About

William Newsome is the Harman Family Provostial Professor of Neurobiology at the Stanford University School of Medicine and the Founding Director of the Wu Tsai Neurosciences Institute. He holds a B.S. degree in physics from Stetson University and a Ph.D. in biology from the California Institute of Technology. Dr. Newsome is a leading investigator in systems and cognitive neuroscience, with fundamental contributions to understanding the neural mechanisms underlying visual perception and simple forms of decision making. His research focuses on how neural systems process visual information and support decision-making processes. He has received numerous honors and awards, including the Rank Prize in Optoelectronics, the Spencer Award, the Distinguished Scientific Contribution Award of the American Psychological Association, the Dan David Prize of Tel Aviv University, the Karl Spencer Lashley Award of the American Philosophical Society, and the Champalimaud Vision Award. Dr. Newsome has delivered distinguished lectureships such as the Marr Lecture at the University of Cambridge and the Brenda Milner Lecture at McGill University. He was elected to the National Academy of Sciences in 2000 and the American Philosophical Society in 2011. Additionally, he co-chaired the NIH BRAIN working group, which was tasked with forming a national plan for neuroscience research in the United States.

Research topics

• Computer Science
• Psychology
• Machine Learning
• Medicine
• Artificial Intelligence
• Neuroscience
• Biology
• Cognitive science
• Algorithm
• Cognitive psychology

Selected publications

• Are task representations gated in macaque prefrontal cortex?

  arXiv (Cornell University) · 2023-06-29 · 2 citations

  preprintOpen access

  A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques. This short note proposes an alternative explanation for the reported results from the macaque data.

  PublisherOA PDFDOI

• Frontmatter

  Cornell University Press eBooks · 2022-08-26

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Decoding and perturbing decision states in real time

  Nature · 2021 · 138 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• Differential Encoding in Prefrontal Cortex Projection Neuron Classes Across Cognitive Tasks

  SSRN Electronic Journal · 2020-01-01 · 2 citations

  articleOpen access
  PublisherDOI

• Remote, brain region–specific control of choice behavior with ultrasonic waves

  Science Advances · 2020 · 129 citations

  Senior authorCorresponding
  • Computer Science
  • Neuroscience
  • Computer Science

  The ability to modulate neural activity in specific brain circuits remotely and systematically could revolutionize studies of brain function and treatments of brain disorders. Sound waves of high frequencies (ultrasound) have shown promise in this respect, combining the ability to modulate neuronal activity with sharp spatial focus. Here, we show that the approach can have potent effects on choice behavior. Brief, low-intensity ultrasound pulses delivered noninvasively into specific brain regions of macaque monkeys influenced their decisions regarding which target to choose. The effects were substantial, leading to around a 2:1 bias in choices compared to the default balanced proportion. The effect presence and polarity was controlled by the specific target region. These results represent a critical step towards the ability to influence choice behavior noninvasively, enabling systematic investigations and treatments of brain circuits underlying disorders of choice.

  PublisherOA PDFDOI

• Opportunities and limitations of genetically modified nonhuman primate models for neuroscience research

  Proceedings of the National Academy of Sciences · 2020 · 122 citations

  • Neuroscience
  • Psychology
  • Cognitive science

  The recently developed new genome-editing technologies, such as the CRISPR/Cas system, have opened the door for generating genetically modified nonhuman primate (NHP) models for basic neuroscience and brain disorders research. The complex circuit formation and experience-dependent refinement of the human brain are very difficult to model in vitro, and thus require use of in vivo whole-animal models. For many neurodevelopmental and psychiatric disorders, abnormal circuit formation and refinement might be at the center of their pathophysiology. Importantly, many of the critical circuits and regional cell populations implicated in higher human cognitive function and in many psychiatric disorders are not present in lower mammalian brains, while these analogous areas are replicated in NHP brains. Indeed, neuropsychiatric disorders represent a tremendous health and economic burden globally. The emerging field of genetically modified NHP models has the potential to transform our study of higher brain function and dramatically facilitate the development of effective treatment for human brain disorders. In this paper, we discuss the importance of developing such models, the infrastructure and training needed to maximize the impact of such models, and ethical standards required for using these models.

  PublisherOA PDFDOI

• Value and choice as separable, stable representations in orbitofrontal cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2020-01-02 · 1 citations

  preprintOpen accessSenior author

  Abstract Value-based decision-making operates on multiple variables—including offer value, choice, expected outcome, and recent history—each functioning at different times in the decision process. Orbitofrontal cortex (OFC) has long been implicated in value-based decision-making, but it is unclear how downstream circuits might read out complex OFC responses into separate representations of the relevant variables to support different cognitive functions at specific times. We recorded from single neurons in OFC while macaque monkeys made cost-benefit decisions to juice offers. Using a novel analysis—optimal targeted dimensionality reduction—we discovered orthogonal, static dimensions (i.e. linear combinations of neurons) that selectively represented the value, choice, and expected reward of the present and, separately, previous offers. The neural composition of most representations was stable over discrete time periods that aligned to concurrent cognitive demands. We applied a new set of statistical methods to determine that the sensitivity, specificity and stability of the representations were greater than expected from the low-level features—dimensionality and temporal smoothness—of the responses alone. The separability and stability of OFC representations suggest a mechanism by which downstream circuits can read out specific task-relevant variables at appropriate times.

  PublisherOA PDFDOI

• Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

  bioRxiv (Cold Spring Harbor Laboratory) · 2020-03-14 · 13 citations

  preprintOpen access

  SUMMARY Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca 2+ -activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

  PublisherOA PDFDOI

• Value and choice as separable and stable representations in orbitofrontal cortex

  Nature Communications · 2020-07-10 · 43 citations

  articleOpen accessSenior author

  Value-based decision-making requires different variables-including offer value, choice, expected outcome, and recent history-at different times in the decision process. Orbitofrontal cortex (OFC) is implicated in value-based decision-making, but it is unclear how downstream circuits read out complex OFC responses into separate representations of the relevant variables to support distinct functions at specific times. We recorded from single OFC neurons while macaque monkeys made cost-benefit decisions. Using a novel analysis, we find separable neural dimensions that selectively represent the value, choice, and expected reward of the present and previous offers. The representations are generally stable during periods of behavioral relevance, then transition abruptly at key task events and between trials. Applying new statistical methods, we show that the sensitivity, specificity and stability of the representations are greater than expected from the population's low-level features-dimensionality and temporal smoothness-alone. The separability and stability suggest a mechanism-linear summation over static synaptic weights-by which downstream circuits can select for specific variables at specific times.

  PublisherOA PDFDOI

• Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

  Cell · 2020-12-17 · 128 citations

  articleOpen access
  PublisherOA PDFDOI

Recent grants

• NIH Grant R01EY005603

  NIH · $2.2M · 2009

• NIH Grant R37EY005603

  NIH · $2.4M · 2003

Frequent coauthors

• Kenneth H. Britten

  University of California, Davis

  42 shared

• Krishna V. Shenoy

  Howard Hughes Medical Institute

  36 shared

• Diogo Peixoto

  Stanford University

  30 shared

• Gregory D. Horwitz

  University of Washington

  28 shared

• Greg S. Corrado

  Google (United States)

  27 shared

• Chandramouli Chandrasekaran

  26 shared

• Leo P. Sugrue

  26 shared

• J. Anthony Movshon

  New York University

  23 shared

Education

• B.S.

  Stetson University

• Ph.D.

  California Institute of Technology

Awards & honors

• Henry J. Kaiser Award for Excellence in Teaching (1991, 1997…
• Golden Brain Award, Minerva Foundation (1992)
• The Rank Prize in Optoelectronics, The Rank Prize Funds, Lon…
• MERIT Award, National Eye Institute (1993)
• W. Alden Spencer Award for highly original contributions to…