5 Kasım 2024 Veri seti Açık Erişim

Karagoz, Osman Kaan; Kilic, Aysegul; Aydin, Emin Yusuf; Ankarali, Mustafa Mert; Uyanik, Ismail

JSON

{
  "conceptdoi": "10.48623/aperta.274061", 
  "conceptrecid": "274061", 
  "created": "2024-11-05T23:34:23.917223+00:00", 
  "doi": "10.48623/aperta.274062", 
  "files": [
    {
      "bucket": "20dde27f-2447-42c4-af51-2abcd46fa943", 
      "checksum": "md5:4d45d26034b3f37c45601766ffdb5787", 
      "key": "PredictiveUncertainty-ActiveSensing.zip", 
      "links": {
        "self": "https://aperta.ulakbim.gov.tr/api/files/20dde27f-2447-42c4-af51-2abcd46fa943/PredictiveUncertainty-ActiveSensing.zip"
      }, 
      "size": 821237, 
      "type": "zip"
    }
  ], 
  "id": 274062, 
  "links": {
    "badge": "https://aperta.ulakbim.gov.tr/badge/doi/10.48623/aperta.274062.svg", 
    "bucket": "https://aperta.ulakbim.gov.tr/api/files/20dde27f-2447-42c4-af51-2abcd46fa943", 
    "conceptbadge": "https://aperta.ulakbim.gov.tr/badge/doi/10.48623/aperta.274061.svg", 
    "conceptdoi": "https://doi.org/10.48623/aperta.274061", 
    "doi": "https://doi.org/10.48623/aperta.274062", 
    "html": "https://aperta.ulakbim.gov.tr/record/274062", 
    "latest": "https://aperta.ulakbim.gov.tr/api/records/274062", 
    "latest_html": "https://aperta.ulakbim.gov.tr/record/274062"
  }, 
  "metadata": {
    "access_right": "open", 
    "access_right_category": "success", 
    "creators": [
      {
        "affiliation": "Middle East Technical University", 
        "name": "Karagoz, Osman Kaan"
      }, 
      {
        "affiliation": "Middle East Technical University", 
        "name": "Kilic, Aysegul"
      }, 
      {
        "affiliation": "Hacettepe University", 
        "name": "Aydin, Emin Yusuf"
      }, 
      {
        "affiliation": "Middle East Technical University", 
        "name": "Ankarali, Mustafa Mert"
      }, 
      {
        "affiliation": "Hacettepe University", 
        "name": "Uyanik, Ismail"
      }
    ], 
    "description": "<p>Animals use active sensing movements to shape the spatiotemporal characteristics of sensory signals to better perceive their environment under varying conditions. However, the underlying mechanisms governing the generation of active sensing movements are not known. To address this, we investigated the role of active sensing movements in the refuge tracking behavior of <em>Eigenmannia virescens</em>, a species of weakly electric fish. These fish track the longitudinal movements of a refuge in which they hide by swimming back and forth in a single linear dimension. During refuge tracking, <em>Eigenmannia</em> exhibits stereotyped whole-body oscillations when the quality of the sensory signals degrades. We developed a closed-loop feedback control model to examine the role of these ancillary movements on the task performance. Our modeling suggests that fish may use active sensing to minimize predictive uncertainty in state estimation during refuge tracking. The proposed model generates simulated fish trajectories that are statistically indistinguishable from that of the actual fish, unlike the open-loop noise generator and stochastic resonance generator models in the literature. These findings reveal the significance of closed-loop control in active sensing behavior, offering new insights into the underlying mechanisms of dynamic sensory modulation.</p>", 
    "doi": "10.48623/aperta.274062", 
    "has_grant": true, 
    "keywords": [
      "active sensing", 
      "weakly electric fish", 
      "sensorimotor control", 
      "state estimation"
    ], 
    "language": "eng", 
    "license": {
      "id": "cc-by-sa"
    }, 
    "notes": "This repository contains the MATLAB code and dataset used in the study:\nKaragoz, O. K., Kilic, A., Aydin, E. Y., Ankarali, M. M., & Uyanik, I. \"Predictive Uncertainty in State-Estimation Drives Active Sensing Behavior.\"", 
    "publication_date": "2024-11-05", 
    "related_identifiers": [
      {
        "identifier": "10.48623/aperta.274061", 
        "relation": "isVersionOf", 
        "scheme": "doi"
      }
    ], 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "274062"
          }, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "274061"
          }
        }
      ]
    }, 
    "resource_type": {
      "title": "Veri seti", 
      "type": "dataset"
    }, 
    "science_branches": [
      "Temel Bilimler > Ya\u015fam Bilimleri > N\u00f6robiyoloji > N\u00f6rosistemler"
    ], 
    "title": "Dataset and Code for the Article: Predictive Uncertainty in State-Estimation Drives Active Sensing", 
    "tubitak_grants": [
      {
        "program": "1001", 
        "project_number": "120E198", 
        "workgroup": "EEEAG"
      }
    ]
  }, 
  "owners": [
    2482
  ], 
  "revision": 1, 
  "stats": {
    "downloads": 1.0, 
    "unique_downloads": 1.0, 
    "unique_views": 19.0, 
    "version_downloads": 1.0, 
    "version_unique_downloads": 1.0, 
    "version_unique_views": 19.0, 
    "version_views": 27.0, 
    "version_volume": 821237.0, 
    "views": 27.0, 
    "volume": 821237.0
  }, 
  "updated": "2024-11-05T23:34:23.967221+00:00"
}