Training dynamically balanced excitatory-inhibitory networks.
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We sh...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0220547 |
| _version_ | 1819111777656373248 |
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| author | Alessandro Ingrosso L F Abbott |
| author_facet | Alessandro Ingrosso L F Abbott |
| author_sort | Alessandro Ingrosso |
| collection | DOAJ |
| description | The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability. |
| first_indexed | 2024-12-22T04:03:01Z |
| format | Article |
| id | doaj.art-1040ac807d464ccc8789b94a6e1e4f31 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T04:03:01Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1040ac807d464ccc8789b94a6e1e4f312022-12-21T18:39:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01148e022054710.1371/journal.pone.0220547Training dynamically balanced excitatory-inhibitory networks.Alessandro IngrossoL F AbbottThe construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability.https://doi.org/10.1371/journal.pone.0220547 |
| spellingShingle | Alessandro Ingrosso L F Abbott Training dynamically balanced excitatory-inhibitory networks. PLoS ONE |
| title | Training dynamically balanced excitatory-inhibitory networks. |
| title_full | Training dynamically balanced excitatory-inhibitory networks. |
| title_fullStr | Training dynamically balanced excitatory-inhibitory networks. |
| title_full_unstemmed | Training dynamically balanced excitatory-inhibitory networks. |
| title_short | Training dynamically balanced excitatory-inhibitory networks. |
| title_sort | training dynamically balanced excitatory inhibitory networks |
| url | https://doi.org/10.1371/journal.pone.0220547 |
| work_keys_str_mv | AT alessandroingrosso trainingdynamicallybalancedexcitatoryinhibitorynetworks AT lfabbott trainingdynamicallybalancedexcitatoryinhibitorynetworks |