Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b]
We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to sh...
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2014-02-01
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| Series: | F1000Research |
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| Online Access: | http://f1000research.com/articles/2-88/v2 |
| _version_ | 1818137528244371456 |
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| author | Gabriele Scheler |
| author_facet | Gabriele Scheler |
| author_sort | Gabriele Scheler |
| collection | DOAJ |
| description | We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction. |
| first_indexed | 2024-12-11T09:57:44Z |
| format | Article |
| id | doaj.art-0a301f9205344fb38a9a2244039071b5 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-11T09:57:44Z |
| publishDate | 2014-02-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-0a301f9205344fb38a9a2244039071b52022-12-22T01:12:13ZengF1000 Research LtdF1000Research2046-14022014-02-01210.12688/f1000research.2-88.v23899Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b]Gabriele Scheler0Carl Correns Foundation for Mathematical Biology, Mountain View, CA, 94040, USAWe present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.http://f1000research.com/articles/2-88/v2NeurodevelopmentNeuronal & Glial Cell BiologyNeuronal Signaling MechanismsTheoretical & Computational Neuroscience |
| spellingShingle | Gabriele Scheler Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] F1000Research Neurodevelopment Neuronal & Glial Cell Biology Neuronal Signaling Mechanisms Theoretical & Computational Neuroscience |
| title | Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] |
| title_full | Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] |
| title_fullStr | Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] |
| title_full_unstemmed | Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] |
| title_short | Learning intrinsic excitability in medium spiny neurons [v2; ref status: indexed, http://f1000r.es/30b] |
| title_sort | learning intrinsic excitability in medium spiny neurons v2 ref status indexed http f1000r es 30b |
| topic | Neurodevelopment Neuronal & Glial Cell Biology Neuronal Signaling Mechanisms Theoretical & Computational Neuroscience |
| url | http://f1000research.com/articles/2-88/v2 |
| work_keys_str_mv | AT gabrielescheler learningintrinsicexcitabilityinmediumspinyneuronsv2refstatusindexedhttpf1000res30b |