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Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components.

Bednar JA - Front Neuroinform (2009)

Bottom Line: These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators.In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica.Additional examples show how to interface easily with models in other types of simulators.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.

ABSTRACT
Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate.

No MeSH data available.


Example architecture. This Figure shows the simulation from Figure 3 running in Topographica. On the input sheet is a 2D Gaussian pattern generated by Topographica and presented to the underlying spiking network, with the resulting spike count responses shown on the ON and OFF RGC sheets. The type of input pattern and its parameters can be manipulated as shown.
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Figure 4: Example architecture. This Figure shows the simulation from Figure 3 running in Topographica. On the input sheet is a 2D Gaussian pattern generated by Topographica and presented to the underlying spiking network, with the resulting spike count responses shown on the ON and OFF RGC sheets. The type of input pattern and its parameters can be manipulated as shown.

Mentions: Figure 3 shows the Python code for wrapping this network as a Photoreceptor Sheet (Photoreceptors), a connection to PyNN (PyNNR), and two ganglion cell Sheets (ON_RGC and OFF_RGC), and Figure 4 shows the resulting simulation running in Topographica. The example code would be nearly the same for interfacing to any other external simulation that consists of two-dimensional arrays of neurons, and so we will step through each part of this code to show how the interface is achieved. In each case, the relevant line of code is marked with a circled number, which can be found on the code listing. Note that this code constitutes the complete, runnable model specification for Topographica; it is not a code excerpt or a high-level interface to some underlying, complicated interfacing code, but instead it is all that was required to connect to and run the external simulation within Topographica.


Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components.

Bednar JA - Front Neuroinform (2009)

Example architecture. This Figure shows the simulation from Figure 3 running in Topographica. On the input sheet is a 2D Gaussian pattern generated by Topographica and presented to the underlying spiking network, with the resulting spike count responses shown on the ON and OFF RGC sheets. The type of input pattern and its parameters can be manipulated as shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666198&req=5

Figure 4: Example architecture. This Figure shows the simulation from Figure 3 running in Topographica. On the input sheet is a 2D Gaussian pattern generated by Topographica and presented to the underlying spiking network, with the resulting spike count responses shown on the ON and OFF RGC sheets. The type of input pattern and its parameters can be manipulated as shown.
Mentions: Figure 3 shows the Python code for wrapping this network as a Photoreceptor Sheet (Photoreceptors), a connection to PyNN (PyNNR), and two ganglion cell Sheets (ON_RGC and OFF_RGC), and Figure 4 shows the resulting simulation running in Topographica. The example code would be nearly the same for interfacing to any other external simulation that consists of two-dimensional arrays of neurons, and so we will step through each part of this code to show how the interface is achieved. In each case, the relevant line of code is marked with a circled number, which can be found on the code listing. Note that this code constitutes the complete, runnable model specification for Topographica; it is not a code excerpt or a high-level interface to some underlying, complicated interfacing code, but instead it is all that was required to connect to and run the external simulation within Topographica.

Bottom Line: These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators.In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica.Additional examples show how to interface easily with models in other types of simulators.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.

ABSTRACT
Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate.

No MeSH data available.