arXiv (All)
open access
AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
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 chan
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Autism genetic tuning#97943
- NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images#298206
- Exploring the Relationship Between Social Expertise and Ability to Process Different Types of Abstract Concepts and the Neural Resources Allocated to Their Processing#893193
- The Core Emotion Framework: A Resolving Synthesis for Affective Science, Quantum Cognition, and Somatic Safety Engineering#690322
- The Architecture of Affective Agility: A Structural-Constructivist Analysis of Operator Functionalism, Systemic Strengthening, and Global Integration in the Core Emotion Framework#204157
Record · ID 1064273
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive