scholarly journals Simulating large-scale models of brain neuronal circuits using Google Cloud Platform

2020 ◽  
Author(s):  
Subhashini Sivagnanam ◽  
Wyatt Gorman ◽  
Donald Doherty ◽  
Samuel A Neymotin ◽  
Stephen Fang ◽  
...  
Keyword(s):  
Large Scale ◽  
Circuit Simulation ◽  
Experimental Studies ◽  
Model Parameters ◽  
Cortical Model ◽  
Detailed Model ◽  
Scale Models ◽  
Brain Circuit ◽  
Large Scale Simulations ◽  
The Brain

Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resulting brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations, with each simulated second of the full circuit model requiring approximately 50 cores hours. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.

Brain stem – from general view to computational model based on switchboard rules of operation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Włodzisław Duch ◽  
Dariusz Mikołajewski
Keyword(s):  
Brain Stem ◽  
Large Scale ◽  
Ionic Channels ◽  
General View ◽  
The Central Nervous System ◽  
Network States ◽  
Large Scale Simulations ◽  
Influence Of Noise ◽  
The Brain ◽  
Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

scholarly journals Certificate Of Online Learning And Teaching (COLT) At The University Of Hawaii: A Horse Of Another Color For Earning College Credits

2013 ◽  
Vol 10 (4) ◽  
pp. 255-262 ◽  
Author(s):  
Michael P. Menchaca ◽  
Ellen S. Hoffman
Keyword(s):  
Online Education ◽  
Large Scale ◽  
College Credits ◽  
Learning And Teaching ◽  
Innovative Program ◽  
Scale Models ◽  
Brick And Mortar ◽  
World Class ◽  
The University ◽  
The Brain

Current conventional wisdom may perceive that higher education is outdated and maybe even likely to collapse. Online education is often predicted to replace brick-and-mortar campuses with systems providing students access to world-class learning via smartphones and tablets. Many private and commercial ventures are embracing such concepts. However, in the race to implement large-scale models, significant key elements such as understanding that learning can be social, affective, personal, and even cultural may be missing. Thus, creative yet research-based programs at the university level are needed. While it is true that existing university structures might inhibit the implementation of radical programs, there are opportunities where such innovation can be offered. In the case of the Department of Educational Technology at the University of Hawaii, an option for a program at the certificate level not necessarily leading to a traditional degree was provided. The certificate option provided an opportunity to explore entrepreneurial models while also incorporating what we understand about learning, the brain, and newer technologies. This paper describes the circumstances and approach that led to the creation of an innovative program that still fit within current university structures.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

scholarly journals A data-informed mean-field approach to mapping of cortical parameter landscapes

2021 ◽  
Vol 17 (12) ◽  
pp. e1009718
Author(s):  
Zhuo-Cheng Xiao ◽  
Kevin K. Lin ◽  
Lai-Sang Young
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Mean Field ◽  
Network Models ◽  
Complete Characterization ◽  
Model Parameters ◽  
Cortical Model ◽  
Experimental Values ◽  
The Many ◽  
Data Informed

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

scholarly journals A unified computational model for cortical post-synaptic plasticity

2020 ◽  
Author(s):  
Tuomo Mäki-Marttunen ◽  
Nicolangelo Iannella ◽  
Andrew G. Edwards ◽  
Gaute T. Einevoll ◽  
Kim T. Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Ampa Receptor ◽  
Cortical Plasticity ◽  
Experimental Studies ◽  
Intracellular Signalling ◽  
Synaptic Proteins ◽  
Model Parameters ◽  
Synaptic Potentiation ◽  
Detailed Model ◽  
Cortical Areas

AbstractCortical synapses possess a machinery of signalling pathways that leads to various modes of post-synaptic plasticity. Such pathways have been examined to a great detail separately in many types of experimental studies. However, a unified picture on how multiple biochemical pathways collectively shape the observed synaptic plasticity in the neocortex is missing. Here, we built a biochemically detailed model of post-synaptic plasticity that includes the major signalling cascades, namely, CaMKII, PKA, and PKC pathways which, upon activation by Ca2+, lead to synaptic potentiation or depression. We adjusted model components from existing models of intracellular signalling into a single-compartment simulation framework. Furthermore, we propose a statistical model for the prevalence of different types of membrane-bound AMPA-receptor tetramers consisting of GluR1 and GluR2 subunits in proportions suggested by the biochemical signalling model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance. We show that our model can reproduce neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex. Moreover, we demonstrate that our model can be fit to data from many cortical areas and that the resulting model parameters reflect the involvement of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.Significance statementNeocortical synaptic plasticity has been studied experimentally in a number of cortical areas, showing how interactions between neuromodulators and post-synaptic proteins shape the outcome of the plasticity. On the other hand, non-detailed computational models of long-term plasticity, such as Hebbian rules of synaptic potentiation and depression, have been widely used in modelling of neocortical circuits. In this work, we bridge the gap between these two branches of neuroscience by building a detailed model of post-synaptic plasticity that can reproduce observations on cortical plasticity and provide biochemical meaning to the simple rules of plasticity. Our model can be used for predicting the effects of chemical or genetic manipulations of various intracellular signalling proteins on induction of plasticity in health and disease.

scholarly journals A data-informed mean-field approach to mapping of cortical parameter landscapes

2021 ◽  
Author(s):  
Zhuo-Cheng Xiao ◽  
Kevin K Lin ◽  
Lai-Sang Young
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Mean Field ◽  
Network Models ◽  
Complete Characterization ◽  
Model Parameters ◽  
Cortical Model ◽  
Experimental Values ◽  
The Many ◽  
Data Informed

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

scholarly journals Efficient parameterization of large-scale dynamic models based on relative measurements

2019 ◽  
Author(s):  
Leonard Schmiester ◽  
Yannik Schälte ◽  
Fabian Fröhlich ◽  
Jan Hasenauer ◽  
Daniel Weindl
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Error Model ◽  
Supplementary Information ◽  
Reaction Networks ◽  
Model Parameters ◽  
Biological Processes ◽  
Wide Range ◽  
Scale Models ◽  
Relative Measurements

Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale. Results Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements. Availability and implementation Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons

2021 ◽  
Author(s):  
Fereshteh Lagzi ◽  
Martha Canto Bustos ◽  
Anne-Marie Oswald ◽  
Brent Doiron
Keyword(s):  
Large Scale ◽  
Mean Field ◽  
Spike Timing ◽  
Excitatory Synapses ◽  
Mean Field Models ◽  
Stdp Rule ◽  
Somatostatin Neurons ◽  
Large Scale Simulations ◽  
The Brain ◽  
Parvalbumin Neurons

AbstractLearning entails preserving the features of the external world in the neuronal representations of the brain, and manifests itself in the form of strengthened interactions between neurons within assemblies. Hebbian synaptic plasticity is thought to be one mechanism by which correlations in spiking promote assembly formation during learning. While spike timing dependent plasticity (STDP) rules for excitatory synapses have been well characterized, inhibitory STDP rules remain incomplete, particularly with respect to sub-classes of inhibitory interneurons. Here, we report that in layer 2/3 of the orbitofrontal cortex of mice, inhibition from parvalbumin (PV) interneurons onto excitatory (E) neurons follows a symmetric STDP function and mediates homeostasis in E-neuron firing rates. However, inhibition from somatostatin (SOM) interneurons follows an asymmetric, Hebbian STDP rule. We incorporate these findings in both large scale simulations and mean-field models to investigate how these differences in plasticity impact network dynamics and assembly formation. We find that plasticity of SOM inhibition builds lateral inhibitory connections and increases competition between assemblies. This is reflected in amplified correlations between neurons within assembly and anti-correlations between assemblies. An additional finding is that the emergence of tuned PV inhibition depends on the interaction between SOM and PV STDP rules. Altogether, we show that incorporation of differential inhibitory STDP rules promotes assembly formation through competition, while enhanced inhibition both within and between assemblies protects new representations from degradation after the training input is removed.

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