author_facet Butler, Thomas Charles
Benayoun, Marc
Wallace, Edward
van Drongelen, Wim
Goldenfeld, Nigel
Cowan, Jack
Butler, Thomas Charles
Benayoun, Marc
Wallace, Edward
van Drongelen, Wim
Goldenfeld, Nigel
Cowan, Jack
author Butler, Thomas Charles
Benayoun, Marc
Wallace, Edward
van Drongelen, Wim
Goldenfeld, Nigel
Cowan, Jack
spellingShingle Butler, Thomas Charles
Benayoun, Marc
Wallace, Edward
van Drongelen, Wim
Goldenfeld, Nigel
Cowan, Jack
Proceedings of the National Academy of Sciences
Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
Multidisciplinary
author_sort butler, thomas charles
spelling Butler, Thomas Charles Benayoun, Marc Wallace, Edward van Drongelen, Wim Goldenfeld, Nigel Cowan, Jack 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.1118672109 <jats:p>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.</jats:p> Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations Proceedings of the National Academy of Sciences
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title Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_unstemmed Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_full Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_fullStr Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_full_unstemmed Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_short Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_sort evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
topic Multidisciplinary
url http://dx.doi.org/10.1073/pnas.1118672109
publishDate 2012
physical 606-609
description <jats:p>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.</jats:p>
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author Butler, Thomas Charles, Benayoun, Marc, Wallace, Edward, van Drongelen, Wim, Goldenfeld, Nigel, Cowan, Jack
author_facet Butler, Thomas Charles, Benayoun, Marc, Wallace, Edward, van Drongelen, Wim, Goldenfeld, Nigel, Cowan, Jack, Butler, Thomas Charles, Benayoun, Marc, Wallace, Edward, van Drongelen, Wim, Goldenfeld, Nigel, Cowan, Jack
author_sort butler, thomas charles
container_issue 2
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container_title Proceedings of the National Academy of Sciences
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description <jats:p>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.</jats:p>
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spelling Butler, Thomas Charles Benayoun, Marc Wallace, Edward van Drongelen, Wim Goldenfeld, Nigel Cowan, Jack 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.1118672109 <jats:p>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.</jats:p> Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations Proceedings of the National Academy of Sciences
spellingShingle Butler, Thomas Charles, Benayoun, Marc, Wallace, Edward, van Drongelen, Wim, Goldenfeld, Nigel, Cowan, Jack, Proceedings of the National Academy of Sciences, Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations, Multidisciplinary
title Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_full Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_fullStr Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_full_unstemmed Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_short Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_sort evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
title_unstemmed Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations
topic Multidisciplinary
url http://dx.doi.org/10.1073/pnas.1118672109