Learning structure of sensory inputs with synaptic plasticity leads to interference
Chrol-Cannon J, Jin Y (2015)
Frontiers in Computational Neuroscience 9.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Chrol-Cannon, Joseph;
Jin, YaochuUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
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Abstract / Bemerkung
Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs 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 for three 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 the presentation 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 plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.
Erscheinungsjahr
2015
Zeitschriftentitel
Frontiers in Computational Neuroscience
Band
9
eISSN
1662-5188
Page URI
https://pub.uni-bielefeld.de/record/2978536
Zitieren
Chrol-Cannon J, Jin Y. Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience. 2015;9.
Chrol-Cannon, J., & Jin, Y. (2015). Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience, 9. https://doi.org/10.3389/fncom.2015.00103
Chrol-Cannon, Joseph, and Jin, Yaochu. 2015. “Learning structure of sensory inputs with synaptic plasticity leads to interference”. Frontiers in Computational Neuroscience 9.
Chrol-Cannon, J., and Jin, Y. (2015). Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience 9.
Chrol-Cannon, J., & Jin, Y., 2015. Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience, 9.
J. Chrol-Cannon and Y. Jin, “Learning structure of sensory inputs with synaptic plasticity leads to interference”, Frontiers in Computational Neuroscience, vol. 9, 2015.
Chrol-Cannon, J., Jin, Y.: Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience. 9, (2015).
Chrol-Cannon, Joseph, and Jin, Yaochu. “Learning structure of sensory inputs with synaptic plasticity leads to interference”. Frontiers in Computational Neuroscience 9 (2015).
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