How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networks

Published: Feb. 27, 2025

The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 9. This ratio is key factor for achieving the often reported balance excitation and inhibition, hallmark cortical computation. However, neither previous theoretical nor simulation studies have addressed how differences in EI will affect layer-specific dynamics computational properties. We investigate this question using sparsely connected network model neurons. To keep physiological range firing rates, we varied threshold or synaptic strength between find decreasing allows explore higher-dimensional space enhance capacity represent complex input. By comparing empirical layer 2/3 rodent barrel cortex, predict has higher dimensionality coding than 4. Furthermore, our analysis primary visual data Allen Brain Institute corroborates modelling results, also demonstrating increased capabilities 2/3.

Language: Английский

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The tuning of tuning: How adaptation influences single cell information transfer

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(5), P. e1012043 - e1012043

Published: May 13, 2024

Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. To effectively transfer information about stimulus to next processing level, a neuron needs be able adapt its working range properties of stimulus. Here, we focus intrinsic neural that influence in cortical and how tightly their need tuned statistics for them effective. We start by measuring encoding putative excitatory inhibitory L2/3 mouse barrel cortex. Excitatory show high thresholds strong adaptation, making fire sparsely resulting compression information, whereas favour fast spiking more information. Next, turn computational modelling ask two transfer: 1) spike-frequency adaptation 2) shape IV-curve. find subthreshold (but not threshold) ‘h-current’, properly leak conductance can increase neuron, threshold range. Finally, verify effect IV-curve slope our experimental recordings form heterogeneous population than neurons. These relationships between features coding had been quantified before will aid computational, theoretical systems neuroscientists understanding neuronal populations alter properties, such as through impact neuromodulators. Why variability is larger ones an exciting question, which future research needed.

Language: Английский

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Tunable anti-ambipolar vertical bilayer organic electrochemical transistor enable neuromorphic retinal pathway

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 26, 2024

Abstract Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce complexity size bio-realistic Hodgkin-Huxley(HH) spiking circuits logic circuits. However, limited stable anti-ambipolar materials prevent design integrated, tunable, multifunctional logic-based systems. In this work, a general approach tuning characteristics is presented through assembly p-n bilayer in vertical OECT (vOECT) architecture. The reduces device footprint, while material controls characteristics, allowing control device’s on off threshold voltages, peak position, reducing thereby enabling tunable neurons gates. Combining these components, mimic retinal pathway reproducing wavelength light intensity encoding horizontal cells to ganglion demonstrated. This work enables further incorporation conformable adaptive into biointegrated devices featuring sensory coding parallel processing diverse artificial intelligence computing applications.

Language: Английский

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Time-lapse imaging of identified granule cells in the mouse dentate gyrus after entorhinal lesion in vitro reveals heterogeneous cellular responses to denervation

Frontiers in Neuroanatomy, Journal Year: 2025, Volume and Issue: 18

Published: Jan. 21, 2025

Denervation of neurons is a network consequence brain injury. The effects denervation on can be readily studied in vitro using organotypic slice cultures entorhinal cortex and hippocampus. Following transection the entorhino-dentate projection, granule cells (GCs) are denervated show average transient loss spines their distal dendrites but not non-denervated proximal dendrites. In present study, we addressed question how single GCs segments react to denervation. Local adeno-associated virus (AAV)-injections were employed transduce dentate with tdTomato projection EGFP. This made it possible visualize both innervating fibers target identify dendritic located “entorhinal” “hippocampal” zone gyrus. Confocal time-lapse imaging was used image after Time-matched served as controls. line previous reports, spine ~30% (2–4 days post-lesion) zone. However, individual showed considerable variability response layers, decreases well increases density observed at cell level. Based standard deviations effect sizes this computer simulation yielded recommendations for minimum number that should analyzed future studies model.

Language: Английский

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Low-dimensional model for adaptive networks of spiking neurons

Physical review. E, Journal Year: 2025, Volume and Issue: 111(1)

Published: Jan. 24, 2025

We investigate a large ensemble of quadratic integrate-and-fire neurons with heterogeneous input currents and adaptation variables. Our analysis reveals that, for specific class adaptation, termed spike-frequency the high-dimensional system can be exactly reduced to low-dimensional ordinary differential equations, which describes dynamics three mean-field variables: population's firing rate, mean membrane potential, variable. The resulting rate equations (FREs) uncover key generic feature networks adaptation: Both center width distribution neurons' frequencies are reduced, this largely promotes emergence collective synchronization in network. findings further supported by bifurcation FREs, accurately captures spiking neuron network, including phenomena such as oscillations, bursting, macroscopic chaos.

Language: Английский

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Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation

Neuroinformatics, Journal Year: 2025, Volume and Issue: 23(2)

Published: Feb. 1, 2025

Language: Английский

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Direct and Retrograde Wave Propagation in Unidirectionally Coupled Wilson-Cowan Oscillators

Physical Review Letters, Journal Year: 2025, Volume and Issue: 134(5)

Published: Feb. 6, 2025

Some biological systems exhibit both direct and retrograde propagating signal waves despite unidirectional coupling. To explain this phenomenon, we study a chain of unidirectionally coupled Wilson-Cowan oscillators. Surprisingly, find that changes in the homogeneous global input to suffice reverse wave propagation direction. obtain insights, analyze frequencies bifurcations limit cycle solutions as function input. Specifically, determine directionality is controlled by differences intrinsic oscillators caused differential proximity homoclinic bifurcation.

Language: Английский

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Complex Spiking Neural Network Evaluated by Injury Resistance Under Stochastic Attacks

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 186 - 186

Published: Feb. 13, 2025

Brain-inspired models are commonly employed for artificial intelligence. However, the complex environment can hinder performance of electronic equipment. Therefore, enhancing injury resistance brain-inspired is a crucial issue. Human brains have self-adaptive abilities under injury, so drawing on advantages human brain to construct model intended enhance its resistance. But current still lack bio-plausibility, meaning they do not sufficiently draw real neural systems' structure or function. To address this challenge, paper proposes spiking network (Com-SNN) as model, in which topology inspired by topological characteristics biological functional networks, nodes Izhikevich neuron models, and edges synaptic plasticity with time delay co-regulated excitatory synapses inhibitory synapses. evaluate Com-SNN, two injury-resistance metrics investigated compared SNNs alternative topologies stochastic removal simulate consequence attacks. In addition, mechanism remains unclear, revealing understanding development analyzes dynamic regulation Com-SNN The experimental results indicate that superior other SNNs, demonstrating our help improve SNNs. Our imply an intrinsic element impacting resistance, another impacts

Language: Английский

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How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networks

Published: Feb. 27, 2025

The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 9. This ratio is key factor for achieving the often reported balance excitation and inhibition, hallmark cortical computation. However, neither previous theoretical nor simulation studies have addressed how differences in EI will affect layer-specific dynamics computational properties. We investigate this question using sparsely connected network model neurons. To keep physiological range firing rates, we varied threshold or synaptic strength between find decreasing allows explore higher-dimensional space enhance capacity represent complex input. By comparing empirical layer 2/3 rodent barrel cortex, predict has higher dimensionality coding than 4. Furthermore, our analysis primary visual data Allen Brain Institute corroborates modelling results, also demonstrating increased capabilities 2/3.

Language: Английский

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Inhibitory cell type heterogeneity in a spatially structured mean-field model of V1

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Inhibitory interneurons in the cortex are classified into cell types differing their morphology, electrophysiology, and connectivity. Although it is known that parvalbumin (PV), somatostatin (SST), vasoactive intestinal polypeptide-expressing neurons (VIP), major inhibitory neuron subtypes cortex, have distinct modulatory effects on excitatory neurons, how heterogeneous spatial connectivity properties relate to network computations not well understood. Here, we study implications of dynamics spatially-structured neural networks. We develop a mean-field model system order systematically examine excitation-inhibition balance, dynamical stability, cell-type specific gain modulations. The incorporates three with probabilities recent evidence long-range projections SST neurons. Position-dependent firing rate predictions validated against simulations, balanced solutions under Gaussian assumptions derived from scaling arguments. Stability analysis shows while E-I circuits homogeneous population result instability, maintains stability projections. This suggests mixture short inhibitions may be key providing diverse maintaining stability. further find conductance-based synaptic transmissions necessary reproduce experimentally observed cell-type-specific modulations inhibition by PV mechanisms underlying changes elucidated using linear response theory. Our theoretical approach offers insight computational function distance-dependent structure.

Language: Английский

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