Comparison of visual quantities in untrained neural networks
Summary: The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuni...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Cell Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124723009117 |
| _version_ | 1797731084479234048 |
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| author | Hyeonsu Lee Woochul Choi Dongil Lee Se-Bum Paik |
| author_facet | Hyeonsu Lee Woochul Choi Dongil Lee Se-Bum Paik |
| author_sort | Hyeonsu Lee |
| collection | DOAJ |
| description | Summary: The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains. |
| first_indexed | 2024-03-12T11:53:39Z |
| format | Article |
| id | doaj.art-59b2134810ab46b2b5dfd70dec265fe8 |
| institution | Directory Open Access Journal |
| issn | 2211-1247 |
| language | English |
| last_indexed | 2024-03-12T11:53:39Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports |
| spelling | doaj.art-59b2134810ab46b2b5dfd70dec265fe82023-08-31T05:02:07ZengElsevierCell Reports2211-12472023-08-01428112900Comparison of visual quantities in untrained neural networksHyeonsu Lee0Woochul Choi1Dongil Lee2Se-Bum Paik3Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of KoreaDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of KoreaDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of KoreaDepartment of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; Corresponding authorSummary: The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains.http://www.sciencedirect.com/science/article/pii/S2211124723009117CP: Neuroscience |
| spellingShingle | Hyeonsu Lee Woochul Choi Dongil Lee Se-Bum Paik Comparison of visual quantities in untrained neural networks Cell Reports CP: Neuroscience |
| title | Comparison of visual quantities in untrained neural networks |
| title_full | Comparison of visual quantities in untrained neural networks |
| title_fullStr | Comparison of visual quantities in untrained neural networks |
| title_full_unstemmed | Comparison of visual quantities in untrained neural networks |
| title_short | Comparison of visual quantities in untrained neural networks |
| title_sort | comparison of visual quantities in untrained neural networks |
| topic | CP: Neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2211124723009117 |
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