Browsing by Author "Reimer, Jacob"

Now showing 1 - 6 of 6
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    Binary and analog variation of synapses between cortical pyramidal neurons
    (eLife Sciences Publications Ltd., 2022) Dorkenwald, Sven; Turner, Nicholas L.; Macrina, Thomas; Lee, Kisuk; Lu, Ran; Wu, Jingpeng; Bodor, Agnes L.; Bleckert, Adam A.; Brittain, Derrick; Kemnitz, Nico; Silversmith, William M.; Ih, Dodam; Zung, Jonathan; Zlateski, Aleksandar; Tartavull, Ignacio; Yu, Szi-Chieh; Popovych, Sergiy; Wong, William; Castro, Manuel; Jordan, Chris S.; Wilson, Alyssa M.; Froudarakis, Emmanouil; Buchanan, JoAnn; Takeno, Marc M.; Torres, Russel; Mahalingam, Gayathri; Collman, Forrest; Schneider-Mizell, Casey M.; Bumbarger, Daniel J.; Li, Yang; Becker, Lynne; Suckow, Shelby; Reimer, Jacob; Tolias, Andreas S.; Macarico da Costa, Nuno; Reid, R. Clay; Seung, H. Sebastian
    Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
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    Distinct organization of two cortico-cortical feedback pathways
    (Springer Nature, 2022) Shen, Shan; Jiang, Xiaolong; Scala, Federico; Fu, Jiakun; Fahey, Paul; Kobak, Dmitry; Tan, Zhenghuan; Zhou, Na; Reimer, Jacob; Sinz, Fabian; Tolias, Andreas S.
    Neocortical feedback is critical for attention, prediction, and learning. To mechanically understand its function requires deciphering its cell-type wiring. Recent studies revealed that feedback between primary motor to primary somatosensory areas in mice is disinhibitory, targeting vasoactive intestinal peptide-expressing interneurons, in addition to pyramidal cells. It is unknown whether this circuit motif represents a general cortico-cortical feedback organizing principle. Here we show that in contrast to this wiring rule, feedback between higher-order lateromedial visual area to primary visual cortex preferentially activates somatostatin-expressing interneurons. Functionally, both feedback circuits temporally sharpen feed-forward excitation eliciting a transient increase–followed by a prolonged decrease–in pyramidal cell activity under sustained feed-forward input. However, under feed-forward transient input, the primary motor to primary somatosensory cortex feedback facilitates bursting while lateromedial area to primary visual cortex feedback increases time precision. Our findings argue for multiple cortico-cortical feedback motifs implementing different dynamic non-linear operations.
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    NEURD: automated proofreading and feature extraction for connectomics
    (2023-04-21) Celii, Brendan; Pitkow, Xaq; Reimer, Jacob
    We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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    NEURD: automated proofreading and feature extraction for connectomics
    (2024-03-28) Celii, Brendan; Reimer, Jacob; Pitkow, Xaq
    We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for tasks unable to be performed manually at these scales, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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    Structure and function of axo-axonic inhibition
    (eLife Sciences Publications Ltd, 2021) Schneider-Mizell, Casey M.; Bodor, Agnes L.; Collman, Forrest; Brittain, Derrick; Bleckert, Adam; Dorkenwald, Sven; Turner, Nicholas L.; Macrina, Thomas; Lee, Kisuk; Lu, Ran; Wu, Jingpeng; Zhuang, Jun; Nandi, Anirban; Hu, Brian; Buchanan, JoAnn; Takeno, Marc M.; Torres, Russel; Mahalingam, Gayathri; Bumbarger, Daniel J.; Li, Yang; Chartrand, Thomas; Kemnitz, Nico; Silversmith, William M.; Ih, Dodam; Zung, Jonathan; Zlateski, Aleksandar; Tartavull, Ignacio; Popovych, Sergiy; Wong, William; Castro, Manuel; Jordan, Chris S.; Froudarakis, Emmanouil; Becker, Lynne; Suckow, Shelby; Reimer, Jacob; Tolias, Andreas S.; Anastassiou, Costas A.; Seung, H. Sebastian; Reid, R. Clay; Costa, Nuno Maçarico da
    Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
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    Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy
    (Elsevier, 2022) Liu, Zhuohe; Lu, Xiaoyu; Villette, Vincent; Gou, Yueyang; Colbert, Kevin L.; Lai, Shujuan; Guan, Sihui; Land, Michelle A.; Lee, Jihwan; Assefa, Tensae; Zollinger, Daniel R.; Korympidou, Maria M.; Vlasits, Anna L.; Pang, Michelle M.; Su, Sharon; Cai, Changjia; Froudarakis, Emmanouil; Zhou, Na; Patel, Saumil S.; Smith, Cameron L.; Ayon, Annick; Bizouard, Pierre; Bradley, Jonathan; Franke, Katrin; Clandinin, Thomas R.; Giovannucci, Andrea; Tolias, Andreas S.; Reimer, Jacob; Dieudonné, Stéphane; St-Pierre, François
    Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.