scholarly journals NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model

2021 ◽  
pp. 1-40
Author(s):  
Cecilia Romaro ◽  
Fernando Araujo Najman ◽  
William W. Lytton ◽  
Antonio C. Roque ◽  
Salvador Dura-Bernal
Keyword(s):  
Local Field ◽  
Large Scale ◽  
Field Potential ◽  
Network Size ◽  
Model Parameters ◽  
Neuron Models ◽  
Cortical Microcircuit ◽  
Optimizing Model ◽  
New Research ◽  
High Level

Abstract The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.

scholarly journals Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics

2018 ◽  
Vol 32 (2) ◽  
pp. 255-270 ◽  
Author(s):  
Han Wang ◽  
Kun Xie ◽  
Li Xie ◽  
Xiang Li ◽  
Meng Li ◽  
...  
Keyword(s):  
Local Field ◽  
Sparse Coding ◽  
Local Field Potential ◽  
Large Scale ◽  
Brain Connectivity ◽  
Field Potential ◽  
Functional Brain

scholarly journals Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

2021 ◽  
Author(s):  
Gustavo B Libotte ◽  
Lucas Anjos ◽  
Regina Almeida ◽  
Sandra Malta ◽  
Renato Silva
Keyword(s):  
Large Scale ◽  
Compartmental Model ◽  
Gaussian Process Regression ◽  
Predictive Ability ◽  
Model Parameters ◽  
Study Case ◽  
Estimation Of Model Parameters ◽  
Reported Data ◽  
High Level ◽  
Political Economic

Abstract Reliable data is essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

scholarly journals Large- and multi-scale networks in the rodent brain during novelty exploration

2020 ◽  
Author(s):  
Michael X Cohen ◽  
Bernhard Englitz ◽  
Arthur S C França
Keyword(s):  
Prefrontal Cortex ◽  
Parietal Cortex ◽  
Local Field ◽  
Neural Activity ◽  
Large Scale ◽  
Field Potential ◽  
Brain Regions ◽  
Data Availability ◽  
Data Matrix ◽  
Temporal Scales

AbstractNeural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

scholarly journals Enhancing the estimation of compartmental model parameters for COVID-19 data with a high level of uncertainty

2020 ◽  
Author(s):  
Gustavo B. Libotte ◽  
Lucas dos Anjos ◽  
Regina C. Almeida ◽  
Sandra M. C. Malta ◽  
Renato S. Silva
Keyword(s):  
Large Scale ◽  
Gaussian Process Regression ◽  
Predictive Ability ◽  
Compartmental Models ◽  
Model Parameters ◽  
The Novel ◽  
Novel Coronavirus ◽  
Reported Data ◽  
High Level ◽  
Political Economic

AbstractResearch on predictions related to the spread of the novel coronavirus are crucial in decision-making to mitigate the disease. Computational simulations are often used as a basis for forecasting the dynamics of epidemics and, for this purpose, compartmental models have been widely used to assess the situation resulting from the spread of the disease in the population. Reliable data is essential to obtain adequate simulations. However, several political, economic, and social factors have caused inconsistencies in the reported data, which are reflected in the capacity for realistic simulations and predictions. Such uncertainties are mainly motivated by a large-scale underreporting of cases due to the reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of compartmental models, we propose strategies capable of improving the ability to predict the spread of the disease. We show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, thus improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

scholarly journals Modeling the local field potential by a large-scale layered cortical network model

2009 ◽  
Vol 3 ◽  
Author(s):  
Henrik Lindén ◽  
Tobias Potjans ◽  
Gaute Einevoll ◽  
Sonja Grün ◽  
Markus Diesmann
Keyword(s):  
Network Model ◽  
Local Field ◽  
Local Field Potential ◽  
Large Scale ◽  
Field Potential ◽  
Cortical Network

scholarly journals Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs

2021 ◽  
Vol 15 ◽  
Author(s):  
Bruno Golosio ◽  
Gianmarco Tiddia ◽  
Chiara De Luca ◽  
Elena Pastorelli ◽  
Francesco Simula ◽  
...  
Keyword(s):  
Biological Activity ◽  
Programming Languages ◽  
Large Scale ◽  
Parallel Implementation ◽  
Neuron Models ◽  
Integrate And Fire ◽  
C Programming ◽  
Cortical Microcircuit ◽  
Simulation Time ◽  
Integrate And Fire Neuron

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity.

scholarly journals Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

2021 ◽  
Vol 12 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Tianzhi Tang ◽  
Leila Fakhraei ◽  
Xuanyu Wu ◽  
Sidharth Hulyalkar ◽  
...  
Keyword(s):  
Local Field ◽  
Single Unit ◽  
Large Scale ◽  
Brain Activity ◽  
Low Cost ◽  
Local Field Potentials ◽  
Field Potential ◽  
Brain Regions ◽  
Field Potentials ◽  
Brain Target

Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

scholarly journals Estimation of neural network model parameters from local field potentials (LFPs)

2019 ◽  
Author(s):  
Jan-Eirik W. Skaar ◽  
Alexander J. Stasik ◽  
Espen Hagen ◽  
Torbjørn V. Ness ◽  
Gaute T. Einevoll
Keyword(s):  
Network Model ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Low Frequency ◽  
Network Models ◽  
Neural Nets ◽  
Model Parameters ◽  
Neuron Network ◽  
Local Field Potential Lfp

AbstractMost modeling in systems neuroscience has beendescriptivewhere neural representations, that is, ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented bymechanisticmodels based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to infer properties of the neuronal network. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three synaptic weight parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation.Significance statementMost of what we have learned about brain networksin vivohave come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from ‘background’ LFP signals, suggesting a future key role for LFP in development of network models.

scholarly journals Origin of slow spontaneous resting-state neuronal fluctuations in brain networks

2018 ◽  
Vol 115 (26) ◽  
pp. 6858-6863 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Local Field ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Field Potential ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

Resting- or baseline-state low-frequency (0.01–0.2 Hz) brain activity is observed in fMRI, EEG, and local field potential recordings. These fluctuations were found to be correlated across brain regions and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow resting-state fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (<0.05 Hz) activity could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra- and extracellular K+and Na+and intracellular Cl−ions, Na+/K+exchange pump, and KCC2 cotransporter. In the network model simulating resting awake-like brain state, we observed infra-slow fluctuations in the extracellular K+concentration, Na+/K+pump activation, firing rate of neurons, and local field potentials. Holding K+concentration constant prevented generation of the infra-slow fluctuations. The amplitude and peak frequency of this activity were modulated by the Na+/K+pump, AMPA/GABA synaptic currents, and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state activity observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are reported as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex

2017 ◽  
Vol 8 ◽  
Author(s):  
David L. Boothe ◽  
Alfred B. Yu ◽  
Pawel Kudela ◽  
William S. Anderson ◽  
Jean M. Vettel ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Local Field ◽  
Local Field Potential ◽  
Large Scale ◽  
Membrane Damage ◽  
Field Potential ◽  
Neuronal Membrane ◽  
Large Scale Simulation
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