scholarly journals Hierarchical Timescales in the Neocortex: Mathematical Mechanism and Biological Insights

2021 ◽  
Author(s):  
Songting Li ◽  
Xiao-Jing Wang
Keyword(s):  
Large Scale ◽  
Detailed Balance ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Progressive Increase ◽  
Connectivity Matrix ◽  
Electrophysiological Properties ◽  
Neuronal Populations ◽  
Sensory Neural ◽  
Experimental Findings

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short-time scales whereas higher association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense inter-areal cortical connectivity known in modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically-based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: (1) a macroscopic gradient of synaptic excitation, (2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and (3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks.

Correlations and Population Dynamics in Cortical Networks

2008 ◽  
Vol 20 (9) ◽  
pp. 2185-2226 ◽  
Author(s):  
Birgit Kriener ◽  
Tom Tetzlaff ◽  
Ad Aertsen ◽  
Markus Diesmann ◽  
Stefan Rotter
Keyword(s):  
Mean Field ◽  
Power Spectra ◽  
Membrane Potentials ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Connectivity Matrix ◽  
Cortical Networks ◽  
Population Activity ◽  
Neuronal Populations ◽  
Experimental Findings

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

scholarly journals Direct comparison of population receptive fields from fMRI and large-scale neurophysiological recordings in awake non-human primates

2020 ◽  
Author(s):  
P. Christiaan Klink ◽  
Xing Chen ◽  
Wim Vanduffel ◽  
Pieter R. Roelfsema
Keyword(s):  
Large Scale ◽  
Receptive Fields ◽  
Field Potential ◽  
Spatial Summation ◽  
Network Nodes ◽  
Retinotopic Organization ◽  
Neuronal Signal ◽  
Distinct Frequency ◽  
Neural Underpinnings ◽  
Brain Fmri

AbstractPopulation receptive field (pRF) modeling is a popular method to map the retinotopic organization of the human brain with fMRI. While BOLD-based pRF-maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they truly represent. We address this question with whole-brain fMRI and large-scale neurophysiological recordings in awake non-human primates. Several pRF-models were independently fit to the BOLD signal, multi-unit spiking activity (MUA) and local field potential (LFP) power in distinct frequency bands. Our results provide a retinotopic characterization of cortical and subcortical areas, suggest brain-wide compressive (i.e., sublinear) spatial summation, and demonstrate a visually tuned deactivation of default mode network nodes. Cross-signal analysis of pRF-map structure (eccentricity-size relation) indicates that the neural underpinnings of BOLD-pRFs are area-specific. In V1, BOLD-pRFs mirror MUA, while in V4 they are more similar to the tuning of the gamma LFP-power.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Position Selectivity in Scene- and Object-Responsive Occipitotemporal Regions

2007 ◽  
Vol 98 (4) ◽  
pp. 2089-2098 ◽  
Author(s):  
Sean P. MacEvoy ◽  
Russell A. Epstein
Keyword(s):  
Large Scale ◽  
Scene Perception ◽  
Receptive Fields ◽  
Neural Responses ◽  
Retinal Position ◽  
Vertical Meridian ◽  
Retinal Stimulation ◽  
Natural Vision ◽  
Visual Hemifields ◽  
Insight Into

Complex visual scenes preferentially activate several areas of the human brain, including the parahippocampal place area (PPA), the retrosplenial complex (RSC), and the transverse occipital sulcus (TOS). The sensitivity of neurons in these regions to the retinal position of stimuli is unknown, but could provide insight into their roles in scene perception and navigation. To address this issue, we used functional magnetic resonance imaging (fMRI) to measure neural responses evoked by sequences of scenes and objects confined to either the left or right visual hemifields. We also measured the level of adaptation produced when stimuli were either presented first in one hemifield and then repeated in the opposite hemifield or repeated in the same hemifield. Although overall responses in the PPA, RSC, and TOS tended to be higher for contralateral stimuli than for ipsilateral stimuli, all three regions exhibited position-invariant adaptation, insofar as the magnitude of adaptation did not depend on whether stimuli were repeated in the same or opposite hemifields. In contrast, object-selective regions showed significantly greater adaptation when objects were repeated in the same hemifield. These results suggest that neuronal receptive fields (RFs) in scene-selective regions span the vertical meridian, whereas RFs in object-selective regions do not. The PPA, RSC, and TOS may support scene perception and navigation by maintaining stable representations of large-scale features of the visual environment that are insensitive to the shifts in retinal stimulation that occur frequently during natural vision.

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

2021 ◽  
pp. 016173462110425
Author(s):  
Jianing Xi ◽  
Jiangang Chen ◽  
Zhao Wang ◽  
Dean Ta ◽  
Bing Lu ◽  
...  
Keyword(s):  
Congenital Anomaly ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Scale Model ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

scholarly journals Mechanism of large-scale flow reversals in turbulent thermal convection

2018 ◽  
Vol 4 (11) ◽  
pp. eaat7480 ◽  
Author(s):  
Yin Wang ◽  
Pik-Yin Lai ◽  
Hao Song ◽  
Penger Tong
Keyword(s):  
Thermal Convection ◽  
Large Scale ◽  
Careful Analysis ◽  
Extreme Value Statistics ◽  
Convection Cell ◽  
Heat Accumulation ◽  
Thermal Plumes ◽  
Minute Amount ◽  
Large Scale Flow ◽  
Experimental Findings

It is commonly believed that heat flux passing through a closed thermal convection system is balanced so that the convection system can remain at a steady state. Here, we report a new kind of convective instability for turbulent thermal convection, in which the convective flow stays over a long steady “quiet period” having a minute amount of heat accumulation in the convection cell, followed by a short and intermittent “active period” with a massive eruption of thermal plumes to release the accumulated heat. The rare massive eruption of thermal plumes disrupts the existing large-scale circulation across the cell and resets its rotational direction. A careful analysis reveals that the distribution of the plume eruption amplitude follows the generalized extreme value statistics with an upper bound, which changes with the fluid properties of the convecting medium. The experimental findings have important implications to many closed convection systems of geophysical scale, in which massive eruptions and sudden changes in large-scale flow pattern are often observed.

scholarly journals Attention operates uniformly throughout the classical receptive field and the surround

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Bram-Ernst Verhoef ◽  
John HR Maunsell
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Field Structure ◽  
Neuronal Responses ◽  
Large Variability ◽  
Area V4 ◽  
Classical Receptive Field ◽  
Spatially Varying

Shifting attention among visual stimuli at different locations modulates neuronal responses in heterogeneous ways, depending on where those stimuli lie within the receptive fields of neurons. Yet how attention interacts with the receptive-field structure of cortical neurons remains unclear. We measured neuronal responses in area V4 while monkeys shifted their attention among stimuli placed in different locations within and around neuronal receptive fields. We found that attention interacts uniformly with the spatially-varying excitation and suppression associated with the receptive field. This interaction explained the large variability in attention modulation across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation that has not been previously described. A spatially-tuned normalization model precisely accounted for all observed attention modulations and for the spatial summation properties of neurons. These results provide a unified account of spatial summation and attention-related modulation across both the classical receptive field and the surround.

Power spectral density of magnetic field and ion velocity fluctuations from inertial to kinetic ranges

2021 ◽  
Author(s):  
Jana Šafránková ◽  
Zdeněk Němeček ◽  
František Němec ◽  
Luca Franci ◽  
Alexander Pitňa
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Large Scale ◽  
Collisionless Plasma ◽  
Power Spectra ◽  
Physical Parameters ◽  
Particle In Cell ◽  
High Reynolds ◽  
Experimental Findings ◽  
Turbulent Cascade

<p>The solar wind is a unique laboratory to study the turbulent processes occurring in a collisionless plasma with high Reynolds numbers. A turbulent cascade—the process that transfers the free energy contained within the large scale fluctuations into the smaller ones—is believed to be one of the most important mechanisms responsible for heating of the solar corona and solar wind. The paper analyzes power spectra of solar wind velocity, density and magnetic field fluctuations that are computed in the frequency range around the break between inertial and kinetic scales. The study uses measurements of the Bright Monitor of the Solar Wind (BMSW) on board the Spektr-R spacecraft with a time resolution of 32 ms complemented with 10 Hz magnetic field observations from the Wind spacecraft propagated to the Spektr-R location. The statistics based on more than 42,000 individual spectra show that: (1) the spectra of both quantities can be fitted by two (three in the case of the density) power-law segments; (2) the median slopes of parallel and perpendicular fluctuation velocity and magnetic field components are different; (3) the break between MHD and kinetic scales as well as the slopes are mainly controlled by the ion beta parameter. These experimental results are compared with high-resolution 2D hybrid particle-in-cell simulations, where the electrons are considered to be a massless, charge-neutralizing fluid with a constant temperature, whereas the ions are described as macroparticles representing portions of their distribution function. In spite of several limitations (lack of the electron kinetics, lower dimensionality), the model results agree well with the experimental findings. Finally, we discuss differences between observations and simulations in relation to the role of important physical parameters in determining the properties of the turbulent cascade.</p>

Behaviour of slender concrete shear walls with high-strength reinforcement under reversed cyclic loading.

2021 ◽  
Author(s):  
Nima Aghniaey ◽  
Murat Saatcioglu ◽  
Hassan Aoude
Keyword(s):  
Large Scale ◽  
Shear Walls ◽  
High Strength ◽  
Shear Wall ◽  
Analytical Investigation ◽  
Seismic Behaviour ◽  
Drift Capacity ◽  
Lateral Drift ◽  
Experimental Findings ◽  
Reversed Cyclic

Research on seismic behaviour of shear walls with high-strength steel is limited. A combined experimental and analytical investigation was conducted to assess seismic behaviour of flexure-dominant shear walls. A large-scale concrete shear wall with Grade 690 MPa (ASTM A1035) reinforcement and 84 MPa concrete was tested under simulated seismic loading. The wall was a ¼ -scale of a 6-storey shear wall, with 4.53 m height and 1.45 m length. It sustained a lateral drift of 1.8% prior to developing failure due to the rupturing of longitudinal reinforcement. This is 35% less than the drift capacity of a companion wall reinforced with 400 MPa reinforcement tested earlier. VecTor2 software was used to conduct an analytical parametric study to expand the experimental findings. The results indicate that the reinforcement grade has a significant impact on strength, ductility and hysteretic behaviour of shear walls.

FPGA Coprocessor for Simulation of Neural Networks Using Compressed Matrix Storage

2011 ◽  
pp. 255-275
Author(s):  
Jörg Bornschein
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sparse Matrix ◽  
Receptive Fields ◽  
The State ◽  
Connectivity Matrix ◽  
Vector Product ◽  
Sparse Connectivity ◽  
Matrix Vector ◽  
Direct Implementation

An FPGA-based coprocessor has been implemented which simulates the dynamics of a large recurrent neural network composed of binary neurons. The design has been used for unsupervised learning of receptive fields. Since the number of neurons to be simulated (>104) exceeds the available FPGA logic capacity for direct implementation, a set of streaming processors has been designed. Given the state- and activity vectors of the neurons at time t and a sparse connectivity matrix, these streaming processors calculate the state- and activity vectors for time t + 1. The operation implemented by the streaming processors can be understood as a generalized form of a sparse matrix vector product (SpMxV). The largest dataset, the sparse connectivity matrix, is stored and processed in a compressed format to better utilize the available memory bandwidth.

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