Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission
Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/10/7/577 |
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| author | Daniel Miner Florentin Wörgötter Christian Tetzlaff Michael Fauth |
| author_facet | Daniel Miner Florentin Wörgötter Christian Tetzlaff Michael Fauth |
| author_sort | Daniel Miner |
| collection | DOAJ |
| description | Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity. |
| first_indexed | 2024-03-10T10:06:56Z |
| format | Article |
| id | doaj.art-ce700f32b06748a69489b55099ccd7f2 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-10T10:06:56Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-ce700f32b06748a69489b55099ccd7f22023-11-22T01:30:03ZengMDPI AGBiology2079-77372021-06-0110757710.3390/biology10070577Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information TransmissionDaniel Miner0Florentin Wörgötter1Christian Tetzlaff2Michael Fauth3Bernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August University, Friedrich Hund Platz 1, 37077 Göttingen, GermanyBernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August University, Friedrich Hund Platz 1, 37077 Göttingen, GermanyBernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August University, Friedrich Hund Platz 1, 37077 Göttingen, GermanyBernstein Center for Computational Neuroscience, Third Institute of Physics, Georg-August University, Friedrich Hund Platz 1, 37077 Göttingen, GermanyOur brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.https://www.mdpi.com/2079-7737/10/7/577self-organizationsynaptic plasticityinformation transfer |
| spellingShingle | Daniel Miner Florentin Wörgötter Christian Tetzlaff Michael Fauth Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission Biology self-organization synaptic plasticity information transfer |
| title | Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission |
| title_full | Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission |
| title_fullStr | Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission |
| title_full_unstemmed | Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission |
| title_short | Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission |
| title_sort | self organized structuring of recurrent neuronal networks for reliable information transmission |
| topic | self-organization synaptic plasticity information transfer |
| url | https://www.mdpi.com/2079-7737/10/7/577 |
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