About

Tatiana Engel is an Associate Professor at the Princeton Neuroscience Institute. Her research focuses on core brain functions—perception, action, and decision-making—that depend on complex patterns of neural activity coordinated within local microcircuits and across brain regions. Her lab investigates how this widespread activity emerges from anatomical connectivity and how it gives rise to behavior, utilizing computational and theoretical approaches. They develop mathematical models and data analysis methods to uncover distributed circuit mechanisms from rich experimental data, employing tools and ideas from fields such as statistical mechanics, machine learning, dynamical systems theory, and information theory. Engel's work benefits from close collaborations with experimental neuroscience laboratories that collect neurophysiological data in animals engaged in tasks involving attention, decision-making, and learning. She earned her Ph.D. from Humboldt University of Berlin in 2007.

Research topics

• Computer Science
• Neuroscience
• Psychology
• Sociology
• Artificial Intelligence
• Machine Learning
• Cognitive psychology
• Algorithm
• Mathematics
• Engineering

Selected publications

• Finding clues to circuit structure in population dynamics and single-neuron selectivity

  Neuron · 2026-05-01

  article1st authorCorresponding
  PublisherDOI

• Brain-wide organization of intrinsic timescales at single-neuron resolution

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-31 · 4 citations

  preprintOpen accessSenior authorCorresponding

  Variations in intrinsic neural timescales across the mammalian forebrain reflect the anatomical structure and functional specialization of brain areas and individual neurons. Yet, the organization of timescales beyond the forebrain remains unexplored. We analyzed intrinsic timescales of single neurons across the entire mouse brain. Median timescales were up to fivefold longer in the midbrain and hindbrain than in the forebrain. Spatial patterns of gene expression predicted timescale variation at a resolution finer than brain-area boundaries. Across neurons, the diversity of timescales revealed a multiscale architecture, in which fast timescales determined regional differences in medians, while slow timescales universally followed a power-law distribution with an exponent near 2, indicating a shared dynamical regime across the brain consistent with the edge of instability or chaos. These organizing principles for the dynamics of single neurons across the brain provide a foundation for linking cellular activity with regional specialization and brain-wide computation.

  PublisherOA PDFDOI

• Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks

  Nature Machine Intelligence · 2025-10-20 · 5 citations

  articleOpen accessSenior author

  Trained recurrent neural networks (RNNs) have become the leading framework for modelling neural dynamics in the brain, owing to their capacity to mimic how population-level computations arise from interactions among many units with heterogeneous responses. RNN units are commonly modelled using various nonlinear activation functions, assuming these architectural differences do not affect emerging task solutions. Here, contrary to this view, we show that single-unit activation functions confer inductive biases that influence the geometry of neural population trajectories, single-unit selectivity and fixed-point configurations. Using a model distillation approach, we find that differences in neural representations and dynamics reflect qualitatively distinct circuit solutions to cognitive tasks emerging in RNNs with different activation functions, leading to disparate generalization behaviour on out-of-distribution inputs. Our results show that seemingly minor architectural differences provide strong inductive biases for task solutions, raising a question about which RNN architectures better align with mechanisms of task execution in biological networks.

  PublisherOA PDFDOI

• A brain-wide map of neural activity during complex behaviour

  Nature · 2025-09-03 · 61 citations

  articleOpen access

  . It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here we report a comprehensive set of recordings from 621,733 neurons recorded with 699 Neuropixels probes across 139 mice in 12 laboratories. The data were obtained from mice performing a decision-making task with sensory, motor and cognitive components. The probes covered 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map and assess how neural activity encodes key task variables. Representations of visual stimuli transiently appeared in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of midbrain and hindbrain regions that also encoded choices. Neural responses correlated with impending motor action almost everywhere in the brain. Responses to reward delivery and consumption were also widespread. This publicly available dataset represents a resource for understanding how computations distributed across and within brain areas drive behaviour.

  PublisherOA PDFDOI

• The dynamics and geometry of choice in the premotor cortex

  Nature · 2025-06-25 · 24 citations

  articleOpen accessSenior author

  Abstract The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code 1,2 . Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations 3–8 . Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a unifying geometric principle for neural encoding of sensory and dynamic cognitive variables.

  PublisherOA PDFDOI

• A low-dimensional glomerular code for olfactory perception

  Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) · 2025-01-01

  article

• No Central Executive? Decision Formation Through Multi-Area Population Dynamics

  Journal of Neuroscience · 2025-11-12 · 2 citations

  articleOpen access

  Perceptual decision-making is the process by which sensory evidence is combined with prior knowledge and transformed into possible movement plans according to a rule or policy. Classic studies suggested that perceptual decisions emerge from a feedforward hierarchy of brain areas with distinct functions and fairly homogeneous neural representations. However, more recent findings argue that decisions emerge from distributed, recurrent computations across many brain areas (a "heterarchy") with complex, heterogeneous representations. How can we make sense of these findings in a way that preserves the computational elegance of the conventional view? In this review, we describe how a new generation of studies is leveraging high-density electrophysiology, incisive task designs, causal manipulations (e.g., optogenetics) and statistical approaches for probing inter-area communication, and theoretical methods that connect population dynamics with representational geometry to build a modern framework for understanding perceptual decisions.

  PublisherOA PDFDOI

• Brain-wide representations of prior information in mouse decision-making

  Nature · 2025-09-03 · 43 citations

  articleOpen access

  . Here, to investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with a prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions that, notably, span all levels of processing, from early sensory areas (the lateral geniculate nucleus and primary visual cortex) to motor regions (secondary and primary motor cortex and gigantocellular reticular nucleus) and high-level cortical regions (the dorsal anterior cingulate area and ventrolateral orbitofrontal cortex). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers a brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large-scale recordings on a single standardized task.

  PublisherOA PDFDOI

• Latent circuit inference from heterogeneous neural responses during cognitive tasks

  Nature Neuroscience · 2025-02-10 · 25 citations

  articleOpen accessSenior author

  Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.

  PublisherOA PDFDOI

• A doubly stochastic renewal framework for partitioning spiking variability

  Nature Communications · 2025-09-30 · 1 citations

  articleOpen accessSenior author

  The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. An inhomogeneous Poisson process, the standard model for partitioning firing rate and spiking irregularity, cannot account for diverse spike statistics observed across neurons. We introduce a doubly stochastic renewal point process, a flexible mathematical framework for partitioning spiking variability, which captures the broad spectrum of spiking irregularity from periodic to super-Poisson. We validate our partitioning framework using intracellular voltage recordings and develop a method for estimating spiking irregularity from data. We find that the spiking irregularity of cortical neurons decreases from sensory to association areas and is nearly constant for each neuron under many conditions but can also change across task epochs. Spiking network models show that spiking irregularity depends on connectivity and can change with external input. These results help improve the precision of estimating firing rates on single trials and constrain mechanistic models of neural circuits.

  PublisherOA PDFDOI

Recent grants

• Discovering dynamic computations from large-scale neural activity recordings

  NIH · $1.3M · 2018–2022

• Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior

  NIH · $1.3M · 2023–2024

Frequent coauthors

• Anna Levina

  Bernstein Center for Computational Neuroscience Tübingen

  30 shared

• Roxana Zeraati

  26 shared

• Tirin Moore

  20 shared

• Nicholas A. Steinmetz

  Stanford University

  17 shared

• Claudia Kopp

  Klinikum rechts der Isar

  16 shared

• Matthias Kreuzer

  16 shared

• E. Kochs

  16 shared

• Verena Hammelmann

  Ludwig-Maximilians-Universität München

  16 shared

Labs

• Engel LabPI