Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons
Network activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structur...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-10-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00134/full |
| _version_ | 1811327176908210176 |
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| author | Christoph Kolodziejski Christoph Kolodziejski Christoph Kolodziejski Christian Tetzlaff Christian Tetzlaff Christian Tetzlaff Florentin Wörgötter Florentin Wörgötter |
| author_facet | Christoph Kolodziejski Christoph Kolodziejski Christoph Kolodziejski Christian Tetzlaff Christian Tetzlaff Christian Tetzlaff Florentin Wörgötter Florentin Wörgötter |
| author_sort | Christoph Kolodziejski |
| collection | DOAJ |
| description | Network activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structure of the network. To address this question a time-continuous treatment of plasticity is required. However, this is - even in simple recurrent network structures - currently not possible. Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. We use noisy periodic external input signals, which through the recurrent connections lead to complex actual ongoing inputs and observe that large stable ranges emerge in these networks without boundaries or weight-normalization. Somewhat counter-intuitively, we find that about 40% of these cases are obtained with an LTP-dominated STDP-curve. Noise can reduce stability in some cases, but generally this does not occur. Instead stable domains are often enlarged. This study is a first step towards a better understanding of the ongoing interactions between activity and plasticity in recurrent networks using STDP. The results suggests that stability of (sub-)networks should generically be present also in larger structures. |
| first_indexed | 2024-04-13T15:02:16Z |
| format | Article |
| id | doaj.art-444d66b4f3bb4d40adece42eec8c8fbb |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-13T15:02:16Z |
| publishDate | 2010-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-444d66b4f3bb4d40adece42eec8c8fbb2022-12-22T02:42:15ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882010-10-01410.3389/fncom.2010.001341505Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neuronsChristoph Kolodziejski0Christoph Kolodziejski1Christoph Kolodziejski2Christian Tetzlaff3Christian Tetzlaff4Christian Tetzlaff5Florentin Wörgötter6Florentin Wörgötter7Bernstein Center for Computational NeuroscienceMax Planck Institute for Dynamics and Self-OrganizationUniversity of GöttingenBernstein Center for Computational NeuroscienceMax Planck Institute for Dynamics and Self-OrganizationUniversity of GöttingenBernstein Center for Computational NeuroscienceUniversity of GöttingenNetwork activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structure of the network. To address this question a time-continuous treatment of plasticity is required. However, this is - even in simple recurrent network structures - currently not possible. Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. We use noisy periodic external input signals, which through the recurrent connections lead to complex actual ongoing inputs and observe that large stable ranges emerge in these networks without boundaries or weight-normalization. Somewhat counter-intuitively, we find that about 40% of these cases are obtained with an LTP-dominated STDP-curve. Noise can reduce stability in some cases, but generally this does not occur. Instead stable domains are often enlarged. This study is a first step towards a better understanding of the ongoing interactions between activity and plasticity in recurrent networks using STDP. The results suggests that stability of (sub-)networks should generically be present also in larger structures.http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00134/fullLong-Term PotentiationLong-term depressionRecurrent networksasymmetric STDPdifferential Hebbian plasticitySpike-timing-dependent plasticity |
| spellingShingle | Christoph Kolodziejski Christoph Kolodziejski Christoph Kolodziejski Christian Tetzlaff Christian Tetzlaff Christian Tetzlaff Florentin Wörgötter Florentin Wörgötter Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons Frontiers in Computational Neuroscience Long-Term Potentiation Long-term depression Recurrent networks asymmetric STDP differential Hebbian plasticity Spike-timing-dependent plasticity |
| title | Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons |
| title_full | Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons |
| title_fullStr | Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons |
| title_full_unstemmed | Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons |
| title_short | Closed-form treatment of the interactions between neuronal activity and timing-dependent plasticity in networks of linear neurons |
| title_sort | closed form treatment of the interactions between neuronal activity and timing dependent plasticity in networks of linear neurons |
| topic | Long-Term Potentiation Long-term depression Recurrent networks asymmetric STDP differential Hebbian plasticity Spike-timing-dependent plasticity |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00134/full |
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