As the head of the Computational Neurobiology Laboratory at Salk, Sejnowski’s latest project is poised to delve deeply into the intricate workings of neural circuits involved in working memory. With the NIH award funding $3.5 million over the next five years, his team intends to revolutionize our understanding by leveraging advanced computational models to dissect the dynamical activity of neuronal networks. These models aim to uncover principles that govern how information is temporarily encoded and maintained in the brain, a cornerstone process critical for decision-making and reasoning.

Working memory, a fundamental cognitive system, enables the transient storage and manipulation of information necessary for complex tasks. Despite its significance, the precise neural mechanisms underlying its robustness and capacity limitations remain enigmatic. Sejnowski’s approach utilizes state-of-the-art techniques in computational neuroscience, combining biophysically realistic neural network models with machine learning algorithms to simulate and predict the behavior of large-scale brain circuits. Such technological sophistication promises to elucidate how synaptic plasticity and circuit dynamics coalesce to sustain memory traces in the prefrontal cortex and hippocampus.

This research direction extends beyond theoretical exploration by aiming to address practical concerns related to neuropsychiatric conditions. Memory impairments are pervasive across multiple disorders, including schizophrenia, traumatic brain injury (TBI), and post-traumatic stress disorder (PTSD). By decoding the neural code of working memory with unprecedented granularity, Sejnowski’s team aspires to identify biomarkers and potential therapeutic targets that could alleviate cognitive deficits associated with these afflictions. The translational impact of this project cannot be overstated as it offers hope for interventions that restore normal cognitive function.

Throughout his distinguished career, Sejnowski has consistently pioneered transformative technologies and ideas in neuroscience. His earlier contributions profoundly shaped the study of neuroeconomics, neuroanatomy, neurophysiology, cognitive psychology, and artificial intelligence. A landmark achievement was his 1985 invention of the Boltzmann machine alongside Geoffrey Hinton, PhD, a quantum leap for neural network learning algorithms. The Boltzmann machine represents the first algorithm capable of learning internal representations in multilayer networks, providing a biologically plausible framework that continues to influence the development of AI to this day.

Beyond algorithmic advances, Sejnowski developed NETtalk, an early neural network system that mimicked human speech synthesis by learning to convert written text into phonetic output. This pioneering work laid a critical foundation for the field of deep learning and current technologies like ChatGPT, which simulate language and cognition through complex neural architectures. His dual focus on biological inspiration and computational implementation epitomizes the fusion of neuroscience and artificial intelligence.

Sejnowski’s influence extends deeply into neuroimaging advancements and the exploration of behaviorally relevant neural systems. His recent innovation involves a novel method for precisely measuring synaptic strength and plasticity, vital parameters underpinning learning and memory formation. By quantifying how synapses encode information and undergo activity-dependent changes, this technique sheds light on the synaptic basis of cognitive decline observed in aging and neurodegenerative diseases. Such insights are critical for developing strategies to preserve cognitive function across the lifespan.

Recognition of Sejnowski’s scientific excellence is reflected in the numerous prestigious awards he has garnered over the years. Most notably, he was honored with the 2024 Brain Prize and the 2022 Gruber Prize in Neuroscience, reflecting his outstanding contributions to understanding brain function. These accolades underscore his role as a luminary whose work bridges multiple disciplines and drives progress in the neurosciences.

A testament to his standing in the scientific community, Sejnowski holds positions in several elite academies, including the United States National Academy of Sciences, National Academy of Medicine, National Academy of Engineering, and National Academy of Inventors. He is also a Fellow of the Royal Society in the United Kingdom and a member of the American Philosophical Society, highlighting his global influence as a thought leader and innovator.

The Salk Institute, where Sejnowski conducts his research, is renowned for its relentless pursuit of groundbreaking knowledge in biological sciences. Founded by Jonas Salk, the developer of the first effective polio vaccine, the institute embodies a culture of bold scientific exploration. Within its walls, scientists continually push the boundaries of understanding in neuroscience, cancer biology, aging, immunobiology, and computational biology, among other fields. Sejnowski’s work fits seamlessly into this mission, combining innovation with impact.

Sejnowski’s ongoing commitment to deciphering the biological basis of cognition, learning, and memory signifies a new chapter in brain science. As computational tools grow increasingly sophisticated, melding detailed biophysical models with AI-driven analytics, his laboratory stands at the forefront of unraveling the complexities of neural information processing. This work holds profound implications not only for fundamental neuroscience but also for the development of targeted treatments for cognitive impairments.

With the NIH Director’s Pioneer Award supporting this ambitious endeavor, Terrence Sejnowski’s visionary research will continue to illuminate the neural architecture of working memory. His innovative approach exemplifies the power of interdisciplinary science to tackle some of the most challenging questions in brain function and dysfunction. The coming years promise remarkable advances from his lab that could redefine therapeutic strategies for mental health disorders and enhance our comprehension of the human mind.

Subject of Research: Neural circuit dynamics of working memory and computational modeling of cognitive processes in health and disease.

Article Title: NIH Director’s Pioneer Award Enables Terrence Sejnowski’s Cutting-Edge Computational Exploration of Working Memory Circuitry

News Publication Date: October 13, 2025

Web References:
– https://www.salk.edu/scientist/terrence-sejnowski/
– https://commonfund.nih.gov/pioneer
– https://www.salk.edu/news-release/upgrading-brain-storage-quantifying-how-much-information-our-synapses-can-hold/
– https://www.salk.edu/news-release/salk-professor-terrence-sejnowski-wins-brain-prize/
– https://www.salk.edu/news-release/salk-institutes-terrence-sejnowski-awarded-gruber-prize/
– https://www.salk.edu/news-release/terrence-sejnowski-elected-to-the-royal-society-and-the-american-philosophical-society/

Image Credits: Salk Institute

Keywords: Life sciences, Neuroscience, Cognitive neuroscience, Computational neuroscience, Memory, Cognition, Cognitive psychology, Psychological science, Social sciences

Prof. Shagrir’s academic journey is distinguished by his acquisition of foundational degrees in rigorous scientific disciplines. He earned his bachelor’s degree in mathematics and computer science from the Hebrew University itself, grounding him in the fundamental concepts of computation and numerical analysis. This was complemented by a master’s degree in the history and philosophy of science, which provided a broad, reflective perspective on scientific methodologies and epistemologies. His academic rigor culminated in a Ph.D. from the University of California, San Diego, where he specialized in philosophy and cognitive science, focusing on the intricate relationships between human cognition, computation, and philosophical theory.

Throughout his career, Prof. Shagrir has held a series of influential administrative roles that underscore his suitability for the position of Rector. Notably, his tenure as Vice-President for International Affairs has been marked by strategic expansions and pivotal collaborations, which enhanced the Hebrew University’s global presence. Under his leadership, the International Office played a central role in navigating complex challenges such as the ongoing repercussions of the COVID-19 pandemic, the traumatic October 7 terror attack that shook the region, and the consequential Iron Swords War. These crises required agile management strategies to ensure continuity in international educational programs and partnerships, a challenge Prof. Shagrir met with resilience and forward-thinking solutions.

Significantly, during his vice presidency, the university witnessed an impressive expansion of international degree programs. By leveraging his interdisciplinary expertise and global network, Prof. Shagrir successfully forged exchange programs with over 100 partner institutions worldwide. This broad network has not only enriched the intellectual climate at the Hebrew University but has also diversified the student and faculty population, thereby fostering a richer, more inclusive academic milieu. These developments further solidify the university’s reputation as a hub of cutting-edge research and cross-cultural academic exchange.

Looking ahead, Prof. Shagrir is set to take office as Rector in October 2025, succeeding the current Rector, Prof. Tamir Sheafer, who has recently been appointed President of the university. This leadership reshuffle represents a strategic alignment aimed at consolidating the university’s strengths in both governance and academic excellence. The new Rector’s vision involves a commitment to upholding and enhancing the university’s standing as a national and international leader in research, education, and innovation.

Prof. Asher Cohen, the President of the Hebrew University, expressed his strong support for Prof. Shagrir’s appointment. He highlighted the central role Prof. Shagrir has played in strengthening the university’s international footprint. Prof. Cohen’s confidence is rooted in Prof. Shagrir’s proven track record of facilitating robust partnerships and navigating complex global challenges through innovative administrative practices. According to Cohen, these qualities will be instrumental in driving the university forward into an era defined by increased competitiveness, interdisciplinary collaboration, and research excellence.

Prof. Shagrir’s vision extends beyond mere administrative oversight; it encompasses a holistic approach focused on integrating cutting-edge technology within educational frameworks. He has expressed a keen interest in embedding innovative teaching and learning technologies into the university’s core operations. This forward-looking strategy aims to enhance pedagogical effectiveness, foster deeper student engagement, and prepare graduates for a rapidly evolving global knowledge economy that demands adaptability and technological fluency.

In his statements following the election, Prof. Shagrir articulated his commitment to diversity and inclusion as foundational pillars of the university’s future. Recognizing the complexity of modern academic environments, he emphasized fostering a heterogeneous community of students and faculty to stimulate intellectual vitality and social cohesion. This approach is expected to not only enhance the overall educational experience but also to drive novel research outcomes by bringing multiple perspectives and interdisciplinary methodologies to bear on pressing scientific and societal challenges.

Under Prof. Shagrir’s leadership, the international dimension of the Hebrew University is anticipated to deepen further. His track record indicates that he will prioritize the expansion of global partnerships that facilitate collaborative research and exchange. By integrating diverse academic traditions and research methodologies, these collaborations enrich the university’s intellectual ecosystem and contribute to the production of globally relevant knowledge in fields ranging from computational sciences and cognitive studies to social sciences and humanities.

The university community is also attentive to Prof. Shagrir’s responsiveness to external challenges and crises. His handling of emergency situations like the pandemic and regional security conflicts demonstrated an ability to implement effective contingency plans and maintain academic continuity. These experiences underscore his capacity for strategic resilience, a critical competence for leading a complex institution like the Hebrew University in an unpredictable geopolitical climate.

As Prof. Shagrir prepares to take the helm, he acknowledged the collaborative nature of university governance. He highlighted the importance of working in close conjunction with Prof. Tamir Sheafer, the incoming President, alongside faculty members and administrative staff, to ensure a unified and effective leadership. This cohesion is vital for advancing the university’s mission of fostering academic excellence, expanding research frontiers, and nurturing innovative educational models amidst the dynamic landscape of higher education.

In conclusion, Prof. Oron Shagrir’s election as Rector signifies not only a leadership change but a continuity of the Hebrew University’s commitment to global engagement, academic rigor, and innovation. His interdisciplinary background and extensive administrative experience uniquely position him to shepherd the university through its forthcoming challenges and opportunities. With a clear vision that integrates international collaboration, technological advancement, diversity, and crisis management, Prof. Shagrir is set to guide the Hebrew University into a new era of academic distinction and societal impact.

Subject of Research: Not specified in the source content.

Article Title: Not specified in the source content.

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Web References: Not specified beyond the image URL provided.

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Image Credits: Hebrew University of Jerusalem

Keywords: Universities, Scientific organizations, Educational institutions, Education administration, Scientific community

The premise stands on the extraordinary progress of LLMs, sophisticated AI systems trained on vast amounts of textual data from the web, books, and academic literature. These models, initially designed for natural language processing tasks like translation or summarization, have evolved into remarkably nuanced predictors of complex human traits. Wolfram’s study methodically benchmarks the predictive power of LLMs against genomic data and expert human evaluations, uncovering insights that could redefine assessment metrics in psychology and education.

Genomics, which has long been heralded as a critical avenue to understanding individual differences in cognition, relies on identifying specific gene variants linked to intelligence and learning ability. While powerful, genomic predictors often require extensive datasets, are prone to ethical controversies, and frequently struggle to capture the environmental and sociocultural components influencing cognitive development. Wolfram’s research posits that LLMs, grounded in linguistic and contextual world knowledge, offer a complementary—and in some cases superior—approach.

The methodology deployed in the study involves applying state-of-the-art LLMs to naturally occurring textual outputs associated with individuals, such as essays, social media posts, and academic writing. By analyzing syntactic complexity, semantic richness, and thematic coherence, the models generate cognitive profiles without explicit phenotype data. These AI-derived predictions are then directly compared to polygenic scores derived from genome-wide association studies (GWAS) and to expert assessments conducted by seasoned psychologists and educators.

Notably, the results demonstrate that LLMs achieve predictive accuracy on par with genomic methods, a finding that challenges the long-held assumption that genetic markers remain the gold standard for identifying cognitive aptitude. The AI’s ability to contextualize language within broader narratives and cultural frameworks allows it to capture subtle cognitive and educational signals that genetic data may overlook. Moreover, when combined with expert assessments, LLM-generated predictions enhance overall accuracy, indicating a complementary relationship rather than a competitive one.

The implications of this research extend beyond academic curiosity into practical applications. In education, for instance, AI-powered assessments could provide real-time, scalable, and non-invasive evaluations of student learning styles, comprehension, and potential cognitive challenges, facilitating personalized learning experiences at an unprecedented scale. This prospect could democratize access to educational resources, particularly in under-resourced settings where expert evaluators are scarce.

Furthermore, the study addresses concerns related to privacy and data security by emphasizing that LLM predictions can be made from publicly available or consented textual data without the need for genetic sampling, which is costlier and more intrusive. This advantage positions large language models as ethically favorable tools, provided that transparency and consent are rigorously maintained in data collection practices.

Critically, Wolfram also explores the limitations inherent in relying solely on AI models. While LLMs demonstrate remarkable capacity, they are sensitive to biases encoded in training data, including cultural, socioeconomic, and linguistic biases. These factors could skew predictive outcomes if not carefully mitigated through refined model training and validation techniques. The study calls for an interdisciplinary approach where AI specialists collaborate closely with cognitive scientists and ethicists to ensure equitable and responsible deployment.

In the realm of cognitive science, the ability to quantify mental constructs such as working memory, fluid intelligence, and verbal reasoning through language-based AI tools opens new avenues for research. Traditionally challenging to measure with precision, these dimensions are accessible by LLMs analyzing discourse patterns and conceptual complexity. This reframing could accelerate hypothesis testing and theory development, transforming the way intelligence is operationalized and measured.

Moreover, the predictive use of large language models may influence neuropsychological assessments, psychiatric evaluations, and even workplace talent identification. Early indications suggest that nuanced verbal outputs captured by LLMs correlate with cognitive function and educational attainment, offering auxiliary data points that can supplement clinical and administrative decision-making processes. The integration of these models could streamline assessments and offer continuous monitoring capabilities unobtainable by conventional methods.

Wolfram’s study further engages with the ethical dimensions of employing AI in predictive psychology. The paper underscores the necessity of safeguarding individuals from potential misuse of predictive data, highlighting risks such as stigmatization, discrimination, and privacy breaches. It advocates for stringent regulatory frameworks and continuous monitoring to balance innovation with respect for human rights.

Looking ahead, the research hints at the prospect of synergistic models that integrate genomic, linguistic, and expert inputs, leveraging the strengths of each modality. Such hybrid approaches promise more comprehensive and nuanced forecasts of cognitive ability and educational outcomes, establishing a new frontier in predictive accuracy.

Importantly, this emerging AI-driven paradigm democratizes knowledge by enabling non-invasive, cost-effective, and scalable approaches to measure cognition and learning. It offers a potent tool to bridge disparities in educational achievement and cognitive science research infrastructure worldwide, potentially transforming policy development and individualized support services.

In summary, the study by Wolfram marks a watershed moment in cognitive and educational assessment, revealing that large language models offer a predictive capacity that challenges long-established methodologies. By harnessing the intrinsic link between language and cognition, these AI systems stand poised to revolutionize our understanding of the human mind, with profound implications for education, psychology, and beyond.

As large language models continue to evolve, their integration into scientific inquiry and practical applications must be guided by ethical considerations, interdisciplinary collaboration, and rigorous validation. The promise of AI as a complementary or even superior predictor of cognition beckons a future where technology and human expertise converge to unlock unprecedented insights into the fabric of intelligence and learning.

Subject of Research: Cognitive and educational outcome prediction using large language models compared to genomics and expert assessment.

Article Title: Large language models predict cognition and education close to or better than genomics or expert assessment.

Article References:
Wolfram, T. Large language models predict cognition and education close to or better than genomics or expert assessment. Commun Psychol 3, 95 (2025). https://doi.org/10.1038/s44271-025-00274-x

Image Credits: AI Generated