Cortical cell assemblies and their underlying connectivity: An in silico study

PLoS Computational Biology, Год журнала: 2024, Номер 20(3), С. e1011891 - e1011891

Опубликована: Март 11, 2024

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study functional cell assemblies. However, determining patterns synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using detailed, large-scale cortical network model. Using combination established methods detected stimulus-evoked spiking activity 186,665 neurons. We studied how structure underlies assembly composition, quantifying effects thalamic innervation, recurrent connectivity, and spatial arrangement synapses on dendrites. determined that features reduce up 30%, 22%, 10% uncertainty neuron belonging an assembly. The were activated stimulus-specific sequence grouped based their position sequence. found different groups affected degrees by structural considered. Additionally, was more predictive membership if its direction aligned with temporal order activation, it originated strongly interconnected populations, clustered dendritic branches. In summary, reversing Hebb’s postulate, showed cells are wired together, fire interact shape emergence This includes qualitative aspect connectivity: not just amount, but also local matters; subcellular level form clustering presence specific motifs.

Язык: Английский

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BlueRecording: A pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 14, 2024

Abstract As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed permit identification biological origins different components signals, evaluation signal sensitivity anatomical, physiological, geometric factors, selection recording parameters maximize information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics neuro-simulation validation. To enable efficient calculation from large neural simulations, we have developed BlueRecording , a pipeline consisting standalone Python code, along with extensions Neurodamus simulation control application, CoreNEURON computation engine, SONATA data format, online such In particular, implement general form reciprocity theorem, which is capable handling non-dipolar current sources, as be found in long axons recordings close source, well complex tissue anatomy, dielectric heterogeneity, electrode geometries. our knowledge, this first application generalized (i.e., non-dipolar) reciprocity-based approach simulate EEG recordings. We use tools calculate an silico model rat somatosensory cortex hippocampus study contribution differences between regions cell types.

Язык: Английский

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Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations

Imaging Neuroscience, Год журнала: 2024, Номер 2, С. 1 - 20

Опубликована: Янв. 1, 2024

Abstract Digital brain atlases define a hierarchy of regions and their locations in three-dimensional Cartesian space, providing standard coordinate system which diverse datasets can be integrated for visualization analysis. Although this has well-defined anatomical axes, it does not provide the best description complex geometries layered such as neocortex. As better alternative, we propose laminar systems that consider curvature structure region interest. These consist principal axis aligned to local vertical direction measuring depth, two other axes describe flatmap, two-dimensional representation horizontal extents layers. The main property flatmaps is they allow seamless mapping between 2D 3D spaces through structured dimensionality reduction where information aggregated along depth. We introduce general method based on digital according user specifications. complemented by set metrics characterize quality resulting flatmaps. applied our rodent atlases. First, an atlas rat somatosensory cortex Paxinos Watson’s atlas, enhancing with adapted geometry region. Second, Allen Mouse Brain Atlas Common Coordinate Framework version 3, whole isocortex. used one these new annotations 33 individual barrels barrel columns are nonoverlapping follow cortex, therefore, producing most accurate mouse date. Additionally, introduced several applications highlighting utility data data-driven modeling. free software implementation methods benefit community.

Язык: Английский

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Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Май 17, 2023

Abstract The CA1 region of the hippocampus is one most studied regions rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth experimental data on its structure function, it has been challenging reconcile information obtained from diverse approaches. To address this challenge, we present community-driven, full-scale silico model rat that integrates broad range data, synapse network, including reconstruction principal afferents, Schaffer collaterals, effects acetylcholine system. We tested validated each component final network model, made input assumptions, strategies explicit transparent. unique flexibility allows scientists scientific questions. In article, describe methods used set up simulations reproduce extend vitro vivo experiments. Among several applications focus theta rhythm, prominent hippocampal oscillation associated with various behavioral correlates use our computer findings. Finally, make code available through hippocampushub.eu portal, which also provides extensive analyses user-friendly interface facilitate adoption usage. This neuroscience community-driven represents valuable tool for integrating foundation further research into complex workings region.

Язык: Английский

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Specific inhibition and disinhibition in the higher-order structure of a cortical connectome

Cerebral Cortex, Год журнала: 2024, Номер 34(11)

Опубликована: Ноя. 1, 2024

Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced blanket no targeting specificity. We analyzed the structure excitation and in MICrONS $mm^{3}$ dataset, an electron microscopic reconstruction a piece cortical tissue. found that was structured around feed-forward flow large non-random neuron motifs information from small number sources larger potential targets. Inhibitory neurons connected specific sequential positions these motifs, implementing targeted symmetrical competition between them. None trends detectable only pairwise connectivity, demonstrating by motifs. While descriptions circuits range non-specific blanket-inhibition targeted, our results describe form specificity existing higher-order connectome. These findings have important implications for role learning synaptic plasticity.

Язык: Английский

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Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Авг. 26, 2023

Abstract Digital brain atlases define a hierarchy of regions and their locations in three-dimensional Cartesian space. They provide standard coordinate system which diverse datasets can be integrated for visualization analysis. Although this has well-defined anatomical axes, it does not the best context to work with complex geometries layered such as neocortex. To address that, we introduce laminar systems that consider curvature structure region interest. These new consist principal axis, locally aligned vertical direction measuring depth, two other axes describe flatmap, two-dimensional representation horizontal extents layers. The main property flatmap is allows seamless mapping information back forth between 2D 3D spaces, way consistent axis. It involves structured dimensionality reduction where aggregated along depth. We propose method enhance flatmaps based on user specifications set metrics characterize quality flatmaps. applied our an atlas rat somatosensory cortex Paxinos Watson’s atlas, enhancing adapted geometry region. Further, Allen Mouse Brain Atlas Common Coordinate Framework version 3 whole isocortex. used produce annotations 33 individual barrels barrel columns cortex. Thanks properties resulting are non-overlapping follow Additionally, introduced several applications highlighting utility data data-driven modeling. free software implementation methods benefit community.

Язык: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Авг. 15, 2022

Abstract 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.

Язык: Английский

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Specific inhibition and disinhibition in the higher-order structure of a cortical connectome

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 22, 2023

ABSTRACT Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced blanket no targeting specificity. We analyzed the structure excitation and in MICrONS mm 3 dataset, an electron microscopic reconstruction a piece cortical tissue. found that was structured around feed-forward flow large non-random neuron motifs information from small number sources larger potential targets. Inhibitory neurons connected specific sequential positions these motifs, implementing targeted symmetrical competition between them. None trends detectable only pairwise connectivity, demonstrating by motifs. While descriptions circuits range non-specific blanket-inhibition targeted, our results describe form specificity existing higher-order connectome. These findings have important implications for role learning synaptic plasticity.

Язык: Английский

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Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 21, 2024

Abstract Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify parameters detailed biophysical such that they match physiological measurements at scale or perform computational tasks. Here, we describe framework for simulation in neuroscience—J axley —which addresses this challenge. By making use automatic differentiation and GPU acceleration, J opens up possibility efficiently optimize large-scale with gradient descent. We show can learn several hundreds voltage two photon calcium recordings, sometimes orders magnitude more than previous methods. then demonstrate makes it possible train recurrent network working memory tasks, feedforward morphologically neurons 100,000 solve computer vision task. Our analyses dramatically improves ability build data- task-constrained models, creating unprecedented opportunities investigating computations across multiple scales.

Язык: Английский

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A biophysically-detailed model of inter-areal interactions in cortical sensory processing

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 13, 2024

Abstract Mechanisms of top-down modulation in sensory perception and their relation to underlying connectivity are not completely understood. We present here a biophysically-detailed computational model two interconnected cortical areas, representing the first steps processing hierarchy, as tool for potential discovery. The integrates large body data from rodent primary somatosensory cortex reproduces biological features across multiple scales: handful ion channels defining diversity electrical types hundreds thousands morphologically detailed neurons, local long-range networks mediated by millions synapses. Notably, incorporates target lamination patterns associated with feed-forward feedback pathways. use study impact inter-areal interactions on processing. First, we exhibit cortico-cortical loop between areas (X Y), wherein input area X produces response components time, driven stimulus second Y. perform structural functional characterization this loop, finding differential layer-specific pathways directions. Second, explore discrimination presenting four different spatially-segregate patterns. observe well-defined temporal sequences cell assembly activation, specificity early but late assemblies X, i.e., stimulus-driven component feedback-driven component. also find earliest Y be specific pairs patterns, consistent topography connections. Finally, examine integration bottom-up signals. When coincident component, an approximate linear superposition responses. implied lack interaction naive absence plasticity mechanisms that would underlie learning influences. This work represents step simulations.

Язык: Английский

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