dorsal/arxiv
View SchemaSingle neuron computation: from dynamical system to feature detector
| Authors | Sungho Hong, Blaise Aguera y Arcas, Adrienne L. Fairhall |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0612025 |
| URL | https://arxiv.org/abs/q-bio/0612025 |
Abstract
White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input, and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input/output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.
{
"annotation_id": "28a3e54d-ec2a-48c2-a7e8-27dad614f858",
"date_created": "2026-03-02T18:01:35.038000Z",
"date_modified": "2026-03-02T18:01:35.038000Z",
"file_hash": "d75e0f81845ebf8cc79be3b960ae40d577c1f63ad5895dcca1db941f41a1b855",
"private": false,
"record": {
"abstract": "White noise methods are a powerful tool for characterizing the computation\nperformed by neural systems. These methods allow one to identify the feature or\nfeatures that a neural system extracts from a complex input, and to determine\nhow these features are combined to drive the system\u0027s spiking response. These\nmethods have also been applied to characterize the input/output relations of\nsingle neurons driven by synaptic inputs, simulated by direct current\ninjection. To interpret the results of white noise analysis of single neurons,\nwe would like to understand how the obtained feature space of a single neuron\nmaps onto the biophysical properties of the membrane, in particular the\ndynamics of ion channels. Here, through analysis of a simple dynamical model\nneuron, we draw explicit connections between the output of a white noise\nanalysis and the underlying dynamical system. We find that under certain\nassumptions, the form of the relevant features is well defined by the\nparameters of the dynamical system. Further, we show that under some\nconditions, the feature space is spanned by the spike-triggered average and its\nsuccessive order time derivatives.",
"arxiv_id": "q-bio/0612025",
"authors": [
"Sungho Hong",
"Blaise Aguera y Arcas",
"Adrienne L. Fairhall"
],
"categories": [
"q-bio.NC",
"physics.bio-ph",
"physics.data-an"
],
"title": "Single neuron computation: from dynamical system to feature detector",
"url": "https://arxiv.org/abs/q-bio/0612025"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "f0ea4e64-2ca3-4f68-a3b0-fafe0215938b",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}