How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model

Annual Review of Neuroscience, Journal Year: 2021, Volume and Issue: 44(1), P. 517 - 546

Published: April 29, 2021

The mouse, as a model organism to study the brain, gives us unprecedented experimental access mammalian cerebral cortex. By determining cortex's cellular composition, revealing interaction between its different components, and systematically perturbing these we are obtaining mechanistic insight into some of most basic properties cortical function. In this review, describe recent advances in our understanding how circuits neurons implement computations, revealed by mouse primary visual Further, discuss studying has broadened range computations performed Finally, address future approaches will fulfill promise elucidating fundamental operations

Language: Английский

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Scaling of sensory information in large neural populations shows signatures of information-limiting correlations

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Jan. 20, 2021

Abstract How is information distributed across large neuronal populations within a given brain area? Information may be roughly evenly populations, so that total scales linearly with the number of recorded neurons. Alternatively, neural code might highly redundant, meaning saturates. Here we investigate how sensory about direction moving visual stimulus hundreds simultaneously neurons in mouse primary cortex. We show sublinearly due to correlated noise these populations. compartmentalized correlations into information-limiting and nonlimiting components, then extrapolate predict grows even larger tens thousands encode 95% direction, much less than These findings suggest uses widely distributed, but nonetheless redundant supports recovering most from smaller subpopulations.

Language: Английский

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Long-term stability of cortical ensembles

eLife, Journal Year: 2021, Volume and Issue: 10

Published: July 30, 2021

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories perception, but it is still unknown how stable or flexible they over time. We used two-photon multiplane calcium imaging track weeks the activity same pyramidal layer 2/3 visual cortex from awake mice recorded their visually responses. Less than half remained active across any two sessions. These formed ensembles that lasted weeks, some were also transient appeared only one single session. Stable preserved most for up 46 days, our longest imaged period, these 'core' cells had stronger functional connectivity. Our results demonstrate neuronal can last could, principle, serve as a substrate long-lasting representation perceptual states memories.

Language: Английский

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Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Neuron, Journal Year: 2022, Volume and Issue: 110(19), P. 3064 - 3075

Published: July 20, 2022

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances technology, allowing large-scale neural recordings awake and behaving animal, have transformed our understanding activity. Studies using these discovered high-dimensional patterns, correlation between behavior, dissimilarity sensory-driven patterns. These findings supported by evidence from developing animals, where a transition toward characteristics is observed as circuit matures, well mature animals across species. newly revealed call for formulation new computation.

Language: Английский

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Social state gates vision using three circuit mechanisms inDrosophila

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

Published: March 17, 2024

Abstract Animals are often bombarded with visual information and must prioritize specific features based on their current needs. The neuronal circuits that detect relay have been well-studied. Yet, much less is known about how an animal adjusts its attention as goals or environmental conditions change. During social behaviors, flies need to focus nearby flies. Here, we study the flow of altered when female Drosophila enter aggressive state. From connectome, identified three state-dependent circuit motifs poised selectively amplify response fly-sized objects: convergence excitatory inputs from neurons conveying select internal state; dendritic disinhibition feature detectors; a switch toggles between two detectors. Using cell-type-specific genetic tools, together behavioral neurophysiological analyses, show each these function during aggression. We reveal this same operate in males courtship pursuit, suggesting disparate behaviors may share mechanisms. Our work provides compelling example using connectome infer mechanisms underlie dynamic processing sensory signals.

Language: Английский

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Long-term intravital subcellular imaging with confocal scanning light-field microscopy

Nature Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: May 27, 2024

Abstract Long-term observation of subcellular dynamics in living organisms is limited by background fluorescence originating from tissue scattering or dense labeling. Existing confocal approaches face an inevitable tradeoff among parallelization, resolution and phototoxicity. Here we present scanning light-field microscopy (csLFM), which integrates axially elongated line-confocal illumination with the rolling shutter (sLFM). csLFM enables high-fidelity, high-speed, three-dimensional (3D) imaging at near-diffraction-limit both optical sectioning low By simultaneous 3D excitation detection, intensity can be reduced below 1 mW mm − 2 , 15-fold higher signal-to-background ratio over sLFM. We imaged 25,000 timeframes optically challenging environments different species, such as migrasome delivery mouse spleen, retractosome generation liver voltage Drosophila . Moreover, facilitates large-scale neural recording crosstalk, leading to high orientation selectivity visual stimuli, similar two-photon microscopy, aids understanding coding mechanisms.

Language: Английский

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Rastermap: a discovery method for neural population recordings

Nature Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Abstract Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time noticing patterns activity related ongoing stimuli or behaviors. With the advent large-scale recordings, such close observation data become difficult. To find neural data, we developed ‘Rastermap’, a visualization method that displays neurons as raster plot after sorting them along one-dimensional axis based on their patterns. We benchmarked Rastermap realistic simulations then used it explore recordings tens thousands from mouse cortex during spontaneous, stimulus-evoked task-evoked epochs. also applied whole-brain zebrafish recordings; wide-field imaging data; electrophysiological rat hippocampus, monkey frontal various cortical subcortical regions mice; artificial networks. Finally, illustrate high-dimensional scenarios where similar algorithms cannot be effectively.

Language: Английский

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Functional connectomics spanning multiple areas of mouse visual cortex

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown

Published: July 29, 2021

Abstract To understand the brain we must relate neurons’ functional responses to circuit architecture that shapes them. Here, present a large connectomics dataset with dense calcium imaging of millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher areas (VISrl, VISal VISlm) an awake mouse viewing natural movies synthetic stimuli. The data were co-registered volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells 0.5 billion synapses. Subsequent proofreading subset this volume yielded reconstructions include complete dendritic trees as well local inter-areal axonal projections map up thousands cell-to-cell connections per neuron. release open-access resource scientific community including set tools facilitate retrieval downstream analysis. In accompanying papers describe our findings using provide comprehensive structural characterization cortical cell types 1–3 most detailed synaptic level connectivity diagram column date 2 , uncovering unique cell-type specific inhibitory motifs can be linked gene expression 4 . Functionally, identify new computational principles how information is integrated across space 5 characterize novel neuronal invariances 6 bring structure function together decipher general principle wires excitatory within 7, 8

Language: Английский

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A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing

Science Advances, Journal Year: 2022, Volume and Issue: 8(44)

Published: Nov. 2, 2022

We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation anatomical and neurophysiological data to date. find this brain-like neural network can reproduce number characteristic brain, in particular capability solve diverse tasks, also on temporally dispersed information, with remarkable robustness noise. This model, whose architecture neurons markedly differ from those deep networks used current artificial intelligence (AI), such as convolutional (CNNs), reproduces coding properties which explanations its superior noise robustness. Because is substantially more energy efficient brain compared CNNs AI, are likely have an impact future technology: blueprints energy-efficient neuromorphic hardware.

Language: Английский

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Neural manifold analysis of brain circuit dynamics in health and disease

Journal of Computational Neuroscience, Journal Year: 2022, Volume and Issue: 51(1), P. 1 - 21

Published: Dec. 16, 2022

Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands neurons. However, development analysis approaches for such large-scale neural recordings have been slower than those applicable single-cell experiments. One approach that has gained recent popularity is manifold learning. This takes advantage fact often, even though datasets may be very high dimensional, dynamics tends traverse a much lower-dimensional space. The topological structures formed by these low-dimensional subspaces are referred as "neural manifolds", and potentially provide insight linking circuit with cognitive function behavioral performance. In this paper we review number linear non-linear learning, including principal component (PCA), multi-dimensional scaling (MDS), Isomap, locally embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, uniform approximation projection (UMAP). We outline methods under common mathematical nomenclature, compare their advantages disadvantages respect use data analysis. apply them from published literature, comparing manifolds result application hippocampal place cells, motor cortical neurons during reaching task, prefrontal multi-behavior task. find many circumstances algorithms produce similar results methods, although particular cases where complexity greater, tend manifolds, at expense interpretability. demonstrate study neurological disorders through simulation mouse model Alzheimer's Disease, speculate help us understand circuit-level consequences molecular cellular neuropathology.

Language: Английский

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