About

David W. Tank is a Professor of Molecular Biology and Neuroscience at Princeton University. He is affiliated with the Center for the Physics of Biological Function, an NSF Physics Frontier Center. His role involves research and teaching in the fields of biophysics, molecular biology, and neuroscience, contributing to the understanding of biological functions through a physics-based approach. Further details about his specific research focus, background, and key contributions are not provided on the page.

Research topics

• Artificial Intelligence
• Computer Science
• Neuroscience
• Biology
• Psychology
• Statistics
• Algorithm
• Evolutionary biology
• Computational biology
• Mathematics
• Genetics
• Chemistry

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 accessSenior author

  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

• Neural circuit models for evidence accumulation through choice-selective sequences

  Nature Communications · 2026-03-17 · 1 citations

  articleOpen access

  Decision making is traditionally thought to be mediated by neurons that accumulate evidence through persistent activity. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We developed two candidate circuit models in which neurons are active sequentially and transfer evidence faithfully to the next active population. One model encodes evidence in the relative firing of two competing chains of neurons, and the other in the network location of a stereotyped, bump-like pattern of neural activity. Neural recordings from four brain regions during an evidence accumulation task revealed that different regions displayed evidence tuning consistent with different candidate models. This work provides a mechanistic explanation for how graded information may be precisely accumulated within and transferred between neural populations, and suggests that different brain regions may accumulate evidence through different circuit mechanisms.

  PublisherDOI

• Error-driven changes in hippocampal representations accompany flexible re-learning

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-21

  preprintOpen accessSenior author

  Flexible behavior requires both the learning of new associations, and the suppression of previous ones, but how neural circuits achieve this balance remains unclear. Here we show that continuous changes in hippocampal representations, known as drift, may facilitate this process. We used voluntary head-fixation and calcium imaging to record from CA1 in rats during an odor-guided navigation task that required frequent re-learning. We found systematic representational changes over the course of the multi-hour sessions that were increased following errors. A simple neural network model revealed that such error-driven drift can enable flexible re-learning by allowing new associations to form from new neural patterns. A consequence of this is that previous associations are maintained in latent synaptic weights. These findings reconcile the apparent tension between representational drift and stable memory storage, demonstrating how dynamic neural codes could support both flexible behavior and lasting memories.

  PublisherOA PDFDOI

• Working memory expands shared task representations in cortex

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

  preprintOpen accessSenior authorCorresponding

  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

• Connectomic reconstruction from hippocampal CA3 reveals spatially graded mossy fiber inputs and selective feedforward inhibition to pyramidal cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-15 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Abstract The mossy fiber (MF) connections to pyramidal cells in hippocampal CA3 are hypothesized to participate in pattern separation and memory encoding, yet no large-scale neuronal wiring diagram exists for these connections. We assembled a 3D electron microscopy volume (∼1×1×0.1mm 3 ) from mouse hippocampal CA3. By proofreading and automated segmentation, we reconstructed and classified all soma-containing neurons—including 1,815 pyramidal cells and 229 inhibitory cells—and over 55,000 MFs. Pyramidal cells receive more numerous MF inputs along a proximodistal gradient. Some distal cells show surprisingly high convergence via relatively small terminals with fewer vesicles. Pyramidal cells share significantly more MF inputs than networks randomized by degree-preserving swap, and are better approximated by networks randomized by proximity-preserving swap. We identify a feedforward inhibitory circuit from MFs via perisomatic interneurons that selectively target a pyramidal subtype. We demonstrated large-scale mapping across levels in the hippocampus—from circuits to cell types to vesicles. The dataset is shared through Pyr, an online platform for hippocampal connectomics.

  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

• Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy

  Nature Communications · 2024-08-10 · 10 citations

  articleOpen access

  Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.

  PublisherOA PDFDOI

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

  Nature Communications · 2024-05-16 · 10 citations

  articleOpen accessSenior authorCorresponding

  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

• Unsupervised discovery of the shared and private geometry in multi-view data

  arXiv (Cornell University) · 2024-08-22 · 1 citations

  preprintOpen access

  Studying complex real-world phenomena often involves data from multiple views (e.g. sensor modalities or brain regions), each capturing different aspects of the underlying system. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental insights into each view and the system as a whole. However, existing methods to characterize such relationships lack the expressivity required to capture nonlinear relationships, describe only shared sources of variance, or discard geometric information that is crucial to drawing insights from data. Here, we present SPLICE: a neural network-based method that infers disentangled, interpretable representations of private and shared latent variables from paired samples of high-dimensional views. Compared to competing methods, we demonstrate that SPLICE 1) disentangles shared and private representations more effectively, 2) yields more interpretable representations by preserving geometry, and 3) is more robust to incorrect a priori estimates of latent dimensionality. We propose our approach as a general-purpose method for finding succinct and interpretable descriptions of paired data sets in terms of disentangled shared and private latent variables.

  PublisherOA PDFDOI

• Neural circuit models for evidence accumulation through choice-selective sequences

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-09-04 · 4 citations

  preprintOpen access

  Decision making is traditionally thought to be mediated by neurons that accumulate evidence through persistent activity. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We developed two new candidate circuit models in which neurons are active sequentially and transfer evidence faithfully to the next active population. One model encodes evidence in the relative firing of two competing chains of neurons, and the other in the network location of a stereotyped pattern ("bump") of neural activity. Neural recordings from four brain regions during an evidence accumulation task revealed that different regions displayed evidence tuning consistent with different candidate models. This work provides a mechanistic explanation for how graded information may be precisely accumulated within and transferred between neural populations, and suggests that different brain regions may accumulate evidence through different circuit mechanisms.

  PublisherOA PDFDOI

Recent grants

• Mechanisms of neural circuit dynamics in working memory

  NIH · $3.1M · 2014–2018

• Grid Cell Dynamics During Navigation In Virtual Reality

  NIH · $362k · 2012–2019

• Neural circuit imaging and stimulation at cellular resolution in virtual reality

  NIH · $3.6M · 2008–2020

• NIH Grant R21NS082956

  NIH · $424k · 2016

• NIH Grant RC1NS068148

  NIH · $1.0M · 2012

Frequent coauthors

• Carlos D. Brody

  Howard Hughes Medical Institute

  62 shared

• H. Sebastian Seung

  Princeton University

  60 shared

• Emre Aksay

  Cornell University

  53 shared

• R. Baker

  48 shared

• Guy Major

  44 shared

• Sue Ann Koay

  Howard Hughes Medical Institute

  37 shared

• Brett D. Mensh

  Howard Hughes Medical Institute

  35 shared

• Jeffrey L. Gauthier

  Swarthmore College

  33 shared