Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks
Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses towards stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-06-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00036/full |
| _version_ | 1819240785384570880 |
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| author | Christian eTetzlaff Christian eTetzlaff Christoph eKolodziejski Marc eTimme Florentin eWörgötter |
| author_facet | Christian eTetzlaff Christian eTetzlaff Christoph eKolodziejski Marc eTimme Florentin eWörgötter |
| author_sort | Christian eTetzlaff |
| collection | DOAJ |
| description | Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses towards stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties. |
| first_indexed | 2024-12-23T14:13:32Z |
| format | Article |
| id | doaj.art-f333a6d54ec041bbbd6ba4ad1c284b2e |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-23T14:13:32Z |
| publishDate | 2012-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-f333a6d54ec041bbbd6ba4ad1c284b2e2022-12-21T17:43:59ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882012-06-01610.3389/fncom.2012.0003621404Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple NetworksChristian eTetzlaff0Christian eTetzlaff1Christoph eKolodziejski2Marc eTimme3Florentin eWörgötter4Georg-August-Universität GöttingenMax Planck Institute for Dynamics and Self-OrganizationMax Planck Institute for Dynamics and Self-OrganizationMax Planck Institute for Dynamics and Self-OrganizationGeorg-August-Universität GöttingenConventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses towards stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties.http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00036/fullHomeostasisNeural Networkplasticitysynaptic scalingsynapseSignal Propagation |
| spellingShingle | Christian eTetzlaff Christian eTetzlaff Christoph eKolodziejski Marc eTimme Florentin eWörgötter Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks Frontiers in Computational Neuroscience Homeostasis Neural Network plasticity synaptic scaling synapse Signal Propagation |
| title | Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks |
| title_full | Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks |
| title_fullStr | Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks |
| title_full_unstemmed | Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks |
| title_short | Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks |
| title_sort | analysis of synaptic scaling in combination with hebbian plasticity in several simple networks |
| topic | Homeostasis Neural Network plasticity synaptic scaling synapse Signal Propagation |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00036/full |
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