and tonically active neurons (TANs) can optimize learning in an approximately Bayesian fashion and improve performance in stochastic environments with reversal. TAN pauses regulate population entropy across spiny neurons and are in turn regulated by such entropy. Captures effects of TAN lesions on reversal and is approximated by Bayesian model of adaptive learning based on uncertainty. Bayesian model scripts in Python are here and here

    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).