Feedforward inhibition and synaptic scaling--two sides of the same coin?
Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions rema...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3310709?pdf=render |
| _version_ | 1811268562742935552 |
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| author | Christian Keck Cristina Savin Jörg Lücke |
| author_facet | Christian Keck Cristina Savin Jörg Lücke |
| author_sort | Christian Keck |
| collection | DOAJ |
| description | Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. |
| first_indexed | 2024-04-12T21:24:28Z |
| format | Article |
| id | doaj.art-2a4145b213c6444abf480bda53a99d8c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T21:24:28Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-2a4145b213c6444abf480bda53a99d8c2022-12-22T03:16:12ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-0183e100243210.1371/journal.pcbi.1002432Feedforward inhibition and synaptic scaling--two sides of the same coin?Christian KeckCristina SavinJörg LückeFeedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.http://europepmc.org/articles/PMC3310709?pdf=render |
| spellingShingle | Christian Keck Cristina Savin Jörg Lücke Feedforward inhibition and synaptic scaling--two sides of the same coin? PLoS Computational Biology |
| title | Feedforward inhibition and synaptic scaling--two sides of the same coin? |
| title_full | Feedforward inhibition and synaptic scaling--two sides of the same coin? |
| title_fullStr | Feedforward inhibition and synaptic scaling--two sides of the same coin? |
| title_full_unstemmed | Feedforward inhibition and synaptic scaling--two sides of the same coin? |
| title_short | Feedforward inhibition and synaptic scaling--two sides of the same coin? |
| title_sort | feedforward inhibition and synaptic scaling two sides of the same coin |
| url | http://europepmc.org/articles/PMC3310709?pdf=render |
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