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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.


Retinotopic and orientation map in V1. Given a particular fixation point (marked with a red + symbol above), the visual field seen by an animal can be divided into a regular grid, with each square representing a 1° × 1° area of visual space. In cortical area V1 of mammals, neurons are arranged into a retinotopic map, with nearby neurons responding to nearby areas of the retina. As an example, the image on the right shows the retinotopic map on the surface of V1 of a tree shrew for an 8° × 7° area of visual space (adapted from Bosking et al., 2002 with permission; scale bar is 1 mm). A stimulus presented in a particular location in visual space (such as the thick black bar shown) evokes a response centered around the corresponding grid square in V1 (6°, 2°). Which specific neurons respond within that general area, however, depends on the orientation of the stimulus. The V1 map is color coded with the preferred orientation of neurons in each location; e.g. the black bar shown at left will primarily activate neurons colored in purple in the corresponding V1 grid squares. Similar maps could be plotted for this same area showing preference for other visual features, such as motion direction, spatial frequency, color, disparity, and eye preference (depending on species). Other cortical areas are arranged into topographic maps for other sensory modalities, such as touch and audition, and for motor outputs. Topographica is designed to simulate any of these cortical or subcortical areas.
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Figure 1: Retinotopic and orientation map in V1. Given a particular fixation point (marked with a red + symbol above), the visual field seen by an animal can be divided into a regular grid, with each square representing a 1° × 1° area of visual space. In cortical area V1 of mammals, neurons are arranged into a retinotopic map, with nearby neurons responding to nearby areas of the retina. As an example, the image on the right shows the retinotopic map on the surface of V1 of a tree shrew for an 8° × 7° area of visual space (adapted from Bosking et al., 2002 with permission; scale bar is 1 mm). A stimulus presented in a particular location in visual space (such as the thick black bar shown) evokes a response centered around the corresponding grid square in V1 (6°, 2°). Which specific neurons respond within that general area, however, depends on the orientation of the stimulus. The V1 map is color coded with the preferred orientation of neurons in each location; e.g. the black bar shown at left will primarily activate neurons colored in purple in the corresponding V1 grid squares. Similar maps could be plotted for this same area showing preference for other visual features, such as motion direction, spatial frequency, color, disparity, and eye preference (depending on species). Other cortical areas are arranged into topographic maps for other sensory modalities, such as touch and audition, and for motor outputs. Topographica is designed to simulate any of these cortical or subcortical areas.

Mentions: In mammals, much of the cortical surface (and many subcortical structures) can be partitioned into topographic maps (Kaas, 1997; Van Essen et al., 2001). These maps contain systematic two-dimensional representations of features relevant to sensory and motor processing, such as retinal position, sound frequency, line orientation, and motion direction (Blasdel, 1992; Merzenich et al., 1975; Ohki et al., 2005; Weliky et al., 1996; Xu et al., 2007). Figure 1 shows an example retinotopic and orientation map from the primary visual cortex (V1). Understanding the development and function of topographic maps is crucial for understanding brain function, and will require integrating large-scale experimental imaging results with single-unit studies of the individual neurons and their connections that make up these maps. In principle, computational modeling can help make these links explicit, in order to explain how topographic maps can emerge from the behavior of single neurons.


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

Bednar JA - Front Neuroinform (2009)

Retinotopic and orientation map in V1. Given a particular fixation point (marked with a red + symbol above), the visual field seen by an animal can be divided into a regular grid, with each square representing a 1° × 1° area of visual space. In cortical area V1 of mammals, neurons are arranged into a retinotopic map, with nearby neurons responding to nearby areas of the retina. As an example, the image on the right shows the retinotopic map on the surface of V1 of a tree shrew for an 8° × 7° area of visual space (adapted from Bosking et al., 2002 with permission; scale bar is 1 mm). A stimulus presented in a particular location in visual space (such as the thick black bar shown) evokes a response centered around the corresponding grid square in V1 (6°, 2°). Which specific neurons respond within that general area, however, depends on the orientation of the stimulus. The V1 map is color coded with the preferred orientation of neurons in each location; e.g. the black bar shown at left will primarily activate neurons colored in purple in the corresponding V1 grid squares. Similar maps could be plotted for this same area showing preference for other visual features, such as motion direction, spatial frequency, color, disparity, and eye preference (depending on species). Other cortical areas are arranged into topographic maps for other sensory modalities, such as touch and audition, and for motor outputs. Topographica is designed to simulate any of these cortical or subcortical areas.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Retinotopic and orientation map in V1. Given a particular fixation point (marked with a red + symbol above), the visual field seen by an animal can be divided into a regular grid, with each square representing a 1° × 1° area of visual space. In cortical area V1 of mammals, neurons are arranged into a retinotopic map, with nearby neurons responding to nearby areas of the retina. As an example, the image on the right shows the retinotopic map on the surface of V1 of a tree shrew for an 8° × 7° area of visual space (adapted from Bosking et al., 2002 with permission; scale bar is 1 mm). A stimulus presented in a particular location in visual space (such as the thick black bar shown) evokes a response centered around the corresponding grid square in V1 (6°, 2°). Which specific neurons respond within that general area, however, depends on the orientation of the stimulus. The V1 map is color coded with the preferred orientation of neurons in each location; e.g. the black bar shown at left will primarily activate neurons colored in purple in the corresponding V1 grid squares. Similar maps could be plotted for this same area showing preference for other visual features, such as motion direction, spatial frequency, color, disparity, and eye preference (depending on species). Other cortical areas are arranged into topographic maps for other sensory modalities, such as touch and audition, and for motor outputs. Topographica is designed to simulate any of these cortical or subcortical areas.
Mentions: In mammals, much of the cortical surface (and many subcortical structures) can be partitioned into topographic maps (Kaas, 1997; Van Essen et al., 2001). These maps contain systematic two-dimensional representations of features relevant to sensory and motor processing, such as retinal position, sound frequency, line orientation, and motion direction (Blasdel, 1992; Merzenich et al., 1975; Ohki et al., 2005; Weliky et al., 1996; Xu et al., 2007). Figure 1 shows an example retinotopic and orientation map from the primary visual cortex (V1). Understanding the development and function of topographic maps is crucial for understanding brain function, and will require integrating large-scale experimental imaging results with single-unit studies of the individual neurons and their connections that make up these maps. In principle, computational modeling can help make these links explicit, in order to explain how topographic maps can emerge from the behavior of single neurons.

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.