Biology subjects

da Costa, N. M.

Publications and source records attributed to da Costa, N. M..

8 recordsLinked to original sources

bioRxiv2026

A quantitative census of millions of postsynaptic structures in a large electron microscopy volume of mouse visual cortex

Neurons display remarkable sub-cellular specificity in their synaptic targeting, which varies by cell type--for example, excitatory neurons prefer to target the spines of other excitatory cells. Modern electron microscopy connectomes enable the study of this sub-cellular specificity and its context in a circuit at unprecedented scale and resolution. However, this scale has also made it challenging to create accurate and efficient methods for classifying and segmenting fine cell components (including spines) across entire volumes. Here, we present a cost-efficient computational pipeline for classifying postsynaptic targets and segmenting structures such as spines. Our method relies only on having a mesh representation of a neuron and avoids processing image or segmentation data directly. Instead, we leverage tools from geometry processing to create features capturing the local geometry of a neurons surface. We couple this core technique with computational and storage optimizations, enabling reliable deployment over hundreds of thousands of neurons for a few hundred dollars in cloud compute cost. We then show that a simple classifier trained on the MICrONS mouse visual cortex dataset can use these mesh-based features to accurately classify synapses as targeting somas, dendritic shafts, or spines (weighted F1 score 0.961). Using this pipeline, we create a map of the postsynaptic structures at over 207.3 million synapses in MICrONS. We present an overview of this census of postsynaptic targeting, finding expected patterns (e.g., excitatory neurons preferentially targeting excitatory spines) as well as unexpected exceptions (e.g., Layer 5 near-projecting and Layer 6 corticothalamic cells often connecting to excitatory neuron shafts). We also demonstrate that these tools can be used to detect spines receiving multiple synaptic inputs, revealing surprising variability in their frequency across cells even within a cell type. We make our postsynaptic target predictions available for study, as well as the code for the computational pipeline and cloud deployment. Beyond MICrONS, we find that the model generalizes well to the H01 connectome without retraining (weighted F1 score 0.949), indicating that these tools will be useful in future connectomics reconstructions. More generally, our work demonstrates that representations derived from neuronal meshes can be a scalable and generalizable primitive for describing morphologies.

neuroscience↗

bioRxiv2025

Hebb's Vision: The Structural Underpinnings of Hebbian Assemblies

1In 1949, Donald Hebb proposed that groups of neurons that activate stereotypically form the organizational building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging, due to a lack of large-scale structure-activity maps. Here, we analyze this relation using a novel dataset that links in vivo optical physiology to connectivity using postmortem elec-tron microscopy (EM). From the fluorescence traces, we extract neural assemblies from higher-order correlations in neural activity. Physiologically, we show that these assemblies exhibit properties consistent with Hebbs theory, including more reliable responses to repeated natural movie inputs than size-matched random ensembles and superior decoding of visual stimuli. Structurally, we find that neurons that participate in assemblies are significantly more integrated into the structural network than those that do not. Contrary to Hebbs original prediction, we do not observe a marked increase in the strength of monosynaptic excitatory connections between cells participating in the same assembly. However, we find significantly stronger indirect feed-forward inhibitory connections targeting cells in other assemblies. These results show that assemblies can be useful components of perception, and, surprisingly, they are delineated by mutual inhibition.

neuroscience↗

bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗

bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2019

Binary and analog variation of synapses between cortical pyramidal neurons

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 (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al. 2007) by a log-normal distribution (Loewenstein, Kuras, and Rumpel 2011; de Vivo et al. 2017; Santuy et al. 2018). 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 (Sorra and Harris 1993; Koester and Johnston 2005; Bartol et al. 2015; Kasthuri et al. 2015; Dvorkin and Ziv 2016; Bloss et al. 2018; Motta et al. 2019). 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. We discuss the implications for the stability-plasticity dilemma.

neuroscience↗

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗

bioRxiv2017

A weighted graph of the projections to mouse auditory cortex

The projections to individual cortical areas from extrinsic sources are a major determinant of the areas function, but we lack comprehensive quantitative input maps even for primary sensory areas in most model species. To quantify all input sources to the mouse primary auditory cortex (Au1), we made localized injections of modified rabies virus (SAD{Delta}G-mCherry) into Au1 of five C57BL/6 mice and identified all the cortical and subcortical areas containing retrogradely labeled cells. Of all neurons projecting to Au1 from extrinsic areas, 27 % were located in the ipsilateral cortex, 14 % in the contralateral cortex, and 58 % in subcortical regions (almost exclusively ipsilateral, predominantly in the medial geniculate nucleus). Although 90 % of the labeled cells in the ipsilateral cortex were located within 1 mm of Au1, most cortical areas projected to Au1, including visual, somatosensory, motor, rhinal, cingulate and piriform cortices. The hierarchical relations of the cortical areas projecting to Au1 were determined based on the proportion of cell bodies in superficial versus deep layers. Feedback projections (from deep layers 5/6) dominated, but temporal association and auditory cortices were on the same hierarchical level, providing input from both superficial and deep layers. Au1 is embedded in a densely connected network that involves a high degree of cross-modal integration.

neuroscience↗

bioRxiv2016

Fast Assembling of Neuron Fragments in Serial 3D Sections

AbstractReconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.

neuroscience↗