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Python scripting in the nengo simulator.

Stewart TC, Tripp B, Eliasmith C - Front Neuroinform (2009)

Bottom Line: Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface.Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas.Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

View Article: PubMed Central - PubMed

Affiliation: Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.

ABSTRACT
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

No MeSH data available.


Related in: MedlinePlus

Basic usage of the Python scripting interface to interact programmatically with a neural model.
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Figure 4: Basic usage of the Python scripting interface to interact programmatically with a neural model.

Mentions: As an example, Figure 4 shows the Python scripting interface being used to duplicate an existing group of neurons (groupA, created using the point-and-click interface). This duplication is performed using the standard Java clone() method. The name of this new neural group is then changed to groupB and it is added to the existing network. These tasks can also be performed via the graphical interface; this example is meant to show the direct relationship between the underlying Java entities, the graphically displayed objects, and the Python scripting.


Python scripting in the nengo simulator.

Stewart TC, Tripp B, Eliasmith C - Front Neuroinform (2009)

Basic usage of the Python scripting interface to interact programmatically with a neural model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666197&req=5

Figure 4: Basic usage of the Python scripting interface to interact programmatically with a neural model.
Mentions: As an example, Figure 4 shows the Python scripting interface being used to duplicate an existing group of neurons (groupA, created using the point-and-click interface). This duplication is performed using the standard Java clone() method. The name of this new neural group is then changed to groupB and it is added to the existing network. These tasks can also be performed via the graphical interface; this example is meant to show the direct relationship between the underlying Java entities, the graphically displayed objects, and the Python scripting.

Bottom Line: Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface.Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas.Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

View Article: PubMed Central - PubMed

Affiliation: Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.

ABSTRACT
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

No MeSH data available.


Related in: MedlinePlus