Minds, machines, and moral boundaries: Ethical challenges in computational neuroscience and neurotechnology

Rapid advances in computational neuroscience and neurotechnology are fundamentally reshaping our ability to decode, model, and modify the human brain. Brain-computer interfaces, neural decoding algorithms, large-scale neural simulations, and AI-driven psychiatric tools now operate at a level of sophistication that raises profound ethical, legal, and societal questions. Neural data represent among the most intimate forms of personal information, capturing cognitive states, emotions, intentions, and identity in ways that conventional biomedical data cannot. Simultaneously, the proliferation of consumer neurotechnologies, AI-assisted diagnostics, and cognitive enhancement tools has outpaced regulatory and ethical frameworks designed to govern their use. As these technologies move from laboratory to clinic and everyday life, establishing robust ethical principles grounded in empirical neuroscientific understanding has become both urgent and necessary.This Research Topic addresses a growing disconnect between the pace of technological development in computational neuroscience and neurotechnology and the maturity of ethical frameworks guiding their responsible deployment. Critical gaps persist around mental privacy, informed consent for neural data collection, algorithmic bias in neural decoding, equitable access to neurotechnology, and the moral status of increasingly sophisticated brain-like computational systems.We aim to catalyse productive dialogue between neuroscientists, ethicists, legal scholars, clinicians, and policymakers to develop actionable and evidence-based ethical guidelines.We welcome all article types addressing ethical dimensions of computational neuroscience and neurotechnology. Specific themes of interest include:- Neural data ethics: Privacy, ownership, consent, and commercialisation of brain-derived data- Cognitive liberty and mental privacy: Right to mental self-determination; risks of involuntary neural decoding- Algorithmic bias: Fairness, transparency, and equity in AI-driven neural diagnostics and predictive models- Large language models and neuroscience applications: Ethical implications of deploying LLMs in mental health, psychiatric diagnosis, and cognitive assessment, including concerns around transparency, hallucination risks, therapeutic boundaries, and the potential erosion of human clinical judgement in neuroscientific and clinical practice- Brain-computer interfaces: Ethical deployment in clinical and consumer contexts; identity and agency implications- Cognitive enhancement: Ethical boundaries of neurotechnology-driven performance enhancement- Moral status and AI: Ethical considerations surrounding brain organoids, neural simulations, and neuromorphic systems- Regulatory and governance frameworks: Legal approaches to neurorights, data protection, and technology oversight- Equity and access: Socioeconomic and global disparities in neurotechnology availability- Research ethics: Informed consent, participant vulnerability, and dual-use concerns in neuroscience research Topic editors: Mahmoud Elbattah, Mohammad Alauthman Source: https://www.frontiersin.org/research-topics/82473/minds-machines-and-moral-boundaries-ethical-challenges-in-computational-neuroscience-and-neurotechnology