Biology subjects

Tank, D. W.

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

6 recordsLinked to original sources

bioRxiv2018

Visual cue-related activity of cells in the medial entorhinal cortex during navigation in virtual reality

During spatial navigation, animals use self-motion to estimate positions through path integration. However, estimation errors accumulate over time and it is unclear how they are corrected. Here we report a new cell class (\"cue cell\") in mouse medial entorhinal cortex (MEC) that encoded visual cue information that could be used to correct errors in path integration. Cue cells accounted for a large fraction of unidentified MEC cells. They exhibited firing fields only near visual cues during virtual navigation and spatially stable activity during navigation in a real arena. Cue cells responses occurred in sequences repeated at each cue and were likely driven by visual inputs. In layers 2/3 of the MEC, cue cells formed clusters. Anatomically adjacent cue cells responded similarly to cues. These cue cell properties demonstrate that the MEC circuits contain a code representing spatial landmarks that could play a significant role in error correction during path integration.

neuroscience↗

bioRxiv2018

Probing variability in a cognitive map using manifold inference from neural dynamics

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task,1-3 and are thought to form a cognitive map of task-relevant variables.1,4,5 However, their activity varies over repeated behavioral conditions,6 such as different runs through the same position or repeated trials. Although widely observed across the brain,7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information.6,11-13 Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold--a space of possible network states. The manifolds structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory,1,5,14 planning,12,15,16 and reinforcement learning.17,18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

neuroscience↗

bioRxiv2018

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

neuroscience↗

bioRxiv2017

An accumulation-of-evidence task using visual pulses for mice navigating in virtual reality

The gradual accumulation of sensory evidence is a crucial component of perceptual decision making, but its neural mechanisms are still poorly understood. Given the wide availability of genetic and optical tools for mice, they can be useful model organisms for the study of these phenomena; however, behavioral tools are largely lacking. Here, we describe a new evidence-accumulation task for head-fixed mice navigating in a virtual reality environment. As they navigate down the stem of a virtual T-maze, they see brief pulses of visual evidence on either side, and retrieve a reward on the arm with the highest number of pulses. The pulses occur randomly with Poisson statistics, yielding a diverse yet well-controlled stimulus set, making the data conducive to a variety of computational approaches. A large number of mice of different genotypes were able to learn and consistently perform the task, at levels similar to rats in analogous tasks. They are sensitive to side differences of a single pulse, and their memory of the cues is stable over time. Moreover, using non-parametric as well as modeling approaches, we show that the mice indeed accumulate evidence: they use multiple pulses of evidence from throughout the cue region of the maze to make their decision, albeit with a small overweighting of earlier cues, and their performance is affected by the magnitude but not the duration of evidence. Additionally, analysis of the mice's running patterns revealed that trajectories are fairly stereotyped yet modulated by the amount of sensory evidence, suggesting that the navigational component of this task may provide a continuous readout correlated to the underlying cognitive variables. Our task, which can be readily integrated with state-of-the-art techniques, is thus a valuable tool to study the circuit mechanisms and dynamics underlying perceptual decision making, particularly under more complex behavioral contexts.

neuroscience↗

bioRxiv2017

Context-invariant encoding of reward location in a distinct hippocampal population

The hippocampus plays a critical role in goal-directed navigation. Across different environments, however, the hippocampal map seems to be randomized, making it unclear how goal locations might be encoded. To address this question, we trained mice to seek reward at several locations on virtual linear tracks, and optical recordings of calcium activity were made at cellular resolution from two major hippocampal output structures, CA1 and the subiculum. These experiments revealed a population of neurons that were consistently active near reward locations. Their pattern of activity sharply contrasted with simultaneously-recorded place cells and even persisted across environments, when other cells remapped randomly. Their timing was closely correlated with reward anticipation behaviors, yet could not be explained by the behaviors themselves, raising the possibility that reward-associated cell signals were used by mice to identify the reward location. These results demonstrate that the hippocampus employs a context-invariant marker of goal locations, and reveal a novel target for studying how the hippocampus contributes to navigation.

neuroscience↗

bioRxiv2017

Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex

To explore theories of predictive coding, we presented mice with repeated sequences of images with novel images sparsely substituted. Under these conditions, mice could be rapidly trained to lick in response to a novel image, demonstrating a high level of performance on the first day of testing. Using 2-photon calcium imaging to record from layer 2/3 neurons in the primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. When a new stimulus sequence was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which then decayed to almost zero activity. The decay time of these transient responses was not fixed, but instead scaled with the length of the stimulus sequence. However, at the same time, we also found a small fraction of the neurons within the population ([~]2%) that continued to respond strongly and periodically to the repeated stimulus. Decoding analysis demonstrated that both the transient and sustained responses encoded information about stimulus identity. We conclude that the layer 2/3 population uses a two-channel predictive code: a dense transient code for novel stimuli and a sparse sustained code for familiar stimuli. These results extend and unify existing theories about the nature of predictive neural codes.

neuroscience↗