{
  "id": 14238532,
  "title": "Designing optimal perturbation inputs for system identification in neuroscience",
  "url": "https://urgent.news/2026/10/13/designing-optimal-perturbation-inputs-for-system-identification-in",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-13T00:00:00.000Z",
  "source": {
    "name": "eLife",
    "slug": "elife",
    "url": "https://elifesciences.org/articles/110030"
  },
  "original_language": "en",
  "account": "Understanding neural networks and their connectivity between neurons is a core challenge in the field of neuroscience. Although passive activity from neurons provides limited data for estimating these connections, scientists often use perturbation-based methods to elicit hidden dynamics. However, the design of these perturbations has typically relied on empirical observations or biological intuition. To enhance connectivity estimation accuracy, researchers have developed a data-driven and theoretically grounded approach. This method treats the neural model as a control system, revealing that passive neural signals do not contain enough hidden information and thus impede system identification. By introducing perturbations, these hidden dynamics are exposed, resulting in improved accuracy.\n\nThe study provides a theoretical foundation for designing perturbation inputs to precisely estimate neural dynamics. Through simulations based on experimental paradigms, including neural state classification and optimal control of neural states, the researchers demonstrated that perturbations designed according to their framework achieve substantially more accurate system identification compared to conventional, intuition-based inputs. This breakthrough enables reliable discrimination of neural states, such as different levels of consciousness and pathological conditions, and facilitates precise control of their transitions toward recovery from abnormal states.",
  "summary": "Investigating the dynamics of neural networks, which are governed by connectivity between neurons, is a fundamental challenge in neuroscience. Because passive (spontaneous) activity provides only limited information for estimating connectivity, perturbation-based approaches are widely applied in neuroscience as they can evoke underlying hidden dynamics. However, the characteristics of such…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}