Robust quantification of orientation selectivity and direction selectivity

Mazurek, Mark E.; Kager, Marisa; Hooser, Stephen D. Van

doi:10.3389/fncir.2014.00092

Cited by 177 publications

(199 citation statements)

References 27 publications

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“…The fit parameters were evaluated with an error matrix (Hessian matrix). As described previously (Mazurek et al, 2014), the error is not trustworthy when the fit parameter is at the manually set boundaries; however, even if some parameters are at the boundaries, the errors of the other parameters are still valid. We used the error values only when the fit parameters were not at their boundaries.…”

Section: Methodsmentioning

confidence: 97%

“…As previously reported, the Gaussian fit does not always converge if the parameters are unbounded (Mazurek et al, 2014). We introduced fit parameter boundaries that are similar to Mazurek et al (2014) as follows:…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the resulting parameters (orientation/direction selectivity index; OSI/DSI) were not checked for significance. Mazurek et al (2014) introduced a method of significance tests, but the relation with the spontaneous firing rate was not discussed in their study. Our model-based method provides a proper treatment of both the spontaneous firing rate and the significance tests.…”

Section: Methodsmentioning

confidence: 99%

“…OS/DS responses have been traditionally characterized by calculating preestablished indices such as the OSI or the DSI (Gegenfurtner et al, 1996;Ringach et al, 2002;Niell and Stryker, 2008), but these have been criticized for the variations of their definitions and the lack of methods for significance tests (Mazurek et al, 2014). To overcome these deficiencies, we used a model-based parameter estimation to characterize the response properties of the SC cells.…”

Section: Characterization Of Orientation and Direction Selective Cellmentioning

confidence: 99%

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Segregation of Visual Response Properties in the Mouse Superior Colliculus and Their Modulation during Locomotion

2017

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The superior colliculus (SC) receives direct input from the retina and integrates it with information about sound, touch, and state of the animal that is relayed from other parts of the brain to initiate specific behavioral outcomes. The superficial SC layers (sSC) contain cells that respond to visual stimuli, whereas the deep SC layers (dSC) contain cells that also respond to auditory and somatosensory stimuli. Here, we used a large-scale silicon probe recording system to examine the visual response properties of SC cells of head-fixed and alert male mice. We found cells with diverse response properties including: (1)

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Characterization Of Orientation and Direction Selective Cellmentioning

confidence: 99%

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Segregation of Visual Response Properties in the Mouse Superior Colliculus and Their Modulation during Locomotion

2017

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“…Finally, in chapter 3, new methods and tools for data acquisition and analysis are introduced and discussed. Mazurek et al (2014) describe new analytical tools to extract quantitative data from orientation and direction selectivity studies in the visual cortex. Hayworth et al (2014) introduce a novel approach for mapping and imaging large libraries of ultrathin sections.…”

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confidence: 99%

Neural Circuits Revealed

2015

Frontiers Research Topics

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The appropriate function of the nervous system relies on precise patterns of connectivity among hundreds to billions of neurons across different biological systems. Evolutionarily conserved patterns of neural circuit organization and connectivity between morphologically and functionally diverse sets of neurons emerge from a remarkably robust set of genetic blueprints, uniquely defining circuits responsible for planning and execution of behavioral repertoires (Arenkiel et al., 2004;Dasen, 2009;Pecho-Vrieseling et al., 2009;Sürmeli et al., 2011;White and Sillitoe, 2013;Inamata and Shirasaki, 2014). Although it is well-established that individual neurons represent the elemental building blocks of the brain, understanding the architecture of neural circuits and how neurons functionally "wire up" through synapses, remains one of biology's major challenges. Our current understanding of how interconnected neuronal populations produce perception, memory, and behavior remains nascent. To unravel the details of complex nervous system function, we must consider not only the morphological and physiological properties of individual neurons, but also the structure and function of connections formed between different cell types. In the last decade much effort has been focused on trying to fully characterize "the brain connectome" and to understand how patterns of synaptic connectivity between neurons might help to better inform the underlying defects associated with neurological and psychiatric disorders (Sporns et al., 2005;Lichtman and Sanes, 2008). More recently there is a growing interest in mapping, and eventually classifying, all synapses in the brain to construct a complete "synaptome" (DeFelipe, 2010;O'Rourke et al., 2012). The very nature of these studies, which rely on multidisciplinary research efforts, have thus catalyzed the development of new research tools and technologies. For instance, advances in molecular genetics, viral engineering, and imaging technologies now allow precise labeling, manipulation, and mapping of complex neural circuits, together revealing previously unattainable details about the cellular morphologies and subcellular structures that are unique to the different types of neurons that make up the brain. Such technological advances could not be possible without successful co-evolution of novel computational tools and analytical methods that allow acquisition, management, and interpretation of gigantic and complex datasets. In fact, this last point perhaps represents the main challenge for the future of "connectomics" .This Research Topic comprises a wide variety of articles contributing to current views and understanding of different neural circuits, how they are organized in neural networks, and what are the functional outputs of this organization. Additionally, pioneering researchers in the field review novel high-throughput tools and analytical approaches, further describing how these methods have evolved to better explore neural circuits at different levels, covering a wide spectrum of ana...

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Neuronal response properties across cytochrome oxidase stripes in primate V2

Peres

Soares

Lima

et al. 2018

J of Comparative Neurology

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Cytochrome oxidase histochemistry reveals large-scale cortical modules in area V2 of primates known as thick, thin, and interstripes. Anatomical, electrophysiological, and tracing studies suggest that V2 cytochrome oxidase stripes participate in functionally distinct streams of visual information processing. However, there is controversy whether the different V2 compartments indeed correlate with specialized neuronal response properties. We used multiple-electrode arrays (16 × 2, 8 × 4 and 4 × 4 matrices) to simultaneously record the spiking activity (N = 190 single units) across distinct V2 stripes in anesthetized and paralyzed capuchin monkeys (N = 3 animals, 6 hemispheres). Visual stimulation consisted of moving bars and full-field gratings with different contrasts, orientations, directions of motion, spatial frequencies, velocities, and color contrasts. Interstripe neurons exhibited the strongest orientation and direction selectivities compared to the thick and thin stripes, with relatively stronger coding for orientation. Additionally, they responded best to higher spatial frequencies and to lower stimulus velocities. Thin stripes showed the highest proportion (80%) of neurons selective to color contrast (compared to 47% and 21% for thick and interstripes, respectively). The great majority of the color selective cells (86%) were also orientation selective. Additionally, thin stripe neurons continued to increase their firing rate for stimulus contrasts above 50%, while thick and interstripe neurons already exhibited some degree of response saturation at this point. Thick stripes best coded for lower spatial frequencies and higher stimulus velocities. In conclusion, V2 CytOx stripes exhibit a mixed degree of segregation and integration of information processing, shedding light into the early mechanisms of vision.

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Robust quantification of orientation selectivity and direction selectivity

Cited by 177 publications

References 27 publications

Segregation of Visual Response Properties in the Mouse Superior Colliculus and Their Modulation during Locomotion

Segregation of Visual Response Properties in the Mouse Superior Colliculus and Their Modulation during Locomotion

Neural Circuits Revealed

Neuronal response properties across cytochrome oxidase stripes in primate V2

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