Connectomic reconstruction of a cortical column

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 25, 2024

ABSTRACT The cerebral cortex of mammals has long been proposed to comprise unit-modules, so-called cortical columns. detailed synaptic-level circuitry such a neuronal network about 10 4 neurons is still unknown. Here, using 3-dimensional electron microscopy, AI-based image processing and automated proofreading, we report the connectomic reconstruction defined column in mouse barrel cortex. appears as structural feature connectome, without need for geometrical or morphological landmarks. We then used connectome definition cell types column, determine intracolumnar circuit modules, analyze logic inhibitory circuits, investigate circuits combination bottom-up top-down signals specificity input, search higher-order structure within homogeneous populations, estimate degree symmetry Hebbian learning various connection types. With this, provide first column-level description cortex, likely substrate mechanistic understanding sensory-conceptual integration learning.

Language: Английский

---

Towards a Foundation Model of the Mouse Visual Cortex

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 24, 2023

The complexity of neural circuits makes it challenging to decipher the brain’s algorithms intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding computational objectives and coding. However, these struggle generalize beyond their training distribution, limiting utility. emergence foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts activity from visual cortices multiple mice model predict neuronal responses arbitrary natural videos. This generalized minimal successfully predicted across various stimulus domains, such as coherent motion noise patterns. It could also be adapted tasks prediction, predicting anatomical cell types, dendritic features, connectivity within MICrONS functional connectomics dataset. Our work is crucial step toward building models. As neuroscience accumulates larger, multi-modal will uncover statistical regularities, enabling rapid adaptation accelerating research.

Language: Английский

---

Cooperative thalamocortical circuit mechanism for sensory prediction errors

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 12, 2023

Abstract The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and outcomes our actions. Discrepancies between expected actual events, referred errors, are leveraged update guide attention towards unexpected events 1–10 . Despite importance error signals for various neural computations across multiple regions, surprisingly little is known about circuit mechanisms responsible their implementation. Here we describe thalamocortical disinhibitory required generating sensory errors in mouse primary visual cortex (V1). Using calcium imaging with optogenetic manipulations mice traverse familiar virtual environment, show that violation animals’ predictions by stimulus preferentially boosts responses layer 2/3 V1 neurons most selective stimulus. Prediction specifically amplify input, rather than representing non-specific surprise or difference signal how input deviates from predictions. Selective amplification implemented cooperative mechanism requiring thalamic pulvinar, cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response VIP inhibit specific subpopulation somatostatin-expressing (SOM) interneurons gate excitatory pulvinar V1, resulting pulvinar-driven response-amplification stimulus-selective V1. Therefore, prioritizes unpredicted information selectively increasing salience features through synergistic interaction neocortical circuits.

Language: Английский

---

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 26, 2024

ABSTRACT Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed correlating structural metrics to functional But prove causation, manipula- tions of would have studied. However, fine-grained scale which trends are expressed makes this approach challenging pursue experimentally. Simulations networks provide an alternative route study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome large- network models SONATA format. In addition creating or manipulating model, it provides tools fit parameters stochastic against existing connectomes. This enables replacement any with equivalent connectomes different levels complexity, transplantation features from one another, systematic study. We employed model rat somatosensory cortex two exemplary use cases: transplanting interneuron electron microscopy data simplified excitatory connectivity. ran series simulations found diverse shifts activity individual neuron populations causally linked these manipulations.

Language: Английский

---

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

Published: Aug. 12, 2024

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local, mid-range extrinsic delineated limits determining connectivity from neuron morphology placement, finding that it reproduces targeting Sst+ requires additional specificity to reproduce PV+ VIP+ interneurons. Globally, was characterized local clusters tied together through hub neurons layer 5, demonstrating how interegional complicit, inseparable networks. suitable for simulation-based studies, 211,712 subvolume made openly available community.

Language: Английский

---

Transcriptomic cell-type specificity of local cortical circuits

Neuron, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity connections at level transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic single-nuclei RNA sequencing to identify cell types, providing inputs defined neuron populations. employ START transcriptomically characterize neurons input 5 different layer-specific cortical populations in mouse primary visual cortex (V1). At subclass level, we observe results consistent with findings from prior studies that resolve using antibody staining, transgenic lines, morphological reconstruction. With improved subtype granularity achieved START, demonstrate various subclasses. These establish resolution types.

Language: Английский

---

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

Published: Nov. 26, 2024

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local, mid-range extrinsic delineated limits determining connectivity from neuron morphology placement, finding that it reproduces targeting Sst+ requires additional specificity to reproduce PV+ VIP+ interneurons. Globally, was characterized local clusters tied together through hub neurons layer 5, demonstrating how interegional complicit, inseparable networks. suitable for simulation-based studies, 211,712 subvolume made openly available community.

Language: Английский

---

Perisomatic Features Enable Efficient and Dataset Wide Cell-Type Classifications Across Large-Scale Electron Microscopy Volumes

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: July 22, 2022

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using variety molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation biological processes underlying type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination an unbiased perspective into subcellular organization brain cells, including their synaptic connectivity nanometer morphology. It also presents clear challenge for analysis to identify cell-types in data that tens thousands neurons, most which incomplete reconstructions. To address this challenge, we present first systematic survey somatic region all cells within cubic millimeter cortex quantitative features obtained from EM. This demonstrates surprising sufficiency perisomatic cell-types, defined primarily based patterns. We then describe how classification facilitates specific characterization locating with rare patterns dataset.

Language: Английский

---

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 17, 2023

Summary Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo . Large-scale, biophysically detailed models offer a tool can complement laboratory approaches. We present model comprising eight somatosensory cortex subregions, 4.2 million morphological electrically-detailed neurons, 13.2 billion local mid-range synapses. In silico tools enabled reproduction extension of experiments under single parameterization, providing strong validation. The reproduced millisecond-precise stimulus-responses, stimulus-encoding targeted optogenetic activation, selective propagation stimulus-evoked activity downstream areas. model’s direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, cortical is shaped by high-dimensional connectivity motifs connectivity, spatial targeting rules inhibitory subpopulations. latter was facilitated using rewired connectome included specific observed different neuron types electron microscopy. also predicted the role interneuron layers stimulus encoding. Simulation large subvolume made available enable further community-driven improvement, validation investigation.

Language: Английский

---

NEURD offers automated proofreading and feature extraction for connectomics

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 15, 2023

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium 2021). Dense reconstruction cellular compartments these EM has been enabled by recent advances Machine Learning (ML) (Lee 2017; Wu Lu Macrina Automated segmentation methods produce exceptionally accurate reconstructions cells, but post-hoc proofreading is still required to generate large connectomes free merge and split errors. The elaborate 3-D meshes neurons contain detailed morphological information multiple scales, from diameter, shape, branching patterns axons dendrites, down fine-scale structure dendritic spines. However, extracting features can require substantial effort piece together existing tools into custom workflows. Building on open-source software for mesh manipulation, here we present “NEURD”, a package that decomposes meshed compact extensively-annotated graph representations. With feature-rich graphs, automate variety tasks such as state art automated errors, cell classification, spine detection, axon-dendritic proximities, other annotations. These enable many downstream analyses neural morphology connectivity, making massive complex datasets more accessible neuroscience researchers focused scientific questions.

Language: Английский

---