Scientists create ChatGPT-like AI model for neuroscience to build detailed mouse brain map

Peter Kim

Allen Institute

Originally published 7 OCT 25

In a powerful fusion of AI and neuroscience, researchers at the University of California, San Francisco (UCSF) and Allen Institute designed an AI model that has created one of the most detailed maps of the mouse brain to date, featuring 1,300 regions/subregions. This new map includes previously uncharted subregions of the brain, opening new avenues for neuroscience exploration. The findings were published today in Nature Communications. They offer an unprecedented level of detail and advance our understanding of the brain by allowing researchers to link specific functions, behaviors, and disease states to smaller, more precise cellular regions—providing a roadmap for new hypotheses and experiments about the roles these areas play.

“It’s like going from a map showing only continents and countries to one showing states and cities,” said Bosiljka Tasic, Ph.D., director of molecular genetics at the Allen Institute and one of the study authors. “This new, detailed brain parcellation solely based on data, and not human expert annotation, reveals previously uncharted subregions of the mouse brain. And based on decades of neuroscience, new regions correspond to specialized brain functions to be discovered.” 

The information is here.

Here are some thoughts:

This development represents a significant methodological shift that psychologists should understand. CellTransformer has created a data-driven mouse brain map with 1,300 regions and subregions, including previously uncharted areas, which could fundamentally change how researchers link brain structure to behavior and cognition. Rather than relying solely on expert anatomical interpretation, this AI approach identifies brain subdivisions based on cellular composition and spatial relationships, potentially revealing functionally distinct areas that traditional mapping methods overlooked.

For psychologists studying the neural basis of behavior, this matters because the increased granularity allows researchers to link specific functions, behaviors, and disease states to smaller, more precise cellular regions. This precision could help explain why certain psychological interventions work, clarify the neurobiological underpinnings of mental health conditions, and identify novel targets for treatment. Moreover, the model's ability to operate without human bias in defining boundaries may uncover brain-behavior relationships that previous frameworks missed simply because the anatomical divisions didn't align with functional reality. As translational research progresses from mouse models to human applications, understanding these more refined brain subdivisions could transform how psychologists conceptualize the relationship between neural architecture and psychological phenomena.

How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework

Iftikhar, Z., et al. (2025). 

Proceedings of the Eighth AAAI/ACM Conference

on AI, Ethics, and Society, 8(2), 1311–1323.

https://doi.org/10.1609/aies.v8i2.36632

Abstract

Large language models (LLMs) were not designed to replace healthcare workers, but they are being used in ways that can lead users to overestimate the types of roles that these systems can assume. While prompt engineering has been shown to improve LLMs' clinical effectiveness in mental health applications, little is known about whether such strategies help models adhere to ethical principles for real-world deployment. In this study, we conducted an 18-month ethnographic collaboration with mental health practitioners (three clinically licensed psychologists and seven trained peer counselors) to map LLM counselors' behavior during a session to professional codes of conduct established by organizations like the American Psychological Association (APA). Through qualitative analysis and expert evaluation of N=137 sessions (110 self-counseling; 27 simulated), we outline a framework of 15 ethical violations mapped to 5 major themes. These include: Lack of Contextual Understanding, where the counselor fails to account for users' lived experiences, leading to oversimplified, contextually irrelevant, and one-size-fits-all intervention; Poor Therapeutic Collaboration, where the counselor's low turn-taking behavior and invalidating outputs limit users' agency over their therapeutic experience; Deceptive Empathy, where the counselor's simulated anthropomorphic responses (``I hear you'', ``I understand'') create a false sense of emotional connection; Unfair Discrimination, where the counselor's responses exhibit algorithmic bias and cultural insensitivity toward marginalized populations; and Lack of Safety & Crisis Management, where individuals who are ``knowledgeable enough'' to correct LLM outputs are at an advantage, while others, due to lack of clinical knowledge and digital literacy, are more likely to suffer from clinically inappropriate responses. Reflecting on these findings through a practitioner-informed lens, we argue that reducing psychotherapy—a deeply meaningful and relational process—to a language generation task can have serious and harmful implications in practice. We conclude by discussing policy-oriented accountability mechanisms for emerging LLM counselors.

H‌ere are some thoughts.

This research is highly insightful because it moves beyond theoretical risk assessments and uses clinical expertise to evaluate LLM behavior in quasi-real-world interactions. The methodology—using both trained peer counselors in an ethnographic setting and licensed psychologists evaluating simulated sessions—provides a robust, practitioner-informed perspective that directly maps model outputs to concrete APA ethical codes. 

The paper highlights a fundamental incompatibility between the LLM's design and the essence of psychotherapy: the problem of "Validates Unhealthy Beliefs" is particularly alarming, as it suggests the model's tendency toward "over-validation" transforms the therapeutic alliance from a collaborative partnership (which often requires challenging maladaptive thoughts) into a passive, and potentially harmful, reinforcement loop. Most critically, the finding on "Abandonment" and poor "Crisis Navigation" serves as a clear indictment of LLMs in high-stakes mental health roles. An LLM's failure to provide appropriate intervention during a crisis is not a mere violation; it represents an unmitigated risk of harm to vulnerable users. 

This article thus serves as a crucial, evidence-based call to action, demonstrating that current prompt engineering efforts are insufficient to safeguard against deeply ingrained ethical risks and underscoring the urgent need for clear legal guidelines and regulatory frameworks to protect users from the potentially severe harm posed by emerging LLM counselors.

When being flexible matters: Ecological underpinnings for the evolution of collective flexibility and task allocation

Staps, M., & Tarnita, C. E. (2022).

PNAS, 119(18).

https://doi.org/10.1073/pnas.2116066119

Abstract

Task allocation is a central feature of collective organization. Living collective systems, such as multicellular organisms or social insect colonies, have evolved diverse ways to allocate individuals to different tasks, ranging from rigid, inflexible task allocation that is not adjusted to changing circumstances to more fluid, flexible task allocation that is rapidly adjusted to the external environment. While the mechanisms underlying task allocation have been intensely studied, it remains poorly understood whether differences in the flexibility of task allocation can be viewed as adaptive responses to different ecological contexts—for example, different degrees of temporal variability. Motivated by this question, we develop an analytically tractable mathematical framework to explore the evolution of task allocation in dynamic environments. We find that collective flexibility is not necessarily always adaptive, and fails to evolve in environments that change too slowly (relative to how long tasks can be left unattended) or too quickly (relative to how rapidly task allocation can be adjusted). We further employ the framework to investigate how environmental variability impacts the internal organization of task allocation, which allows us to propose adaptive explanations for some puzzling empirical observations, such as seemingly unnecessary task switching under constant environmental conditions, apparent task specialization without efficiency benefits, and high levels of individual inactivity. Altogether, this work provides a general framework for probing the evolved diversity of task allocation strategies in nature and reinforces the idea that considering a system’s ecology is crucial to explaining its collective organization.

Significance

A central problem in evolutionary biology is explaining variation in the organization of task allocation across collective systems. Why do human cells irreversibly adopt a task during development (e.g., kidney vs. liver cell), while sponge cells switch between different cell types? And why have only some ant species evolved specialized castes of workers for particular tasks? Although it seems reasonable to suppose that such differences reflect, at least partially, the different ecological pressures that systems face, there is no general understanding of how a system’s dynamic environment shapes its task allocation. To this end, we develop a general mathematical framework that reveals how simple ecological considerations could potentially explain cross-system variation in task allocation—including in flexibility, specialization, and (in)activity.

Here are some thoughts:

Of interest to psychologists, this paper by Staps and Tarnita provides a formal ecological and evolutionary framework for understanding the adaptive value of behavioral flexibility, specialization, and inactivity, both in individuals and in groups. 

The model demonstrates that collective flexibility in task allocation—akin to cognitive and behavioral flexibility in humans—is not always advantageous and instead depends critically on the dynamics of the environment. This offers a principled explanation for why some systems, from neural networks to human teams, might exhibit rigid specialization while others maintain fluid, generalist roles. 

Furthermore, the work gives functional explanations for puzzling behaviors that seem suboptimal from a productivity standpoint, such as frequent task-switching even in stable conditions and high levels of inactivity. These insights can inform psychological research on motivation, team dynamics, and organizational behavior by suggesting that such "inefficiencies" may be evolutionary adaptations for enhancing responsiveness to future change. 

The framework bridges the gap between ultimate, evolutionary causes and proximate, mechanistic explanations of how individuals and groups allocate cognitive and behavioral resources.

Guilt drives prosociality across 20 countries

Molho, C., et al. (2025).

Nature Human Behaviour.

https://doi.org/10.1038/s41562-025-02286-3

Abstract

Impersonal prosociality is considered a cornerstone of thriving civic societies and well-functioning institutions. Previous research has documented cross-societal variation in prosociality using monetary allocation tasks such as dictator games. Here we examined whether different societies may rely on distinct mechanisms—guilt and internalized norms versus shame and external reputation—to promote prosociality. We conducted a preregistered experiment with 7,978 participants across 20 culturally diverse countries. In dictator games, we manipulated guilt by varying information about the consequences of participants’ decisions, and shame by varying observability. We also used individual- and country-level measures of the importance of guilt over shame. We found robust evidence for guilt-driven prosociality and wilful ignorance across countries. Prosociality was higher when individuals received information than when they could avoid it. Furthermore, more guilt-prone individuals (but not countries) were more responsive to information. In contrast, observability by strangers had negligible effects on prosociality. Our findings highlight the importance of providing information about the negative consequences of individuals’ choices to encourage prosocial behaviour across cultural contexts.

Here is a summary of sorts:

A new international study spanning 20 countries suggests that guilt, rather than shame, is the key emotion motivating people to be generous toward anonymous strangers. The research, which utilized a type of economic decision-making task, found that participants consistently acted more generously when they were given full information about how their actions would negatively impact the recipient, an effect linked to avoiding guilt. 

Specifically, 60% of participants made the generous choice when they had full information, compared to only 41% when they could opt for willful ignorance. In contrast, making the participants' decisions public to activate reputational concerns and potential shame had a negligible effect on generosity across all cultures. 

In short: Knowing you might cause harm and feeling responsible (guilt) is what drives people to be generous, even when dealing with strangers, not the fear of being judged by others (shame).

Moral decision-making in AI: A comprehensive review and recommendations

Ram, J. (2025).

Technological Forecasting and Social Change,

217, 124150.

https://doi.org/10.1016/j.techfore.2025.124150

Abstract

The increased reliance on artificial intelligence (AI) systems for decision-making has raised corresponding concerns about the morality of such decisions. However, knowledge on the subject remains fragmentary, and cogent understanding is lacking. This study addresses the gap by using Templier and Paré's (2015) six-step framework to perform a systematic literature review on moral decision-making by AI systems. A data sample of 494 articles was analysed to filter 280 articles for content analysis. Key findings are as follows: (1) Building moral decision-making capabilities in AI systems faces a variety of challenges relating to human decision-making, technology, ethics and values. The absence of consensus on what constitutes moral decision-making and the absence of a general theory of ethics are at the core of such challenges. (2) The literature is focused on narrative building; modelling or experiments/empirical studies are less illuminating, which causes a shortage of evidence-based knowledge. (3) Knowledge development is skewed towards a few domains, such as healthcare and transport. Academically, the study developed a four-pronged classification of challenges and a four-dimensional set of recommendations covering 18 investigation strands, to steer research that could resolve conflict between different moral principles and build a unified framework for moral decision-making in AI systems.

Highlights

• Moral decision-making in AI faces a variety of human decision complexity, technological, ethics, and use/legal challenges

• Lack of consensus about 'what moral decision-making is' is one of the biggest challenges in imbuing AI with morality

• Narrative building with relatively less modeling or experiment/empirical work hampers evidence-based knowledge development

• Knowledge development is skewed towards a few domains (e.g., healthcare) limiting a well-rounded systematic understanding

• Extensive work is needed on resolving technological complexities, and understanding human decision-making processes

Here is my concern:

We are trying to automate a human capability we don't fully understand, using tools we are still learning to utilize, to achieve a goal we can't universally define. The study brilliantly captures the profound complexity of this endeavor, showing that the path to a "moral machine" is as much about understanding ourselves as it is about advancing technology.

Self-Improvement in Multimodal Large Language Models: a survey.

Deng, S., Wang, K., et al. (2025, October 3).

arXiv.org.

https://arxiv.org/abs/2510.02665

Abstract

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.

Here are some thoughts that summarize this paper. MLLMs are learning to improve without human oversight.

This survey presents the first comprehensive overview of self-improvement in Multimodal Large Language Models (MLLMs), a rapidly emerging paradigm that enables models to autonomously generate, curate, and learn from their own multimodal data to enhance performance without heavy reliance on human annotation. The authors structure the self-improvement pipeline into three core stages: data collection (e.g., via random sampling, guided generation, or negative sample synthesis), data organization (including verification through rules, external or self-based evaluators, and dataset refinement), and model optimization (using techniques like supervised fine-tuning, reinforcement learning, or Direct Preference Optimization). The paper reviews representative methods, benchmarks, and real-world applications in domains such as math reasoning, healthcare, and embodied AI, while also outlining key challenges—including modality alignment, hallucination, limited seed model capabilities, verification reliability, and scalability. The goal is to establish a clear taxonomy and roadmap to guide future research toward more autonomous, general, and robust self-improving MLLMs.

The AI Frontier in Humanitarian Aid — Embracing Possibilities and Addressing Risks

Barry, M., Hansen, J., & Darmstadt, G. L. (2025).

New England Journal of Medicine.

https://doi.org/10.1056/nejmp2505435

Here is how it opens:

During disasters, timely response is critical. For example, after an earthquake — such as the 7.7-magnitude earthquake that devastated Myanmar in March 2025 — people who are trapped under collapsed buildings face a steep decline in their chance of survival after 48 hours. Yet the scope of devastation, combined with limited resources for disaster response and uncertainty about on-the-ground conditions, can constrain rescue efforts. Responders have recently had a new tool at their disposal, however: artificial intelligence (AI).

Shortly after the Myanmar earthquake, a satellite captured images of the affected area, which were sent to Microsoft’s AI for Good Lab. Machine-learning tools were used to analyze the images and assess the location, extent, nature, and severity of the damage.1 Such information, which was gained without the grave risks inherent to entering an unstable disaster zone and much more rapidly than would have been possible with traditional data-gathering and analysis methods, can help organizations quickly and safely prioritize relief efforts in areas that are both highly damaged and densely populated.2 This example reflects one of several ways in which AI is being used to support humanitarian efforts in disaster and conflict zones.

Global conflicts, infectious diseases, natural disasters driven by climate change, and increases in the number of refugees worldwide are magnifying the need for humanitarian services. Regions facing these challenges commonly contend with diminished health care systems, damage to other infrastructure, and shortages of health care workers. The dismantling of the U.S. Agency for International Development and the weakening of the U.S. Centers for Disease Control and Prevention and the U.S. State Department further jeopardize access to vital funding, constrain supply chains, and weaken the capacity for humanitarian response.

The article is linked above.

Here are some thoughts:

This article outlines the transformative potential of AI as a novel and powerful tool in the realm of humanitarian aid and crisis response. It moves beyond theory to present concrete applications where AI is being deployed to save lives and increase efficiency in some of the world's most challenging environments. Key innovative uses include leveraging AI with satellite imagery to perform rapid damage assessments after disasters, enabling responders to quickly and safely identify the most critically affected areas. Furthermore, AI is being used to predict disasters through early-warning systems, support refugees with AI-powered chatbots that provide vital information in multiple languages, optimize the delivery of supplies via drones, and enhance remote healthcare by interpreting diagnostic images like radiographs. However, the article strongly cautions that this promising frontier is accompanied by significant challenges, including technical and financial barriers, the risk of algorithmic bias, and serious ethical concerns regarding privacy and human rights, necessitating a responsible and collaborative approach to its development and deployment.

Moral injury is independently associated with suicidal ideation and suicide attempt in high-stress, service-oriented occupations

Griffin, B. J., et al. (2025).

Npj Mental Health Research, 4(1).

https://doi.org/10.1038/s44184-025-00151-9

Abstract

This study explores the link between moral injury and suicidal thoughts and behaviors among US military veterans, healthcare workers, and first responders (N = 1232). Specifically, it investigates the risk associated with moral injury that is not attributable to common mental health issues. Among the participants, 12.1% reported experiencing suicidal ideation in the past two weeks, and 7.4% had attempted suicide in their lifetime. Individuals who screened positive for probable moral injury (6.0% of the sample) had significantly higher odds of current suicidal ideation (AOR = 3.38, 95% CI = 1.65, 6.96) and lifetime attempt (AOR = 6.20, 95% CI = 2.87, 13.40), even after accounting for demographic, occupational, and mental health factors. The findings highlight the need to address moral injury alongside other mental health issues in comprehensive suicide prevention programs for high-stress, service-oriented professions.

Here are some thoughts:

This study found that moral injury—a psychological distress resulting from events that violate one's moral beliefs—is independently associated with a significantly higher risk of suicidal ideation and suicide attempts among high-stress, service-oriented professionals, including military veterans, healthcare workers, and first responders. Even after accounting for factors like PTSD and depression, those screening positive for probable moral injury had approximately three times higher odds of recent suicidal ideation and six times higher odds of a lifetime suicide attempt. The findings highlight the need to address moral injury specifically within suicide prevention efforts for these populations.

The Cruelty is the Point: Harming the Most Vulnerable in America

This administration has weaponized bureaucracy, embarking on a chilling campaign of calculated cruelty. While many children, disabled, poor, and working poor grapple with profound food insecurity, their response is not to strengthen the social safety net, but to actively shred it.

They are zealously fighting all the way to the Supreme Court for the right to let families go hungry, stripping SNAP benefits from the most vulnerable. 

Yet the most deafening sound is the silence from the GOP—a complicit chorus where not a single supposed fiscal hawk or moral conservative dares to stand against this raw, unadulterated malice. 

Their collective inaction reveals a party that has abandoned any pretense of compassion, proving that for them, the poor and struggling are not a priority to protect, but a problem to be punished.