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
• Algorithm

Selected publications

• Single-Cell Perturbations Reveal Selective Modulation of Causal Connectivity During Decision-Making

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-08 · 1 citations

  articleOpen access

  How does cortical connectivity support decision-making? Behavioral tasks often involve multiple sequential phases that implement different computations. For perceptual decision-making during navigation these can include evidence accumulation, decision commitment, and motor program read out. How are these different phases implemented in circuits with fixed anatomical synaptic connectivity? One potential contribution is that the connectivity of neurons is modulated in the different phases, but this has never been tested. Here we used an all-optical method to probe the causal connectivity of excitatory neurons in layer 2/3 of mouse retrosplenial cortex during different behavioral epochs of a navigation-based decision-making task, as well as in the absence of the task. In-task connectivity was different from no-task connectivity: furthermore, these differences were selective to the cue / decision phase, tapering off in later stages of the task. We propose that fast modulation of connectivity is a prevalent mechanism in neural circuit function.

  PublisherDOI

• Transitions in dynamical regime and neural mode during perceptual decisions

  Nature · 2025-09-17 · 8 citations

  articleOpen accessSenior authorCorresponding

  Abstract Perceptual decision-making is thought to be mediated by neuronal networks with attractor dynamics 1,2 . However, the dynamics underlying the complex neuronal responses during decision-making remain unclear. Here we use simultaneous recordings of hundreds of neurons, combined with an unsupervised, deep-learning-based method, to discover decision-related neural dynamics in the rat frontal cortex and striatum as animals accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes: an initial phase dominated by sensory inputs, followed by a phase dominated by autonomous dynamics, with the flow direction (that is, neural mode) largely orthogonal to that in the first regime. We propose that this transition marks the moment of decision commitment, that is, the time when the animal makes up its mind. To test this, we developed a simplified model of the dynamics to estimate a putative neurally inferred time of commitment (nTc) for each trial. This model captures diverse single-neuron temporal profiles, such as ramping and stepping 3,4 . The estimated nTc values were not time locked to stimulus or response timing but instead varied broadly across trials. If nTc marks commitment, evidence before this point should affect the decision, whereas evidence afterwards should not. Behavioural analysis aligned to nTc confirmed this prediction. Our findings show that decision commitment involves a rapid, coordinated transition in dynamical regime and neural mode and suggest that nTc offers a useful neural marker for studying rapid changes in internal brain state.

  PublisherOA PDFDOI

• FixGrower: An efficient and robust curriculum for shaping fixation behavior in rodents

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-17

  preprintOpen accessSenior authorCorresponding

  Center-port fixation is a common prerequisite for many freely-moving rodent tasks in neuroscience and psychology. However, typical protocols for shaping this behavior are non-standardized and inefficient. Moreover, motor errors in fixation termed ‘violations’ often account for a significant fraction of experimental trials, leading to notable data loss during experiments. In light of this, we developed FixGrower, a standardized protocol for center-port fixation training. FixGrower (1) requires a longer initial fixation requirement, (2) increases the required fixation duration at session boundaries customized to each animals’ performance, and (3) delays the introduction of violation penalties until the end of training. We demonstrate FixGrower decreases training time by 61%, yields low violation rates, and generalizes across rodent species and task difficulty. Moreover, the success of this curriculum is well supported by theories of operant conditioning and reinforcement learning. Our findings establish FixGrower as an efficient and broadly applicable curriculum for training fixation behavior in rodents, thereby accelerating training of many tasks in the field.

  PublisherOA PDFDOI

• A Multi‑Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision‑Making

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-30

  preprintOpen access

  Brains excel at robust decision-making and data-efficient learning. Understanding the architectures and dynamics underlying these capabilities can inform inductive biases for deep learning. We present a multi-region brain model that explores the normative role of structured memory circuits in a spatially embedded binary decision-making task from neuroscience. We counterfactually compare the learning performance and neural representations of reinforcement learning (RL) agents with brain models of different interaction architectures between grid and place cells in the entorhinal cortex and hippocampus, coupled with an action-selection cortical recurrent neural network. We demonstrate that a specific architecture-where grid cells receive and jointly encode self-movement velocity signals and decision evidence increments-optimizes learning efficiency while best reproducing experimental observations relative to alternative architectures. Our findings thus suggest brain-inspired structured architectures for efficient RL. Importantly, the models make novel, testable predictions about organization and information flow within the entorhinal-hippocampal-neocortical circuit: we predict that grid cells must conjunctively encode position and evidence for effective spatial decision-making, directly motivating new neurophysiological experiments.

  PublisherOA PDFDOI

• Working memory expands shared task representations in cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-30

  preprintOpen access

  Cognition is thought to emerge from the flexible organization of neural activity, yet how this organization reconfigures across behaviors varying in cognitive load remains unclear. We investigated how the structure of working-memory representations in the cortex compares to task representations that do not involve working memory. We used a task-switching paradigm in virtual reality, where mice alternated between a navigation-based working-memory task and a simpler task with matched sensorimotor demands. During behavior, we simultaneously imaged three cortical areas: higher visual area AM, and two association areas-premotor (M2) and retrosplenial cortex. At the single-neuron level, trial-averaged activity appeared similar across tasks. However, pairwise correlations decreased during the working-memory task, particularly in association areas. In addition, the corresponding linear task subspace explained the variance of both tasks equally well, whereas the simpler task subspace failed to do so, suggesting an asymmetric relationship between them. Nonlinear dimensionality reduction revealed a shared low-dimensional structure across tasks. Yet, the organization of neuronal firing fields along this shared structure accounted for the difference in pairwise correlations: in the working-memory task, firing fields were more disjoint, especially among neurons in association areas that formed sequences along the memory dimension. Moreover, the degree of overlap between these firing fields predicted the mice's behavioral reliance on working memory. We conclude that behaviors varying in cognitive demands are supported by a single low-dimensional neural structure, which can expand or contract depending on cognitive load. We thus provide a framework for how task representations across the cortex reconfigure to support cognitive processes.

  PublisherOA PDFDOI

• Individual variability of neural computations underlying flexible decisions

  Nature · 2024-11-28 · 39 citations

  articleOpen accessSenior authorCorresponding

  Abstract The ability to flexibly switch our responses to external stimuli according to contextual information is critical for successful interactions with a complex world. Context-dependent computations are necessary across many domains 1–3 , yet their neural implementations remain poorly understood. Here we developed a novel behavioural task in rats to study context-dependent selection and accumulation of evidence for decision-making 4–6 . Under assumptions supported by both monkey and rat data, we first show mathematically that this computation can be supported by three dynamical solutions and that all networks performing the task implement a combination of these solutions. These solutions can be identified and tested directly with experimental data. We further show that existing electrophysiological and modelling data are compatible with the full variety of possible combinations of these solutions, suggesting that different individuals could use different combinations. To study variability across individual subjects, we developed automated, high-throughput methods to train rats on our task and trained many subjects using these methods. Consistent with theoretical predictions, neural and behavioural analyses revealed substantial heterogeneity across rats, despite uniformly good task performance. Our theory further predicts a specific link between behavioural and neural signatures, which was robustly supported in the data. In summary, our results provide an experimentally supported theoretical framework to analyse individual variability in biological and artificial systems that perform flexible decision-making tasks, open the door to cellular-resolution studies of individual variability in higher cognition, and provide insights into neural mechanisms of context-dependent computation more generally.

  PublisherDOI

• Author response: Neural population dynamics underlying evidence accumulation in multiple rat brain regions

  2024-08-02 · 1 citations

  peer-reviewOpen access
  PublisherDOI

• Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-28 · 1 citations

  preprintOpen accessCorresponding

  Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

  PublisherOA PDFDOI

• 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 world is changing i.e. nonstationary. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct decision-making datasets of male rats, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.

  PublisherOA PDFDOI

• Magnetic voluntary head-fixation in transgenic rats enables lifespan imaging of hippocampal neurons

  Nature Communications · 2024-05-16 · 10 citations

  articleOpen access

  The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory are not fully understood. Two-photon calcium imaging allows the activity of individual cells to be followed across long periods, but conventional approaches require head-fixation, which limits the type of behavior that can be studied. We present a magnetic voluntary head-fixation system that provides stable optical access to the brain during complex behavior. Compared to previous systems that used mechanical restraint, there are no moving parts and animals can engage and disengage entirely at will. This system is failsafe, easy for animals to use and reliable enough to allow long-term experiments to be routinely performed. Animals completed hundreds of trials per session of an odor discrimination task that required 2-4 s fixations. Together with a reflectance fluorescence collection scheme that increases two-photon signal and a transgenic Thy1-GCaMP6f rat line, we are able to reliably image the cellular activity in the hippocampus during behavior over long periods (median 6 months), allowing us track the same neurons over a large fraction of animals' lives (up to 19 months).

  PublisherOA PDFDOI

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

• Brain Registration and Histology

  NIH · $30.7M · 2017–2023

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)