Universality in the Evolution of Orientation Columns in the Visual Cortex

Kaschube, Matthias; Schnabel, M.; Löwel, Siegrid; Coppola, David M.; White, Leonard; Wolf, Fred

doi:10.1126/science.1194869

Cited by 190 publications

(435 citation statements)

References 30 publications

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“…The model presented here is a special case of that introduced to analyze the formation of geometric visual hallucinations (6). As we note later, it is also closely related to a model of the cortex introduced to study the development of stable orientation preference maps (9,21). These assumptions yield variants of the Wilson-Cowan equations (22) for local density of neural excitation of excitatory (φ) and inhibitory (ψ) neurons (see SI Text).…”

Section: Modelmentioning

confidence: 99%

Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

Butler

Benayoun

Wallace

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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In the cat or primate primary visual cortex (V1), normal vision corresponds to a state where neural excitation patterns are driven by external visual stimuli. A spectacular failure mode of V1 occurs when such patterns are overwhelmed by spontaneously generated spatially self-organized patterns of neural excitation. These are experienced as geometric visual hallucinations. The problem of identifying the mechanisms by which V1 avoids this failure is made acute by recent advances in the statistical mechanics of pattern formation, which suggest that the hallucinatory state should be very robust. Here, we report how incorporating physiologically realistic long-range connections between inhibitory neurons changes the behavior of a model of V1. We find that the sparsity of long-range inhibition in V1 plays a previously unrecognized but key functional role in preserving the normal vision state. Surprisingly, it also contributes to the observed regularity of geometric visual hallucinations. Our results provide an explanation for the observed sparsity of long-range inhibition in V1-this generic architectural feature is an evolutionary adaptation that tunes V1 to the normal vision state. In addition, it has been shown that exactly the same long-range connections play a key role in the development of orientation preference maps. Thus V1's most striking long-range features-patchy excitatory connections and sparse inhibitory connections-are strongly constrained by two requirements: the need for the visual state to be robust and the developmental requirements of the orientational preference map.evolution | fluctuations T he primary visual cortex, V1, represents external stimuli as patterns of neural excitation. In the normal state, patterns of excitation on V1 are driven by sensory stimuli generated in the retina mapped to V1 from the visual field by the retinocortical map (1, 2). Patterns seen as visual hallucinations arise in exceptional circumstances when external stimuli are overwhelmed by internally generated spontaneous patterns of neural excitation. This situation occurs when the circuit parameters governing the dynamics of V1 are changed, for example, through the influence of psychotropic drugs that may act in part through effectively weakening cortical inhibition (3). The mechanism governing spontaneous pattern formation has been shown in previous studies to be closely related to that of diffusion driven pattern formation in chemical and other biological systems, known as the Turing mechanism (4-6). Compelling evidence for this mechanism has been provided by previous studies that have shown that precisely the four basic classes of geometric visual hallucinations (or form constants) and no others, commonly reported by subjects (7), follow directly from the Turing mechanism and spontaneous symmetry breaking of the basic approximate symmetries of V1 associated with translation and orientation preference (6). Here we show that V1 is specially configured to avoid the formation of visual hallucinations and remain stable und...

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Section: Modelmentioning

confidence: 99%

Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

Butler

Benayoun

Wallace

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…The hypothesis that mean pinwheel density in cat (galago) is more than 50% (20%) larger than in ferrets can be rejected with virtual certainty [P < 0.0001(0.0001), bootstrap test]. Meng et al suggest greater variation in pinwheel density across species; however, those data were derived from studies with small sample sizes using first-generation optical imaging methods that are subject to various systematic errors described in detail in [SOM of (1), figures S2 and S3 and pp. [3][4][5][6][7][8][9][10][11][12].…”

mentioning

confidence: 99%

“…W e presented a comparative study of visual cortical orientation columns and pinwheels in three mammalian species whose evolutionary paths separated more than 65 million years ago (1). For this purpose, we introduced methods to measure pinwheel density objectively-i.e., insensitive of image data preprocessing [(1) and supporting online material (SOM) of (1), pp.…”

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confidence: 99%

Response to Comment on “Universality in the Evolution of Orientation Columns in the Visual Cortex“

Keil

Kaschube

Schnabel

et al. 2012

Science

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Meng et al. conjecture that pinwheel density scales with body and brain size. Our data, spanning a 40-fold range of body sizes in Laurasiatheria and Euarchonta, do not support this conclusion. The noncolumnar layout in Glires also appears size-insensitive. Thus, body and brain size may be understood as a constraint on the evolution of visual cortical circuitry, but not as a determining factor. W e presented a comparative study of visual cortical orientation columns and pinwheels in three mammalian species whose evolutionary paths separated more than 65 million years ago (1). For this purpose, we introduced methods to measure pinwheel density objectively-i.e., insensitive of image data preprocessing [(1) and supporting online material (SOM) of (1), pp. 3-12 and 29-41]. We found that statistical measures characterizing the spatial layout of pinwheels from the scale of individual hypercolumns to the entire primary visual cortex (V1) were virtually identical, agreeing with an accuracy of a few percent. To understand how distinct evolutionary lineages can independently evolve this common design, we examined a broad set of mathematical models for the developmental self-organization of orientation columns [(1) and SOM of (1), pp. 13-62]. We found that models from a symmetry-defined class, exhibiting a universal (i.e., model-independent) solution set, robustly predict every aspect of the common design when suppressive long-range interactions are dominant [(1) and SOM of (1), pp. 13-41)]. This suggests that developmental network self-organization has canalized the evolution of neuronal circuitry underlying orientation maps in these species into the common design. A predicted signature of this mechanism is a pinwheel density close to the mathematical constant p. Confirming this prediction, we found that mean pinwheel density was indeed statistically indistinguishable from p (T2%).Meng et al. claim that pinwheel density is not an invariant but a function of V1 size (2). They conjecture a pinwheel density scaling law that can be approximated by , and 450 mm 2 V1 size, respectively (3, 4), pinwheel density in cats (galagos) should be 53% (28%) larger than in ferrets/tupaias. Our methods to accurately estimate pinwheel density are well suited to test this prediction. Figure 1A presents measured pinwheel densities for the four species graphed against body weight. Pinwheel densities appear invariant with respect to body weight. The hypothesis that mean pinwheel density in cat (galago) is more than 50% (20%) larger than in ferrets can be rejected with virtual certainty [P < 0.0001(0.0001), bootstrap test]. Meng et al. suggest greater variation in pinwheel density across species; however, those data were derived from studies with small sample sizes using first-generation optical imaging methods that are subject to various systematic errors described in detail in [SOM of (1),. Our analysis unambiguously indicates that pinwheel density is a genuine invariant feature of orientation column layout over a wide range of V1 sizes and th...

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“…These early sensory networks often comprise large numbers of cells and organize information in a map-like fashion, where spatial proximity of neurons reflects similarity in the selectivity for fundamental stimulus features (5,6). These maps tend to have a complete representation of sensory space and enable subsequent processing steps to select relevant features based on attention or associative learning.…”

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confidence: 99%

Efficient transformation of an auditory population code in a small sensory system

Clemens

Kutzki

Ronacher

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system-the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feedforward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.metric | invertebrates | information theory T o increase their fitness, most animals strive to evaluate sensory signals that reveal the quality of a potential mate. What if an animal has only a few dozen neurons to preprocess this extremely important information? Optimal coding theory suggests that the creation of a sparse, decorrelated representation would be a wise investment of scarce neuronal resources (1).That is indeed what has been found in many sensory modalities and species under natural conditions (2-4).These early sensory networks often comprise large numbers of cells and organize information in a map-like fashion, where spatial proximity of neurons reflects similarity in the selectivity for fundamental stimulus features (5, 6). These maps tend to have a complete representation of sensory space and enable subsequent processing steps to select relevant features based on attention or associative learning. Reading out such a representation by "blind" summation of responses across different neurons would be highly inefficient to recover information, because stimulus features are not only encoded by neuronal activity per se but also by neuronal identity. This type of population code is referred to as labeled-line code (7,8). Accordingly, higher-order sensory areas need to take into account which neurons are active when producing more specific representations of behaviorally relevant stimulus aspects (9). Do the...

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Universality in the Evolution of Orientation Columns in the Visual Cortex

Cited by 190 publications

References 30 publications

Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

Response to Comment on “Universality in the Evolution of Orientation Columns in the Visual Cortex“

Efficient transformation of an auditory population code in a small sensory system

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