Basal Ganglia neural network demonstrations
These projects are downloadable for use with
emergent neural simulator.
Documentation is contained within
each project. It is strongly suggested that before diving into these
simulations, first familiarize
yourself with the emergent simulation package (both the software and
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
(see Frank, 2005, 2006 for original model papers, Cohen & Frank,
2009 and Wiecki & Frank, 2010, 2013 for recent reviews).
New projects for emergent version
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
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.
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
(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.
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
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.
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
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