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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

A neural model of the mammalian vestibular system using the NEF. Boxes represent distinct neural populations and arrows represent projections between them. Inputs to the system are linear acceleration sensed by the left and right otoliths (AL, AR) and the angular velocity from the canals (ΩL and ΩR). From these, the system calculates inertial acceleration (I) using the formula developed by Angelaki et al. (1999). (For further details, see Eliasmith et al., 2002).
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Figure 2: A neural model of the mammalian vestibular system using the NEF. Boxes represent distinct neural populations and arrows represent projections between them. Inputs to the system are linear acceleration sensed by the left and right otoliths (AL, AR) and the angular velocity from the canals (ΩL and ΩR). From these, the system calculates inertial acceleration (I) using the formula developed by Angelaki et al. (1999). (For further details, see Eliasmith et al., 2002).

Mentions: For complex neural models, it is often useful to describe the system of interest at a higher level of abstraction, such as that shown in Figure 2. For this reason, we define heterogeneous groups of neurons (where individual neurons vary in terms of their neural properties such as bias current and gain) and projections between these groups. We can then use the NEF (Eliasmith and Anderson, 2003) as a method for realizing this high-level description using neural models with adjustable degrees of accuracy. The NEF provides not only a method for encoding and decoding time-varying representations using spike trains, but also a method for deriving linearly optimal synaptic connection weights to transform and combine these representations. This approach combines work from a variety of researchers, most notably Georgopoulos et al. (1986), Rieke et al. (1999), Salinas and Abbott (1994), and Seung (1996).


Python scripting in the nengo simulator.

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

A neural model of the mammalian vestibular system using the NEF. Boxes represent distinct neural populations and arrows represent projections between them. Inputs to the system are linear acceleration sensed by the left and right otoliths (AL, AR) and the angular velocity from the canals (ΩL and ΩR). From these, the system calculates inertial acceleration (I) using the formula developed by Angelaki et al. (1999). (For further details, see Eliasmith et al., 2002).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: A neural model of the mammalian vestibular system using the NEF. Boxes represent distinct neural populations and arrows represent projections between them. Inputs to the system are linear acceleration sensed by the left and right otoliths (AL, AR) and the angular velocity from the canals (ΩL and ΩR). From these, the system calculates inertial acceleration (I) using the formula developed by Angelaki et al. (1999). (For further details, see Eliasmith et al., 2002).
Mentions: For complex neural models, it is often useful to describe the system of interest at a higher level of abstraction, such as that shown in Figure 2. For this reason, we define heterogeneous groups of neurons (where individual neurons vary in terms of their neural properties such as bias current and gain) and projections between these groups. We can then use the NEF (Eliasmith and Anderson, 2003) as a method for realizing this high-level description using neural models with adjustable degrees of accuracy. The NEF provides not only a method for encoding and decoding time-varying representations using spike trains, but also a method for deriving linearly optimal synaptic connection weights to transform and combine these representations. This approach combines work from a variety of researchers, most notably Georgopoulos et al. (1986), Rieke et al. (1999), Salinas and Abbott (1994), and Seung (1996).

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