Learning as filtering: Implications for spike-based plasticity.
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-02-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009721&type=printable |
| _version_ | 1826552597679439872 |
|---|---|
| author | Jannes Jegminat Simone Carlo Surace Jean-Pascal Pfister |
| author_facet | Jannes Jegminat Simone Carlo Surace Jean-Pascal Pfister |
| author_sort | Jannes Jegminat |
| collection | DOAJ |
| description | Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network-the Synaptic Filter-and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity. |
| first_indexed | 2024-12-10T13:54:02Z |
| format | Article |
| id | doaj.art-4df6365f41a24338a52ebfe8acc8eefc |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2025-03-14T07:11:39Z |
| publishDate | 2022-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-4df6365f41a24338a52ebfe8acc8eefc2025-03-04T05:30:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-02-01182e100972110.1371/journal.pcbi.1009721Learning as filtering: Implications for spike-based plasticity.Jannes JegminatSimone Carlo SuraceJean-Pascal PfisterMost normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network-the Synaptic Filter-and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009721&type=printable |
| spellingShingle | Jannes Jegminat Simone Carlo Surace Jean-Pascal Pfister Learning as filtering: Implications for spike-based plasticity. PLoS Computational Biology |
| title | Learning as filtering: Implications for spike-based plasticity. |
| title_full | Learning as filtering: Implications for spike-based plasticity. |
| title_fullStr | Learning as filtering: Implications for spike-based plasticity. |
| title_full_unstemmed | Learning as filtering: Implications for spike-based plasticity. |
| title_short | Learning as filtering: Implications for spike-based plasticity. |
| title_sort | learning as filtering implications for spike based plasticity |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009721&type=printable |
| work_keys_str_mv | AT jannesjegminat learningasfilteringimplicationsforspikebasedplasticity AT simonecarlosurace learningasfilteringimplicationsforspikebasedplasticity AT jeanpascalpfister learningasfilteringimplicationsforspikebasedplasticity |