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AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets — arXiv:2609.27729

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27729
License (as archived): CC0-1.0
Archived: 2026-10-08T09:12:56.091Z
SHA-256 of the archived text: 6ff1a485c25e813886cb8c61e5d9c5a1ec343e9a9c970e0a361b63bb67b24865
Stable URL of this page: https://metraipolis.com/documento/dc_1fbdbfbb74ca

How to cite

AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets — arXiv:2609.27729 — arXiv (en), https://arxiv.org/abs/2609.27729 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1fbdbfbb74ca (Metraipolis archive dc_1fbdbfbb74ca, sha256 6ff1a485c25e813886cb8c61e5d9c5a1ec343e9a9c970e0a361b63bb67b24865)

BibTeX

@misc{dc1fbdbfbb74ca,
  title = {AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets — arXiv:2609.27729},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27729},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1fbdbfbb74ca; sha256 6ff1a485c25e813886cb8c61e5d9c5a1ec343e9a9c970e0a361b63bb67b24865},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27729. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or an overly salient memory. Motivated by control theory, we develop an AI-driven neural-surrogate framework that proposes candidate representational changes and tests their predicted perceptual effects from snapshots of stimulus-evoked fMRI activity, without physical stimulation. The framework combines fMRI decoding, deep generative modeling, and constrained latent-space steering. Valence and memorability are used only as worked examples. Using more than 36,000 image-fMRI observations from four deeply sampled Natural Scenes Dataset participants, subject-specific models recovered coarse generative structure from visually responsive cortex (two-way identification, 0.79-0.88; chance, 0.5). Graded perturbations were reconstructed as images and evaluated with automated scorers and human ratings from 7,200 trials by 18 participants. In the primary VDVAE model, valence shifted from -0.61 to +1.03 SD and memorability from -1.34 to +1.45 SD; a later Versatile Diffusion refinement reduced or altered these effects. Across five perturbation levels, human valence ratings moved in the predicted direction under the linear time-correction model (mean slope, 0.038 SD per unit of alpha; 95 percent CI, 0.003-0.074; positive in 16 of 18 participants). Perceived memorability did not change reliably. Baseline agreement with the automated assessor was suggestive for valence (r = 0.30) and weak for memorability (r = 0.10). Extreme perturbations drifted from the original stimulus, so intended change must be weighed against loss of fidelity. These findings provide a falsifiable upstream method for designing and behaviorally testing candidate representational targets for future neuromodulation in psychiatry, while marking the limits of the present static approximation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-24 in cs.AI).
Categorie arXiv: Neurons and Cognition (q-bio.NC) ; Artificial Intelligence (cs.AI); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.27729 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T08:13:26.706Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).