Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference
Synaptic plasticity is often explored as a form of unsupervised adaptationin cortical microcircuits to learn the structure of complex sensoryinputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-08-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00103/full |
| _version_ | 1811284037266833408 |
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| author | Joseph eChrol-Cannon Yaochu eJin |
| author_facet | Joseph eChrol-Cannon Yaochu eJin |
| author_sort | Joseph eChrol-Cannon |
| collection | DOAJ |
| description | Synaptic plasticity is often explored as a form of unsupervised adaptationin cortical microcircuits to learn the structure of complex sensoryinputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data.In this work, input-specific structural changes are analyzed forthree empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks.It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by thepresentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network.To solve the problem of interference, we suggest that models of plasticitybe extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the casein experimental neuroscience. |
| first_indexed | 2024-04-13T02:22:36Z |
| format | Article |
| id | doaj.art-03b65a77fd614d1fba5a63d810d0789d |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-13T02:22:36Z |
| publishDate | 2015-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-03b65a77fd614d1fba5a63d810d0789d2022-12-22T03:06:54ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882015-08-01910.3389/fncom.2015.00103135761Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to InterferenceJoseph eChrol-Cannon0Yaochu eJin1University of SurreyUniversity of SurreySynaptic plasticity is often explored as a form of unsupervised adaptationin cortical microcircuits to learn the structure of complex sensoryinputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data.In this work, input-specific structural changes are analyzed forthree empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks.It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by thepresentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network.To solve the problem of interference, we suggest that models of plasticitybe extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the casein experimental neuroscience.http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00103/fullinterferencerecurrent neural networkssynaptic plastictySpatio-temporal patternsspiking neural networks |
| spellingShingle | Joseph eChrol-Cannon Yaochu eJin Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference Frontiers in Computational Neuroscience interference recurrent neural networks synaptic plasticty Spatio-temporal patterns spiking neural networks |
| title | Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference |
| title_full | Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference |
| title_fullStr | Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference |
| title_full_unstemmed | Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference |
| title_short | Learning Structure of Sensory Inputs with Synaptic Plasticity Leads to Interference |
| title_sort | learning structure of sensory inputs with synaptic plasticity leads to interference |
| topic | interference recurrent neural networks synaptic plasticty Spatio-temporal patterns spiking neural networks |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00103/full |
| work_keys_str_mv | AT josephechrolcannon learningstructureofsensoryinputswithsynapticplasticityleadstointerference AT yaochuejin learningstructureofsensoryinputswithsynapticplasticityleadstointerference |