Basal Ganglia neural network demonstrations

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Basal Ganglia neural network demonstrations, emergent v 6.1 and earlier

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, Cohen & Frank, 2009 and Wiecki & Frank, 2010, 2013 for recent reviews).

Start here - network dynamics, gating, dopamine modulations.
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.

Probabilistic selection (PS) task simulations (Go/NoGo associations) .
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).

Probabilistic selection (choose-A vs avoid-B dissociations, differential medication effects) .
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).

Probabilistic reversal simulations .
Simulates adverse effects of dopamine medications on reversal learning, sparing acquisition.

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.

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.

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).