@article {67287, title = {Neural Correlates of Cognition in Primary Visual versus Neighboring Posterior Cortices during Visual Evidence-Accumulation-based Navigation}, journal = {bioRxiv}, year = {2019}, month = {03/2019}, abstract = {

Studies of perceptual decision-making have often assumed that the main role of sensory cortices is to provide sensory input to downstream processes that accumulate and drive behavioral decisions. We performed a systematic comparison of neural activity in primary visual (V1) to secondary visual and retrosplenial cortices, as mice performed a task where they should accumulate pulsatile visual cues through time to inform a navigational decision. Even in V1, only a small fraction of neurons had sensory-like responses to cues. Instead, in all areas neurons were sequentially active, and contained information ranging from sensory to cognitive, including cue timings, evidence, place/time, decision and reward outcome. Per-cue sensory responses were amplitude-modulated by various cognitive quantities, notably accumulated evidence. This inspired a multiplicative feedback-loop circuit hypothesis that proposes a more intricate role of sensory areas in the accumulation process, and furthermore explains a surprising observation that perceptual discrimination deviates from Weber-Fechner Law.Highlights / eTOC BlurbMice made navigational decisions based on accumulating pulsatile visual cuesThe bulk of neural activity in visual cortices was sequential and beyond-sensoryAccumulated pulse-counts modulated sensory (cue) responses, suggesting feedbackA feedback-loop neural circuit explains behavioral deviations from Weber{\textquoteright}s LawHighlights / eTOC BlurbIn a task where navigation was informed by accumulated pulsatile visual evidence, neural activity in visual cortices predominantly coded for cognitive variables across multiple timescales, including outside of a visual processing context. Even sensory responses to visual pulses were amplitude-modulated by accumulated pulse counts and other variables, inspiring a multiplicative feedback-loop circuit hypothesis that in turn explained behavioral deviations from Weber-Fechner Law.

}, doi = {10.1101/568766}, url = {https://www.biorxiv.org/content/early/2019/03/06/568766}, author = {Koay, Sue Ann and Thiberge, Stephan Y. and Brody, Carlos D. and Tank, David W.} } @article {67290, title = {Sequential and efficient neural-population coding of complex task information}, journal = {bioRxiv}, year = {2019}, month = {10/2019}, abstract = {

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 modes in an information-coding subspace. We show via theory that this can enable optimal decoding directions to be insensitive to neural noise levels. Across posterior cortex, principles of efficient coding thus applied to task-specific information, with neural-population modes as the encoding unit. Remarkably, this encoding function was multiplexed with rapidly changing, sequential neural dynamics, yet reliably followed slow changes in task-variable correlations through time. We can explain this as due to a mathematical property of high-dimensional spaces that the brain might exploit as a temporal scaffold.

}, doi = {10.1101/801654}, url = {https://www.biorxiv.org/content/early/2019/10/13/801654}, author = {Koay, Sue Ann and Thiberge, Stephan Y. and Brody, Carlos D. and Tank, David W.} } @article {67281, title = {Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons}, journal = {Nature}, volume = {570}, year = {2019}, month = {05/2019}, pages = {509 - 513}, abstract = {

There is increased appreciation that dopamine neurons in the midbrain respond not only to reward1\ and reward-predicting cues1,2, but also to other variables such as the distance to reward3, movements4,5,6,7,8,9\ and behavioural choices10,11. An important question is how the responses to these diverse variables are organized across the population of dopamine neurons. Whether individual dopamine neurons multiplex several variables, or whether there are subsets of neurons that are specialized in encoding specific behavioural variables remains unclear. This fundamental question has been difficult to resolve because recordings from large populations of individual dopamine neurons have not been performed in a behavioural task with sufficient complexity to examine these diverse variables simultaneously. Here, to address this gap, we used two-photon calcium imaging through an implanted lens to record the activity of more than 300 dopamine neurons from the ventral tegmental area of the mouse midbrain during a complex decision-making task. As mice navigated in a virtual-reality environment, dopamine neurons encoded an array of sensory, motor and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioural variables, in addition to encoding reward. These functional clusters were spatially organized, with neighbouring neurons more likely to be part of the same cluster. Together with the topography between dopamine neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by dopamine neurons.

}, issn = {0028-0836}, doi = {10.1038/s41586-019-1261-9}, url = {http://www.nature.com/articles/s41586-019-1261-9}, author = {Engelhard, Ben and Finkelstein, Joel and Cox, Julia and Fleming, Weston and Jang, Hee Jae and Ornelas, Sharon and Koay, Sue Ann and Thiberge, Stephan Y. and Daw, Nathaniel D. and Tank, David W. and Witten, Ilana B.} } @article {67283, title = {An Accumulation-of-Evidence Task Using Visual Pulses for Mice Navigating in Virtual Reality}, journal = {Frontiers in Behavioral Neuroscience}, volume = {12}, year = {2018}, month = {Jun-03-2018}, abstract = {

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 (VR) 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{\textquoteright}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.

}, doi = {10.3389/fnbeh.2018.00036}, url = {http://journal.frontiersin.org/article/10.3389/fnbeh.2018.00036/full}, author = {Pinto, Lucas and Koay, Sue A. and Engelhard, Ben and Yoon, Alice M. and Deverett, Ben and Thiberge, Stephan Y. and Witten, Ilana B. and Tank, David W. and Brody, Carlos D.} } @article {67289, title = {CalmAn: An open source tool for scalable Calcium Imaging data Analysis}, journal = {bioRxiv}, year = {2018}, month = {06/2018}, abstract = {

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.

}, doi = {10.1101/339564}, url = {https://www.biorxiv.org/content/early/2018/06/05/339564}, author = {Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, J{\'e}r{\'e}mie and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L. and Zhou, Pengcheng and Tank, David W. and Chklovskii, Dmitri and Pnevmatikakis, Eftychios A.} } @article {67291, title = {Detecting and Correcting False Transients in Calcium Imaging}, journal = {bioRxiv}, year = {2018}, month = {11/2018}, abstract = {

Population recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.

}, doi = {10.1101/473470}, url = {https://www.biorxiv.org/content/early/2018/11/19/473470}, author = {Gauthier, Jeff L. and Koay, Sue Ann and Nieh, Edward H. and Tank, David W. and Pillow, Jonathan W. and Charles, Adam S.} } @article {67288, title = {Specialized and spatially organized coding of sensory, motor, and cognitive variables in midbrain dopamine neurons}, journal = {bioRxiv}, year = {2018}, month = {10/2018}, abstract = {

There is increased appreciation that dopamine (DA) neurons in the midbrain respond not only to reward 1,2 and reward-predicting cues 1,3,4, but also to other variables such as distance to reward 5, movements 6{\textendash}11 and behavioral choices 12{\textendash}15. Based on these findings, a major open question is how the responses to these diverse variables are organized across the population of DA neurons. In other words, do individual DA neurons multiplex multiple variables, or are subsets of neurons specialized in encoding specific behavioral variables? The reason that this fundamental question has been difficult to resolve is that recordings from large populations of individual DA neurons have not been performed in a behavioral task with sufficient complexity to examine these diverse variables simultaneously. To address this gap, we used 2-photon calcium imaging through an implanted lens to record activity of \>300 midbrain DA neurons in the VTA during a complex decision-making task. As mice navigated in a virtual reality (VR) environment, DA neurons encoded an array of sensory, motor, and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioral variables, in addition to encoding reward. These functional clusters were spatially organized, such that neighboring neurons were more likely to be part of the same cluster. Taken together with the topography between DA neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by DA neurons.

}, doi = {10.1101/456194}, url = {https://www.biorxiv.org/content/early/2018/10/29/456194}, author = {Engelhard, Ben and Finkelstein, Joel and Cox, Julia and Fleming, Weston and Jang, Hee Jae and Ornelas, Sharon and Koay, Sue Ann and Thiberge, Stephan and Daw, Nathaniel and Tank, David W. and Witten, Ilana B.} } @article {67286, title = {Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex}, journal = {bioRxiv}, year = {2017}, month = {10/2017}, abstract = {

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.

}, doi = {10.1101/197608}, url = {https://www.biorxiv.org/content/early/2017/10/03/197608}, author = {Homann, Jan and Koay, Sue Ann and Glidden, Alistair M. and Tank, David W. and Berry, Michael J.} } @conference {67293, title = {Stochastic filtering of two-photon imaging using reweighted l1}, booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2017}, month = {03/2017}, publisher = {IEEE}, organization = {IEEE}, address = {New Orleans, LA}, abstract = {
Two-photon (TP) calcium imaging is an important imaging modality in neuroscience, allowing for large-scale recording of neural activity in awake, behaving animals at behavior-relevant timescales. Interpretation of TP data requires the accurate extraction of temporal neural activity traces, which can be accomplished via manual or automated methods. In this work we seek to improve the accuracy of both manual and automated TP microscopy demixing methods by introducing a denoising algorithm based on a statistical model of TP data which includes spatial contiguity, sparse activity and Poisson observations. Our method leverages recent developments in stochastic filtering of structured signals based on Laplacian-scale mixture models (LSMs) to model the neural activity in TP data as a set of spatially correlated sparse variables. We apply our method on TP images taken from the visual cortex of an awake, behaving mouse, and demonstrate improved neural activity demixing over current pre-processing techniques.
}, doi = {10.1109/ICASSP.2017.7952314}, url = {https://ieeexplore.ieee.org/abstract/document/7952314}, author = {Charles, Adam S. and Song, Alexander and Koay, Sue Ann and Tank, David W. and Pillow, Jonathan W.} }