dorsal/arxiv
View SchemaQuantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets
| Authors | Laxmidhar Behera, Indrani Kar, Avshalom Elitzur |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0407001 |
| URL | https://arxiv.org/abs/q-bio/0407001 |
| DOI | 10.1007/s10702-005-7125-6 |
Abstract
A theoretical quantum brain model is proposed using a nonlinear Schroedinger wave equation. The model proposes that there exists a quantum process that mediates the collective response of a neural lattice (classical brain). The model is used to explain eye movements when tracking moving targets. Using a Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical brain, a wave packet is triggered in the quantum brain. This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of 'jumps' and 'rests'. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.
{
"annotation_id": "883c1c7f-2047-4bf7-aedc-a16953ba11cf",
"date_created": "2026-03-02T18:01:31.482000Z",
"date_modified": "2026-03-02T18:01:31.482000Z",
"file_hash": "e5de594f5824f628e5f887171f22ad8d7682642d9c0dfefdbf7ce2981d8aefff",
"private": false,
"record": {
"abstract": "A theoretical quantum brain model is proposed using a nonlinear Schroedinger\nwave equation. The model proposes that there exists a quantum process that\nmediates the collective response of a neural lattice (classical brain). The\nmodel is used to explain eye movements when tracking moving targets. Using a\nRecurrent Quantum Neural Network(RQNN) while simulating the quantum brain\nmodel, two very interesting phenomena are observed. First, as eye sensor data\nis processed in a classical brain, a wave packet is triggered in the quantum\nbrain. This wave packet moves like a particle. Second, when the eye tracks a\nfixed target, this wave packet moves not in a continuous but rather in a\ndiscrete mode. This result reminds one of the saccadic movements of the eye\nconsisting of \u0027jumps\u0027 and \u0027rests\u0027. However, such a saccadic movement is\nintertwined with smooth pursuit movements when the eye has to track a dynamic\ntrajectory. In a sense, this is the first theoretical model explaining the\nexperimental observation reported concerning eye movements in a static scene\nsituation. The resulting prediction is found to be very precise and efficient\nin comparison to classical objective modeling schemes such as the Kalman\nfilter.",
"arxiv_id": "q-bio/0407001",
"authors": [
"Laxmidhar Behera",
"Indrani Kar",
"Avshalom Elitzur"
],
"categories": [
"q-bio.NC",
"q-bio.OT"
],
"doi": "10.1007/s10702-005-7125-6",
"title": "Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets",
"url": "https://arxiv.org/abs/q-bio/0407001"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "6007772d-0145-4cb6-ae2d-b9821cc16be4",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}