Carlos Brody

About

Carlos Brody is a professor affiliated with Princeton University and the Howard Hughes Medical Institute, leading the Laboratory for Quantitative and Computational Systems Neuroscience. His research focuses on understanding the signals shared between brain regions, how these signals coordinate to produce cognition and behavior, and the internal neural signals that underpin decision-making processes. Brody's lab conducts large-scale neural recordings across the brain using advanced techniques such as Neuropixels probes, combined with well-controlled cognitive behaviors in rats, to investigate the internal conversation of the mind. A significant contribution from Brody's research is the discovery of the 'Neurally-inferred Time of Commitment' (nTc), a biomarker detectable in neural populations that marks the moment a subject makes a decision internally, even if the decision is not overtly expressed. His work demonstrates that internal signals like nTc induce sweeping state changes across the brain, highlighting the importance of internal signals in neural activity. Brody's lab aims to uncover more internal signals by leveraging advancements in neural recording technology, AI-based analysis tools, and controlled behavioral paradigms. His research endeavors to define the future of cognitive systems neuroscience by integrating large-scale neural data with innovative analytical methods to understand the structure and meaning of internal neural signals.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Neuroscience
• Biology
• Chemistry
• Cognitive psychology
• Genetics
• Statistics
• Mathematics

Selected publications

• Geometry of abstract learned knowledge in the hippocampus

  Nature · 2021 · 365 citations

  DOI

• Extracting the dynamics of behavior in sensory decision-making experiments

  Neuron · 2021-01-08 · 126 citations

  articleOpen access
  PublisherOA PDFDOI

• Sequential and efficient neural-population coding of complex task information

  Neuron · 2021 · 77 citations

  Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural popul…

  DOI

• Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making

  Nature Communications · 2024-01-22 · 43 citations

  articleOpen accessSenior authorCorresponding

  Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that they covary in their prevalence and that their proposed neural substrates overlap. Here we demonstrate that during decision-making, history biases and apparent lapses can both arise from a common cognitive process that is optimal under mistaken beliefs that the…

  PublisherOA PDFDOI

• Subpopulations of neurons in lOFC encode previous and current rewards at time of choice

  eLife · 2021 · 43 citations

  Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFC's well-documented role in learning, is represented by functional subpopulations in lOFC. Previously, we analyzed neural recordings from rats performing a value-based decision-making task, and we documented trial-by-trial learning…

  DOI

Recent grants

• Mechanisms of neural circuit dynamics in working memory

  NIH · $3.1M · 2014–2018

• Neural circuit underlying decisions driven by accumulation evidence

  NIH · $2.0M · 2015–2020

• NIH Grant R01MH067991

  NIH · $1.5M · 2010

Frequent coauthors

• Alex T. Piet

  Allen Institute for Neural Dynamics

  67 shared

• Athena Akrami

  University College London

  67 shared

• David W. Tank

  Princeton University

  62 shared

• Kevin J Miller

  DeepMind (United Kingdom)

  49 shared

• Charles D. Kopec

  Princeton University

  38 shared

• Matthew Botvinick

  34 shared

• Ahmed El Hady

  31 shared

• Christine M. Constantinople

  New York University

  29 shared

Labs

• Brodylab | Laboratory for Quantitative and Computational Systems NeurosciencePI

Awards & honors

• SFARI Bridge to Independence Award (2021)
• Best Paper Award at RLDM conference (2022)

Similar researchers at Princeton University

• Nathaniel Dawh-96
• Sanjeev Arorah-84
• Jia Dengh-61