Limits...
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.


Topographica software screenshot. This image shows a sample session from Topographica version 0.9.3, available freely at topographica.org. Here the user is studying the behavior of an orientation map in the primary visual cortex (V1), using a model of photoreceptors as the input to the Retina, ON and OFF RGC/LGN cells, and a simple V1 model. The window at the left labeled “Orientation Preference” shows a self-organized orientation map in V1. The window labeled “Activity” shows (from left to right) a sample visual image input to the retina, the ON and OFF channel responses to that input, and (on the right) an orientation-color-coded representation of activity in the V1 Sheet of neurons. The input patterns were generated using the Test Pattern “Preview” dialog at the right. The window labeled “Connection Fields” shows the strengths of the connections to one neuron in V1. The lateral weights for a 9 × 9 sampling of the V1 neurons are shown in the “Weights Array” window in the center; neurons tend to connect to their immediate neighbors and to distant neurons of the same orientation. The “Topographic Mapping” window shows how retinotopy has been distorted by the orientation map, and the “FFT Plot” shows that the orientation map repeats regularly in all dimensions, as in animals. This type of large-scale analysis is difficult with other simulators, but typically requires no new coding or software development once a network simulation has a basic connection to Topographica.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2666198&req=5

Figure 2: Topographica software screenshot. This image shows a sample session from Topographica version 0.9.3, available freely at topographica.org. Here the user is studying the behavior of an orientation map in the primary visual cortex (V1), using a model of photoreceptors as the input to the Retina, ON and OFF RGC/LGN cells, and a simple V1 model. The window at the left labeled “Orientation Preference” shows a self-organized orientation map in V1. The window labeled “Activity” shows (from left to right) a sample visual image input to the retina, the ON and OFF channel responses to that input, and (on the right) an orientation-color-coded representation of activity in the V1 Sheet of neurons. The input patterns were generated using the Test Pattern “Preview” dialog at the right. The window labeled “Connection Fields” shows the strengths of the connections to one neuron in V1. The lateral weights for a 9 × 9 sampling of the V1 neurons are shown in the “Weights Array” window in the center; neurons tend to connect to their immediate neighbors and to distant neurons of the same orientation. The “Topographic Mapping” window shows how retinotopy has been distorted by the orientation map, and the “FFT Plot” shows that the orientation map repeats regularly in all dimensions, as in animals. This type of large-scale analysis is difficult with other simulators, but typically requires no new coding or software development once a network simulation has a basic connection to Topographica.

Mentions: Models supported natively by Topographica typically consist of a collection of topographic maps in cortical or subcortical regions, such as an auditory or visual processing pathway. Figure 2 shows an example simulation along with various types of analysis and plotting. This simple model consists of four separate populations of neurons, called Sheets: one sheet of retinal photoreceptors (labeled Retina), a sheet of ON retinal ganglion cell (RGC)/lateral geniculate nucleus (LGN) cells labeled LGNON, a sheet of OFF cells labeled LGNOFF, and a sheet of V1 pyramidal cells labeled V1. Neurons in each sheet are arranged topographically, with similar properties but at different spatial locations.


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

Bednar JA - Front Neuroinform (2009)

Topographica software screenshot. This image shows a sample session from Topographica version 0.9.3, available freely at topographica.org. Here the user is studying the behavior of an orientation map in the primary visual cortex (V1), using a model of photoreceptors as the input to the Retina, ON and OFF RGC/LGN cells, and a simple V1 model. The window at the left labeled “Orientation Preference” shows a self-organized orientation map in V1. The window labeled “Activity” shows (from left to right) a sample visual image input to the retina, the ON and OFF channel responses to that input, and (on the right) an orientation-color-coded representation of activity in the V1 Sheet of neurons. The input patterns were generated using the Test Pattern “Preview” dialog at the right. The window labeled “Connection Fields” shows the strengths of the connections to one neuron in V1. The lateral weights for a 9 × 9 sampling of the V1 neurons are shown in the “Weights Array” window in the center; neurons tend to connect to their immediate neighbors and to distant neurons of the same orientation. The “Topographic Mapping” window shows how retinotopy has been distorted by the orientation map, and the “FFT Plot” shows that the orientation map repeats regularly in all dimensions, as in animals. This type of large-scale analysis is difficult with other simulators, but typically requires no new coding or software development once a network simulation has a basic connection to Topographica.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Topographica software screenshot. This image shows a sample session from Topographica version 0.9.3, available freely at topographica.org. Here the user is studying the behavior of an orientation map in the primary visual cortex (V1), using a model of photoreceptors as the input to the Retina, ON and OFF RGC/LGN cells, and a simple V1 model. The window at the left labeled “Orientation Preference” shows a self-organized orientation map in V1. The window labeled “Activity” shows (from left to right) a sample visual image input to the retina, the ON and OFF channel responses to that input, and (on the right) an orientation-color-coded representation of activity in the V1 Sheet of neurons. The input patterns were generated using the Test Pattern “Preview” dialog at the right. The window labeled “Connection Fields” shows the strengths of the connections to one neuron in V1. The lateral weights for a 9 × 9 sampling of the V1 neurons are shown in the “Weights Array” window in the center; neurons tend to connect to their immediate neighbors and to distant neurons of the same orientation. The “Topographic Mapping” window shows how retinotopy has been distorted by the orientation map, and the “FFT Plot” shows that the orientation map repeats regularly in all dimensions, as in animals. This type of large-scale analysis is difficult with other simulators, but typically requires no new coding or software development once a network simulation has a basic connection to Topographica.
Mentions: Models supported natively by Topographica typically consist of a collection of topographic maps in cortical or subcortical regions, such as an auditory or visual processing pathway. Figure 2 shows an example simulation along with various types of analysis and plotting. This simple model consists of four separate populations of neurons, called Sheets: one sheet of retinal photoreceptors (labeled Retina), a sheet of ON retinal ganglion cell (RGC)/lateral geniculate nucleus (LGN) cells labeled LGNON, a sheet of OFF cells labeled LGNOFF, and a sheet of V1 pyramidal cells labeled V1. Neurons in each sheet are arranged topographically, with similar properties but at different spatial locations.

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.