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.
Highlights
Large-scale neural modeling requires software tools that support efficient simulation of hundreds of thousands of neurons, and provide researchers with high-level organizational tools
For high-level organization, Nengo makes use of the neural engineering framework (NEF; Eliasmith and Anderson, 2003), which provides methods for abstractly describing the representations and transformations involved in a neural model and how they relate to spiking behavior
To provide access to the broad range of functionality we require, we integrated a Python language scripting system into the simulator. This enables a variety of novel features, including the inspection and modification of running models, the ability to script common experimental tasks, and the integration of non-neural cognitive models. We describe this system, discuss the features related to its use of Python, and provide an extended example of ongoing research that has directly benefited from these abilities
Summary
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). 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
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