scholarly journals Towards a general framework for modeling large-scale biophysical neuronal networks: a full-scale computational model of the rat dentate gyrus

2021 ◽  
Author(s):  
Ivan Georgiev Raikov ◽  
Aaron D Milstein ◽  
Prannath Moolchand ◽  
Gergely G Szabo ◽  
Calvin J Schneider ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Open Source ◽  
Large Scale ◽  
Computational Models ◽  
Hippocampal Formation ◽  
Full Scale ◽  
Scale Model ◽  
Population Activity ◽  
Data Movement

Large-scale computational models of the brain are necessary to accurately represent anatomical and functional variability in neuronal biophysics across brain regions and also to capture and study local and global interactions between neuronal populations on a behaviorally-relevant temporal scale. We present the methodology behind and an initial implementation of a novel open-source computational framework for construction, simulation, and analysis of models consisting of millions of neurons on high-performance computing systems, based on the NEURON and CoreNEURON simulators. This framework includes an HDF5-based data format for storing morphological, synaptic, and connectivity information of large neuronal network models, and an accompanying open source software library that provides efficient, scalable parallel storage and MPI-based data movement capabilities. We outline our approaches for constructing detailed large-scale biophysical models with topographical connectivity and input stimuli, and present simulation results obtained with a full-scale model of the dentate gyrus constructed with our framework. The model generates sparse and spatially selective population activity that fits well with in-vivo experimental data. Moreover, our approach is fully general and can be applied to modeling other regions of the hippocampal formation in order to rapidly evaluate specific hypotheses about large-scale neural architectural features.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes

2020 ◽  
Author(s):  
Anastasiia Gainullina ◽  
Li-Hao Huang ◽  
Helena Todorov ◽  
Kiwook Kim ◽  
Lim Sheau Yng ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Open Source ◽  
Large Scale ◽  
Cholesterol Biosynthesis ◽  
Mononuclear Phagocyte ◽  
Mononuclear Phagocytes ◽  
Metabolic Diversity ◽  
Cell Subpopulations ◽  
Different Tissues

AbstractWe dissect metabolic variability of mononuclear phagocyte (MNP) subpopulations across different tissues through integrative analysis of three large scale datasets. Specifically, we introduce ImmGen MNP Open Source dataset that profiled 337 samples and extended previous ImmGen effort which included 202 samples of mononuclear phagocytes and their progenitors. Next, we analysed Tabula Muris Senis dataset to extract data for 51,364 myeloid cells from 18 tissues. Taken together, a compendium of data assembled in this work covers phagocytic populations found across 38 different tissues. To analyse common metabolic features, we developed novel network-based computational approach for unbiased identification of key metabolic subnetworks based on cellular transcriptional profiles in large-scale datasets. Using ImmGen MNP Open Source dataset as baseline, we define 9 metabolic subnetworks that encapsulate the metabolic differences within mononuclear phagocytes, and demonstrate that these features are robustly found across all three datasets, including lipid metabolism, cholesterol biosynthesis, glycolysis, and a set of fatty acid related metabolic pathways, as well as nucleotide and folate metabolism. We systematically define major features specific to macrophage and dendritic cell subpopulations. Among other things, we find that cholesterol synthesis appears particularly active within the migratory dendritic cells. We demonstrate that interference with this pathway through statins administration diminishes migratory capacity of the dendritic cells in vivo. This result demonstrates the power of our approach and highlights importance of metabolic diversity among mononuclear phagocytes.

scholarly journals Top-Down Suppression of Sensory Cortex in an NMDAR Hypofunction Model of Psychosis

2019 ◽  
Vol 45 (6) ◽  
pp. 1349-1357 ◽  
Author(s):  
Adam Ranson ◽  
Eluned Broom ◽  
Anna Powell ◽  
Fangli Chen ◽  
Guy Major ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Anterior Cingulate ◽  
Sensory Cortex ◽  
Top Down ◽  
Genetic Studies ◽  
Evoked Activity ◽  
Sensory Evoked ◽  
Information Streams

Abstract Conceptual and computational models have been advanced that propose that perceptual disturbances in psychosis, such as hallucinations, may arise due to a disruption in the balance between bottom-up (ie sensory) and top-down (ie from higher brain areas) information streams in sensory cortex. However, the neural activity underlying this hypothesized alteration remains largely unexplored. Pharmacological N-methyl-d-aspartate receptor (NMDAR) antagonism presents an attractive model to examine potential changes as it acutely recapitulates many of the symptoms of schizophrenia including hallucinations, and NMDAR hypofunction is strongly implicated in the pathogenesis of schizophrenia as evidenced by large-scale genetic studies. Here we use in vivo 2-photon imaging to measure frontal top-down signals from the anterior cingulate cortex (ACC) and their influence on activity of the primary visual cortex (V1) in mice during pharmacologically induced NMDAR hypofunction. We find that global NMDAR hypofunction causes a significant increase in activation of top-down ACC axons, and that surprisingly this is associated with an ACC-dependent net suppression of spontaneous activity in V1 as well as a reduction in V1 sensory-evoked activity. These findings are consistent with a model in which perceptual disturbances in psychosis are caused in part by aberrant top-down frontal cortex activity that suppresses the transmission of sensory signals through early sensory areas.

scholarly journals Breakdown of Effective Connectivity During Slow Wave Sleep: Investigating the Mechanism Underlying a Cortical Gate Using Large-Scale Modeling

2009 ◽  
Vol 102 (4) ◽  
pp. 2096-2111 ◽  
Author(s):  
Steve K. Esser ◽  
Sean Hill ◽  
Giulio Tononi
Keyword(s):  
Slow Wave ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Effective Connectivity ◽  
Signal Transmission ◽  
Sensory Stimulation ◽  
Scale Model ◽  
Synaptic Excitation ◽  
Cortical Areas

Effective connectivity between cortical areas decreases during slow wave sleep. This decline can be observed in the reduced interareal propagation of activity evoked either directly in cortex by transcranial magnetic stimulation (TMS) or by sensory stimulation. We present here a large-scale model of the thalamocortical system that is capable of reproducing these experimental observations. This model was constructed according to a large number of physiological and anatomical constraints and includes over 30,000 spiking neurons interconnected by more than 5 million synaptic connections and organized into three cortical areas. By simulating the different effects of arousal promoting neuromodulators, the model can produce a waking or a slow wave sleep-like mode. In this work, we also seek to explain why intercortical signal transmission decreases in slow wave sleep. The traditional explanation for reduced brain responses during this state, a thalamic gate, cannot account for the reduced propagation between cortical areas. Therefore we propose that a cortical gate is responsible for this diminished intercortical propagation. We used our model to test three candidate mechanisms that might produce a cortical gate during slow wave sleep: a propensity to enter a local down state following perturbation, which blocks the propagation of activity to other areas, increases in potassium channel conductance that reduce neuronal responsiveness, and a shift in the balance of synaptic excitation and inhibition toward inhibition, which decreases network responses to perturbation. Of these mechanisms, we find that only a shift in the balance of synaptic excitation and inhibition can account for the observed in vivo response to direct cortical perturbation as well as many features of spontaneous sleep.

Virtual Demonstration Tests of Large-Scale and Complex Artifacts Using an Open Source Parallel CAE System, ADVENTURE

2006 ◽  
Vol 110 ◽  
pp. 133-142 ◽  
Author(s):  
Shinobu Yoshimura
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Three Dimensional ◽  
Element Model ◽  
General Purpose ◽  
Full Scale ◽  
Grid Environments ◽  
Dimensional Finite Element ◽  
Three Dimensional Finite Element ◽  
Cae System

The ADVENTURE project started as one of the research projects in the "Computational Science & Engineering" field selected for the "Research for the Future" Program sponsored by the Japan Society for the Promotion of Science during 1997-2002. Since March 2002, the project has continued as an independent project. In the project we have been developing an advanced general-purpose computational mechanics system, named ADVENTURE, running in various kinds of parallel and ditributed environments. The system is designed to be able to analyze a three-dimensional finite element model of arbitrary shape with 10-100 million DOFs mesh, and additionally to enable parametric and non-parametric shape optimization. The first version of the system has been released from the project website as open source software since March, 2002. 2,049 registered users in academia and industries have downloaded 12,827 modules and been using them, while one company has developed and released its commercial version named ADVENTUREcluster. The ADVENTURE system has been successfully implemented in various types of parallel and distributed environments including PC clusters, massively parallel processers such as Hitachi SR8000/MPP and the Earth Simulator, and Grid environments such as ITBL (IT-based Laboratory). The system has been successfully applied to solve various real world problems such as response of a full scale nuclear pressure vessel model and thermoelastic deformation of full scale electric mounting board of a mobile PC.

scholarly journals A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination

2021 ◽  
Author(s):  
Ruy Gómez-Ocádiz ◽  
Massimiliano Trippa ◽  
Lorenzo Posani ◽  
Simona Cocco ◽  
Rémi Monasson ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Virtual Environments ◽  
Acetylcholine Receptors ◽  
Granule Cells ◽  
Memory Formation ◽  
Synaptic Response ◽  
Population Activity ◽  
Attractor Networks ◽  
Whole Cell Patch Clamp

AbstractEpisodic memory formation and recall are complementary processes that put conflicting requirements on neuronal computations in the hippocampus. How this challenge is resolved in hippocampal circuits is unclear. To address this question, we obtained in vivo whole-cell patch-clamp recordings from dentate gyrus granule cells in head-fixed mice navigating in familiar and novel virtual environments. We find that granule cells consistently show a small transient depolarization of their membrane potential upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the observed transient synaptic response to novel environments may lead to a bias in the granule cell population activity, which can in turn drive the downstream attractor networks to a new state, thereby favoring the switch from generalization to discrimination when faced with novelty. Such a novelty-driven cholinergic switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones.

scholarly journals Connectivity and dynamics in the olfactory bulb

2021 ◽  
Author(s):  
David EC Kersen ◽  
Gaia Tavoni ◽  
Vijay Balasubramanian
Keyword(s):  
Olfactory Bulb ◽  
Lateral Inhibition ◽  
Large Scale ◽  
Computational Models ◽  
Granule Cells ◽  
Cell Activity ◽  
Gamma Oscillations ◽  
Scale Model ◽  
Mitral Cells ◽  
Cortical Feedback

Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the probability of synapse between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. It in turn predicts testable relations between network structure, lateral inhibition, and odor pattern decorrelation; between the density of granule cell activity and LFP oscillation frequency; how cortical feedback to granule cells affects mitral cell activity; and how cortical feedback to mitral cells is modulated by the network embedding. Additionally, the methodology we describe here provides a tractable tool for other researchers.

scholarly journals Topological Determinants of Epileptogenesis in Large-Scale Structural and Functional Models of the Dentate Gyrus Derived From Experimental Data

2007 ◽  
Vol 97 (2) ◽  
pp. 1566-1587 ◽  
Author(s):  
Jonas Dyhrfjeld-Johnsen ◽  
Vijayalakshmi Santhakumar ◽  
Robert J. Morgan ◽  
Ramon Huerta ◽  
Lev Tsimring ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Network Architecture ◽  
Large Scale ◽  
Structural Changes ◽  
Structural Model ◽  
Small World ◽  
Long Distance ◽  
General Importance

In temporal lobe epilepsy, changes in synaptic and intrinsic properties occur on a background of altered network architecture resulting from cell loss and axonal sprouting. Although modeling studies using idealized networks indicated the general importance of network topology in epilepsy, it is unknown whether structural changes that actually take place during epileptogenesis result in hyperexcitability. To answer this question, we built a 1:1 scale structural model of the rat dentate gyrus from published in vivo and in vitro cell type–specific connectivity data. This virtual dentate gyrus in control condition displayed globally and locally well connected (“small world”) architecture. The average number of synapses between any two neurons in this network of over one million cells was less than three, similar to that measured for the orders of magnitude smaller C. elegans nervous system. To study how network architecture changes during epileptogenesis, long-distance projecting hilar cells were gradually removed in the structural model, causing massive reductions in the number of total connections. However, as long as even a few hilar cells survived, global connectivity in the network was effectively maintained and, as a result of the spatially restricted sprouting of granule cell axons, local connectivity increased. Simulations of activity in a functional dentate network model, consisting of over 50,000 multicompartmental single-cell models of major glutamatergic and GABAergic cell types, revealed that the survival of even a small fraction of hilar cells was enough to sustain networkwide hyperexcitability. These data indicate new roles for fractionally surviving long-distance projecting hilar cells observed in specimens from epilepsy patients.

The role of the dentate gyrus in mnemonic functions

Neuroforum ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas-Frederic Sauer ◽  
Marlene Bartos
Keyword(s):  
Dentate Gyrus ◽  
Memory Formation ◽  
Population Activity ◽  
Two Photon ◽  
Select Group ◽  
Cell Association ◽  
Mechanisms Of Memory ◽  
Input Gate

AbstractThe hippocampus is decisive for the storage of conscious memories. Current theories suggest that experience-dependent modifications in excitation–inhibition balance enable a select group of neurons to form a new cell association during learning which represents the new memory trace. It was further proposed that particularly GABAergic-inhibitory interneurons have a large impact on population activity in neuronal networks by means of their inhibitory output synapses. They synchronize active principal cells at high frequencies, thereby supporting their binding to cell assemblies to jointly encode information. However, how cell associations emerge in space and time and how interneurons may contribute to this process is still largely unknown. We started to address this fundamental question in the dentate gyrus (DG) as the input gate of the hippocampus, which has an indispensable role in conscious memory formation. We used a combination of in vivo chronic two-photon imaging of population activity in the DG and the hippocampal areas CA1–3 of mice exposed to a virtual reality, in which they perform a goal-oriented spatial memory tasks, with high-density in vivo recordings and multiple whole-cell recordings in acute slice preparations, to determine how memory engrams emerge during learning. We further examine how GABAergic interneurons may contribute to this process. We believe that these lines of research will add to a better understanding on the mechanisms of memory formation in cortical networks.

Sign in / Sign up

Export Citation Format

Share Document