Biology subjects

Tank, D. W.

Publications and source records attributed to Tank, D. W..

At least 19 recordsLinked to original sources

bioRxiv2026

The geometry of knowledge in the hippocampal-prefrontal system

Decision making is associated with frontal brain circuits and spatial navigation with the hippocampus. In addition, recent work in spatial decision making tasks found single neurons in both areas encoding space conjunctively with other task-relevant variables. However, circuit function is not determined by tuning alone, but also by representational geometry, i.e. the representation of task-relevant variables in neural state space. Here, using Neuropixel recordings in a complex spatial decision making task combined with nonlinear dimensionality reduction, we show an intrinsically low-dimensional neural manifold in medial prefrontal cortex (mPFC) on which key task variables were represented as smooth gradients. This geometry resembled the hippocampal (HPC) map. The mPFC and HPC manifolds from one mouse can predict the behavior across other mice and brain areas. A non-linear representational map between the mPFC and HPC manifolds demonstrates alignment in time. Our work suggests that the representational geometry in HPC and mPFC is distributed and time-aligned using low-dimensional neural codes.

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bioRxiv2026

Hippocampal engrams configure prefrontal context representations to guide flexible decisions

Flexible behavior requires using past experiences to configure cortical computations to suit current task demands. A central question in neuroscience is how memory representations control such reconfigurations. Although hippocampal (HPC) engrams can drive learned behaviors, prior studies have been largely limited to a fixed stimulus-response computation. Thus, whether engrams can drive retrieval of a set of stimulus-response mappings, rather than a specific response itself, remains unresolved. Moreover, how engrams affect cortical task representations and dynamics so as to produce engram-consistent behavior remains unstudied. Here, we address both questions by combining tagging and reactivation of HPC engrams with simultaneous large-scale recordings in medial prefrontal cortex (mPFC) during a context-dependent task-switching paradigm in mice. We report that HPC engram reactivation caused mice to apply engram-consistent decision-rules rather than a specific motor output. Simultaneous mPFC recordings revealed that reactivating the HPC engram for a given context reinstated the representation of that context in mPFC within hundreds of milliseconds, indicating that it was mediated by rapid network effects, leading to choice behavior consistent with the reactivated HPC engram. Other aspects of endogenous dynamics in mPFC were left remarkably intact. Together, our findings provide direct causal evidence that HPC engrams can configure task-relevant population states of downstream cortical circuits in real time, establishing a neural mechanism by which memory traces control flexible behavior.

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bioRxiv2026

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

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.

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bioRxiv2025

Working memory expands shared task representations in cortex

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 mices 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.

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bioRxiv2025

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

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 ([~]1x1x0.1mm3) 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.

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bioRxiv2025

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

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.*

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bioRxiv2025

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

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.

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bioRxiv2023

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

The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory remain unknown. Existing techniques to record neurons in animals are either unsuitable for longitudinal recording from the same cells or make it difficult for animals to express their full naturalistic behavioral repertoire. 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. Together with a novel two-photon fluorescence collection scheme that increases two-photon signal and a transgenic rat line that stably expresses the calcium sensor GCaMP6f in dorsal CA1, we are able to track and record activity from the same hippocampal neurons, during behavior, over a large fraction of animals lives.

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bioRxiv2022

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

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 three fold 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 beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in several weeks.

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bioRxiv2022

Modeling communication and switching nonlinear dynamics in multi-region neural activity

Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision making. However, a precise description of interactions between regions remains an open problem. Moreover, neural dynamics are nonlinear, non-stationary, and can vary dramatically across sessions, days, and animals. Here, we propose multi-region, switching dynamical systems (MR-SDS), a probabilistic model of multiple latent interacting systems that evolve with switching nonlinear dynamics and communication between regions. MR-SDS includes directed interactions between brain regions, allowing for estimation of state-dependent communication signals, and accounts for sensory inputs effects, history effects, and heterogeneity across days and animals. We show that our model accurately recovers latent trajectories, vector fields underlying switching nonlinear dynamics, and cross-region communication profiles in two simulations. We then apply our method to two large-scale, multi-region neural datasets involving mouse decision making. The first includes hundreds of neurons per region, recorded simultaneously at single-cell-resolution across 3 distant cortical regions. The second is a mesoscale widefield dataset of 8 adjacent cortical regions imaged across both hemispheres. On these multi-region datasets, our model outperforms existing piece-wise linear multi-region models and reveals multiple distinct dynamical states and a rich set of cross-region communication profiles.

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bioRxiv2021

Passive Exposure Sparsifies Neural Activity in the Primary Visual Cortex

A notable feature of neural activity is sparseness - namely, that only a small fraction of neurons in a local circuit have high activity at any moment. Not only is sparse neural activity observed experimentally in most areas of the brain, but sparseness has been proposed as an optimization or design principle for neural circuits. Sparseness can increase the energy efficiency of the neural code as well as allow for beneficial computations to be carried out. But how does the brain achieve sparse-ness? Here, we found that when neurons in the primary visual cortex were passively exposed to a set of images over several days, neural responses became more sparse. Sparsification was driven by a decrease in the response of neurons with low or moderate activity, while highly active neurons retained similar responses. We also observed a net decorrelation of neural activity. These changes sculpt neural activity for greater coding efficiency.

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bioRxiv2021

Oculomotor plant and neural dynamics suggest gaze control requires integration on distributed timescales

A fundamental principle of biological motor control is that the neural commands driving movement must conform to the response properties of the motor plants they control. In the oculomotor system, characterizations of oculomotor plant dynamics traditionally supported models in which the plant responds to neural drive to extraocular muscles on exclusively short, subsecond timescales. These models predict that the stabilization of gaze during fixations between saccades requires neural drive that approximates eye position on longer timescales and is generated through the temporal integration of brief eye velocity-encoding signals that cause saccades. However, recent measurements of oculomotor plant behaviour have revealed responses on longer timescales. Furthermore, measurements of firing patterns in the oculomotor integrator have revealed a more complex encoding of eye movement dynamics. Yet, the link between these observations has remained unclear. Here we use measurements from the larval zebrafish to link dynamics in the oculomotor plant to dynamics in the neural integrator. The oculomotor plant in both anaesthetized and awake larval zebrafish was characterized by a broad distribution of response timescales, including those much longer than one second. Analysis of the firing patterns of oculomotor integrator neurons, which exhibited a broadly distributed range of decay time constants, demonstrates the sufficiency of this activity for stabilizing gaze given an oculomotor plant with distributed response timescales. This work suggests that leaky integration on multiple, distributed timescales by the oculomotor integrator reflects an inverse model for generating oculomotor commands, and that multi-timescale dynamics may be a general feature of motor circuitry. KEY POINTSO_LIRecent observations of oculomotor plant response properties and neural activity across the oculomotor system have called into question classical formulations of both the oculomotor plant and the oculomotor integrator. C_LIO_LIHere we use measurements from new and published experiments in the larval zebrafish together with modelling to reconcile recent oculomotor plant observations with oculomotor integrator function. C_LIO_LIWe developed computational techniques to characterize oculomotor plant responses over several seconds in awake animals, demonstrating that long timescale responses seen in anesthetized animals extend to the awake state. C_LIO_LIAnalysis of firing patterns of oculomotor integrator neurons demonstrates the sufficiency of this activity for stabilizing gaze given an oculomotor plant with multiple, distributed response timescales. C_LIO_LIOur results support a formulation of gaze stabilization by the oculomotor system in which commands for stabilizing gaze are generated through integration on multiple, distributed timescales. C_LI

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bioRxiv2021

In situ X-ray assisted electron microscopy staining for large biological samples

Electron microscopy of biological tissue has recently seen an unprecedented increase in imaging throughput moving the ultrastructural analysis of large tissue blocks such as whole brains into the realm of the feasible. However, homogeneous, high quality electron microscopy staining of large biological samples is still a major challenge. To date, assessing the staining quality in electron microscopy requires running a sample through the entire staining protocol end-to-end, which can take weeks or even months for large samples, rendering protocol optimization for such samples to be inefficient. Here we present an in situ time-lapsed X-ray assisted staining procedure that opens the "black box" of electron microscopy staining and allows observation of individual staining steps in real time. Using this novel method we measured the accumulation of heavy metals in large tissue samples immersed in different staining solutions. We show that the measured accumulation of osmium in fixed tissue obeys empirically a quadratic dependence between the incubation time and sample size. We found that potassium ferrocyanide, a classic reducing agent for osmium tetroxide, clears the tissue after osmium staining and that the tissue expands in osmium tetroxide solution, but shrinks in reduced osmium solution. X-ray assisted staining gave access to the in situ staining kinetics and allowed us to develop a diffusion-reaction-advection model that accurately simulates the measured accumulation of osmium in tissue. These are first steps towards in silico staining experiments and simulation-guided optimization of staining protocols for large samples. Hence, X-ray assisted staining will be a useful tool for the development of reliable staining procedures for large samples such as entire brains of mice, monkeys or humans.

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bioRxiv2020

Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Cortical areas seem to form a hierarchy of intrinsic timescales, but whether this is causal to cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we causally tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality, and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes. Specifically, we observed selective changes in the weighting of evidence over time, such that frontal inactivations led to deficits on longer timescales than posterior cortical ones. Likewise, large-scale cortical Ca2+ activity during task performance displayed different temporal integration windows matching the effects of inactivation. Our findings suggest that distributed cortical areas accumulate evidence by leveraging their hierarchy of intrinsic timescales.

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bioRxiv2020

Amplitude modulations of sensory responses, and deviations from Weber's Law in pulsatile evidence accumulation

How do animals make behavioral decisions based on noisy sensory signals, which are moreover a tiny fraction of ongoing activity in the brain? Some theories suggest that sensory responses should be accumulated through time to reduce noise. Others suggest that feedback-based gain control of sensory responses allow small signals to be selectively amplified to drive behavior. We recorded from neuronal populations across posterior cortex as mice performed a decision-making task based on accumulating randomly timed pulses of visual evidence. Here we focus on a subset of neurons, with putative sensory responses that were time-locked to each pulse. These neurons exhibited a variety of amplitude (gain-like) modulations, notably by choice and accumulated evidence. These neural data inspired a hypothetical accumulation circuit with a multiplicative feedback-loop architecture, which parsimoniously explains deviations in perceptual discrimination from Weber-Fechner Law. Our neural observations thus led to a model that synthesizes both accumulation and feedback hypotheses.

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bioRxiv2019

Sequential and efficient neural-population coding of complex task information

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 large neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that correlated task variables were represented by uncorrelated neural-population modes, while pairs of neurons exhibited a variety of signal correlations. This finding relates to principles of efficient coding for task-specific information, with neural-population modes as the encoding unit, and applied across posterior cortical regions and layers 2/3 and 5. Remarkably, this encoding function was multiplexed with sequential neural dynamics as well as reliably followed changes in task-variable correlations through time. We suggest that neural circuits can implement time-dependent encoding in a simple way by using random sequential dynamics as a temporal scaffold.

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bioRxiv2019

Neural Anatomy and Optical Microscopy (NAOMi) Simulation for evaluating calcium imaging methods

The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes large-scale, systematic validation impossible. Here we provide a new framework for evaluating TPM methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this access to in silico ground truth to perform direct comparisons between different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. Overall, by leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

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bioRxiv2019

A Long Timescale Stimulus History Effect in the Primary Visual Cortex

Responses of neurons in the primary visual cortex (V1) are often understood as encoding the current visual stimulus. Yet, some studies indicate that temporal contingency effects exist in the responses of neurons in early sensory areas. We explored if the recent stimulus history would alter the response of V1 layer 2/3 pyramidal cells in head-fixed awake mice during presentation of sequences of complex images. The activity of individual neurons was sparse, such that either one or none of the images in the sequence typically yielded a strong response. We then substituted an image preceding this primary image in order to determine if responses to the primary image were affected. We found that the amplitude of the neurons response could be significantly altered by substitutions up to five images back from the primary image, even when the substituted image elicited virtually no response by itself. This stimulus history effect was heterogeneous across the population, with some cells showing facilitation and others suppression. For individual cells, the history effect was robust and reproducible across days. Our data show that responses of V1 neurons not only reflect the current stimulus but also encode, through their response amplitude, information about multiple images previously presented as far as 1000 msec in the past. This might enable V1 to retain information about the extended trajectory of past stimuli and perform complex temporal computations that are as of yet not appreciated.

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