dorsal/arxiv
View SchemaReading Neural Encodings using Phase Space Methods
| Authors | Henry D. I. Abarbanel, Evren C. Tumer |
|---|---|
| Categories | |
| ArXiv ID | physics/0304003 |
| URL | https://arxiv.org/abs/physics/0304003 |
| Journal | "Perspectives and Problems in Nonlinear Science", eds. E. Kaplan, J Marsden and K Sreenivasan, Springer, 2003 |
Abstract
Environmental signals sensed by nervous systems are often represented in spike trains carried from sensory neurons to higher neural functions where decisions and functional actions occur. Information about the environmental stimulus is contained (encoded) in the train of spikes. We show how to "read" the encoding using state space methods of nonlinear dynamics. We create a mapping from spike signals which are output from the neural processing system back to an estimate of the analog input signal. This mapping is realized locally in a reconstructed state space embodying both the dynamics of the source of the sensory signal and the dynamics of the neural circuit doing the processing. We explore this idea using a Hodgkin-Huxley conductance based neuron model and input from a low dimensional dynamical system, the Lorenz system. We show that one may accurately learn the dynamical input/output connection and estimate with high precision the details of the input signals from spike timing output alone. This form of "reading the neural code" has a focus on the neural circuitry as a dynamical system and emphasizes how one interprets the dynamical degrees of freedom in the neural circuit as they transform analog environmental information into spike trains.
{
"annotation_id": "7d97db15-55c2-4443-8b7f-0fb6d7278ac8",
"date_created": "2026-03-02T18:00:43.434000Z",
"date_modified": "2026-03-02T18:00:43.434000Z",
"file_hash": "3c7054764e4c77dfbe6bfd1cd17cafc18c370944104127ba8e327b01dca6c84c",
"private": false,
"record": {
"abstract": "Environmental signals sensed by nervous systems are often represented in\nspike trains carried from sensory neurons to higher neural functions where\ndecisions and functional actions occur. Information about the environmental\nstimulus is contained (encoded) in the train of spikes. We show how to \"read\"\nthe encoding using state space methods of nonlinear dynamics. We create a\nmapping from spike signals which are output from the neural processing system\nback to an estimate of the analog input signal. This mapping is realized\nlocally in a reconstructed state space embodying both the dynamics of the\nsource of the sensory signal and the dynamics of the neural circuit doing the\nprocessing. We explore this idea using a Hodgkin-Huxley conductance based\nneuron model and input from a low dimensional dynamical system, the Lorenz\nsystem. We show that one may accurately learn the dynamical input/output\nconnection and estimate with high precision the details of the input signals\nfrom spike timing output alone. This form of \"reading the neural code\" has a\nfocus on the neural circuitry as a dynamical system and emphasizes how one\ninterprets the dynamical degrees of freedom in the neural circuit as they\ntransform analog environmental information into spike trains.",
"arxiv_id": "physics/0304003",
"authors": [
"Henry D. I. Abarbanel",
"Evren C. Tumer"
],
"categories": [
"physics.bio-ph",
"q-bio.NC",
"q-bio.QM"
],
"journal_ref": "\"Perspectives and Problems in Nonlinear Science\", eds. E. Kaplan,\n J Marsden and K Sreenivasan, Springer, 2003",
"title": "Reading Neural Encodings using Phase Space Methods",
"url": "https://arxiv.org/abs/physics/0304003"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "e8120efd-c9ae-4880-91f5-e40eb17b0106",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}