Models that contain the Modeling Application : Emergent/PDP++ (Home Page)

(<a href="http://grey.colorado.edu/emergent/index.php/Main_Page">Emergent </a>(a major rewrite of <a href="http://www.cnbc.cmu.edu/Resources/PDP++/PDP++.html">PDP++</a>) is a comprehensive simulation environment for creating complex, sophisticated models of the brain and cognitive processes using neural network models. ... It includes a full GUI environment for constructing networks and the input/output patterns for the networks to process, and many different analysis tools for understanding what the networks are doing. It has a new tabbed-browser style interface with full 3D graphics (via OpenGL and Open Inventor/Coin3D), and powerful new GUI programming tools and data processing and analysis capabilities. The PDP++ software is a neural-network simulation system written in C++. It represents the next generation of the PDP software originally released with the McClelland and Rumelhart "Explorations in Parallel Distributed Processing Handbook", MIT Press, 1987. )
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    Models   Description
1.  Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.
2.  Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)
"... We describe a connectionist model of human attentional networks to explore the possible interplays among the networks from a computational perspective. This model is developed in the framework of leabra (local, error-driven, and associative, biologically realistic algorithm) and simultaneously involves these attentional networks connected in a biologically inspired way. ... We evaluate the model by simulating the empirical data collected on normal human subjects using the Attentional Network Test (ANT). The simulation results fit the experimental data well. In addition, we show that the same model, with a single parameter change that affects executive control, is able to simulate the empirical data collected from patients with schizophrenia. This model represents a plausible connectionist explanation for the functional structure and interaction of human attentional networks."
3.  Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).

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