Functional specificity of recurrent inhibition in visual cortex

Neuron, Год журнала: 2024, Номер 112(6), С. 991 - 1000.e8

Опубликована: Янв. 21, 2024

In the neocortex, neural activity is shaped by interaction of excitatory and inhibitory neurons, defined organization their synaptic connections. Although connections among pyramidal neurons are sparse functionally tuned, connectivity thought to be dense largely unstructured. By measuring in vivo visual responses parvalbumin-expressing (PV+) cells mouse primary cortex, we show that weights nearby specifically tuned according similarity cells' responses. Individual PV+ strongly inhibit those provide them with strong excitation share selectivity. This structured provides a circuit mechanism for inhibition onto despite connectivity, stabilizing within feature-specific ensembles while supporting competition between them.

Язык: Английский

---

Functional connectomics spanning multiple areas of mouse visual cortex

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Июль 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

Язык: Английский

---

Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization

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

Опубликована: Май 13, 2023

Abstract Deciphering the brain’s structure-function relationship is key to understanding neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, classic example primate neocortex organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex debated. A hurdle identifying difficulty characterizing complex nonlinear tuning, especially high-dimensional inputs. Here, we asked whether area V4, mid-level macaque visual system, organized into columns. We combined large-scale linear probe recordings deep learning methods systematically characterize tuning >1,200 V4 silico synthesis most exciting images (MEIs), followed by vivo verification. found that MEIs single exhibited features like textures, shapes, or even high-level attributes such as eye-like structures. Neurons recorded on same silicon probe, inserted orthogonal surface, were selective spatial features, expected from columnar quantified this finding human psychophysics measuring MEI similarity non-linear embedding space, learned contrastive loss. Moreover, selectivity population was clustered, suggesting form distinct functional groups shared feature selectivity, reminiscent cell types. These closely mirrored maps units artificial vision systems, hinting at encoding principles between biological vision. Our findings provide evidence types may constitute universal organizing neocortex, simplifying cortex’s complexity simpler circuit motifs which perform canonical computations.

Язык: Английский

---

Towards a Foundation Model of the Mouse Visual Cortex

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

Опубликована: Март 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.

Язык: Английский

---

Decoding the brain: From neural representations to mechanistic models

Cell, Год журнала: 2024, Номер 187(21), С. 5814 - 5832

Опубликована: Окт. 1, 2024

Язык: Английский

---

NEURD offers automated proofreading and feature extraction for connectomics

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

Опубликована: Март 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.

Язык: Английский

---

Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space

Cerebral Cortex, Год журнала: 2024, Номер 34(10)

Опубликована: Сен. 23, 2024

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity one hundred or more individual neurons. The interpretation the recorded data calls for multiscale computational models with corresponding spatial dimensions signal predictions. Multi-layer neuron network local cortical circuits about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity reproducing features observed in-vivo statistics. Local field can be computed from simulated activity. We here extend a potential model an area $4\times 4\,{\text{mm}^{2}}$, preserving density introducing distance-dependent connection probabilities conduction delays. find that upscaling procedure preserves overall statistics original reproduces asynchronous irregular across populations weak pairwise spike-train correlations in agreement experimental recordings sensory cortex. Also compatible observations, correlation is strong decays over distance micrometers. Enhanced coherence low-gamma band around $50\,\text{Hz}$ may explain recent report apparent band-pass filter effect reach potential.

Язык: Английский

---

Prediction of future input explains lateral connectivity in primary visual cortex

Current Biology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

---

Experience-dependent reorganization of inhibitory neuron synaptic connectivity

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 16, 2025

Organisms continually tune their perceptual systems to the features they encounter in environment 1-3 . We have studied how ongoing experience reorganizes synaptic connectivity of neurons olfactory (piriform) cortex mouse. developed an approach measure vivo , training a deep convolutional network reliably identify monosynaptic connections from spike-time cross-correlograms 4.4 million single-unit pairs. This revealed that excitatory piriform with similar odor tuning are more likely be connected. asked whether enhances this like-to-like but found it was unaffected by exposure. Experience did, however, alter logic interneuron connectivity. Following repeated encounters set odorants, inhibitory responded differentially these stimuli exhibited high degree both incoming and outgoing within cortical network. reorganization depended only on not its pre- or postsynaptic partners. A computational model reorganized predicts increases dimensionality entire network's responses familiar stimuli, thereby enhancing discriminability. confirmed network-level property is present physiological measurements, which showed increased separability evoked versus novel odorants. Thus, simple, non-Hebbian may selectively enhance organism's discrimination environment.

Язык: Английский

---

Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at synaptic level remains poorly understood. Dissecting such neural circuits vertebrates requires knowledge functional properties and ability to directly correlate dynamics with underlying wiring diagram same animal. Here we combine calcium imaging ultrastructural reconstruction, using visual motion accumulation paradigm larval zebrafish. Using connectomic analyses functionally identified cells computational modeling, show that bilateral inhibition, disinhibition, recurrent connectivity are prominent motifs for sensory within anterior hindbrain. We also demonstrate similar insights about structure-function relationship this can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion neuronal response types. used our unique ground truth datasets train test novel classifier algorithm, allowing us assign labels neurons from libraries where lacking. The resulting feature-rich library identities connectomes enabled constrain biophysically realistic network model hindbrain reproduce observed make testable predictions future experiments. Our work exemplifies power hypothesis-driven electron microscopy paired recordings gain mechanistic into signal processing provides framework dissecting vertebrates.

Язык: Английский

---