Automatic discovery of cell types and microcircuitry from neural connectomics
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2015-04-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/04250 |
| _version_ | 1811235840393740288 |
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| author | Eric Jonas Konrad Kording |
| author_facet | Eric Jonas Konrad Kording |
| author_sort | Eric Jonas |
| collection | DOAJ |
| description | Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets. |
| first_indexed | 2024-04-12T11:58:54Z |
| format | Article |
| id | doaj.art-598e810028e540ada9575451fe8d3516 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T11:58:54Z |
| publishDate | 2015-04-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-598e810028e540ada9575451fe8d35162022-12-22T03:33:54ZengeLife Sciences Publications LtdeLife2050-084X2015-04-01410.7554/eLife.04250Automatic discovery of cell types and microcircuitry from neural connectomicsEric Jonas0Konrad Kording1Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, United StatesDepartment of Physical Medicine and Rehabilitation, Northwestern University, Chicago, United States; Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Chicago, United States; Department of Physiology, Northwestern University, Chicago, United StatesNeural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms. It also can reveal interesting structure in the nervous system of Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.https://elifesciences.org/articles/04250connectomicscomputationmicrocircuitry |
| spellingShingle | Eric Jonas Konrad Kording Automatic discovery of cell types and microcircuitry from neural connectomics eLife connectomics computation microcircuitry |
| title | Automatic discovery of cell types and microcircuitry from neural connectomics |
| title_full | Automatic discovery of cell types and microcircuitry from neural connectomics |
| title_fullStr | Automatic discovery of cell types and microcircuitry from neural connectomics |
| title_full_unstemmed | Automatic discovery of cell types and microcircuitry from neural connectomics |
| title_short | Automatic discovery of cell types and microcircuitry from neural connectomics |
| title_sort | automatic discovery of cell types and microcircuitry from neural connectomics |
| topic | connectomics computation microcircuitry |
| url | https://elifesciences.org/articles/04250 |
| work_keys_str_mv | AT ericjonas automaticdiscoveryofcelltypesandmicrocircuitryfromneuralconnectomics AT konradkording automaticdiscoveryofcelltypesandmicrocircuitryfromneuralconnectomics |