dorsal/arxiv
View SchemaLearning intrinsic excitability in medium spiny neurons
| Authors | Gabriele Scheler |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0502023 |
| URL | https://arxiv.org/abs/q-bio/0502023 |
| DOI | 10.12688/f1000research.2-88.v2 |
| Journal | F1000Research 2014, 2:88 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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 parametrization of individual ion channels on the neuronal 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 variability on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how variability 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 variability determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.
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"abstract": "We present an unsupervised, local activation-dependent learning rule for\nintrinsic plasticity (IP) which affects the composition of ion channel\nconductances for single neurons in a use-dependent way. We use a\nsingle-compartment conductance-based model for medium spiny striatal neurons in\norder to show the effects of parametrization of individual ion channels on the\nneuronal activation function. We show that parameter changes within the\nphysiological ranges are sufficient to create an ensemble of neurons with\nsignificantly different activation functions. We emphasize that the effects of\nintrinsic neuronal variability on spiking behavior require a distributed mode\nof synaptic input and can be eliminated by strongly correlated input. We show\nhow variability and adaptivity in ion channel conductances can be utilized to\nstore patterns without an additional contribution by synaptic plasticity (SP).\nThe adaptation of the spike response may result in either \"positive\" or\n\"negative\" pattern learning. However, read-out of stored information depends on\na distributed pattern of synaptic activity to let intrinsic variability\ndetermine spike response. We briefly discuss the implications of this\nconditional memory on learning and addiction.",
"arxiv_id": "q-bio/0502023",
"authors": [
"Gabriele Scheler"
],
"categories": [
"q-bio.NC",
"cs.NE"
],
"doi": "10.12688/f1000research.2-88.v2",
"journal_ref": "F1000Research 2014, 2:88",
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Learning intrinsic excitability in medium spiny neurons",
"url": "https://arxiv.org/abs/q-bio/0502023"
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