Large-scale Brain Simulations Research Articles

Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification

The mechanical properties of human brain tissue remain far from being fully understood. One aspect that has gained more attention recently is their regional dependency, as the brain’s microstructure varies significantly from one region to another. Understanding the correlation between tissue components and the mechanical behavior is an important step toward better understanding how human brain tissue properties change in space and time and to develop highly spatially resolved constitutive models for large-scale brain simulations. Here, we analyze the correlation between human brain tissue components quantified through enzyme-linked immunosorbent assays (ELISA) and material parameters obtained through an inverse parameter identification scheme based on a hyperelastic Ogden model and multimodal mechanical testing data for eight regions of the brain. We use neural networks as a metamodel to save computational costs. The networks are trained on finite element simulation outputs and are able to replace the simulations in the initial optimization step. We identified strong dependencies between mechanical properties and Iba1 associated with microglia cells, collagen VI, GFAP associated with astrocytes, and collagen IV. These results advance our understanding of microstructure-mechanics relations in human brain tissue and will contribute to the development of highly spatially resolved microstructure-informed constitutive models.

Mean-field based framework for forward modeling of LFP and MEG signals.

The use of mean-field models to describe the activity of large neuronal populations has become a very powerful tool for large-scale or whole brain simulations. However, the calculation of brain signals from mean-field models, such as the electric and magnetic fields, is still under development. Thus, the emergence of new methods for an accurate and efficient calculation of such brain signals is currently of great relevance. In this paper we propose a novel method to calculate the local field potentials (LFP) and magnetic fields from mean-field models. The calculation of LFP is done via a kernel method based on unitary LFP's (the LFP generated by a single axon) that was recently introduced for spiking-networks simulations and that we adapt here for mean-field models. The calculation of the magnetic field is based on current-dipole and volume-conductor models, where the secondary currents (due to the conducting extracellular medium) are estimated using the LFP calculated via the kernel method and the effects of medium-inhomogeneities are incorporated. We provide an example of the application of our method for the calculation of LFP and MEG under slow-waves of neuronal activity generated by a mean-field model of a network of Adaptive-Exponential Integrate-and-Fire (AdEx) neurons. We validate our method via comparison with results obtained from the corresponding spiking neuronal networks. Finally we provide an example of our method for whole brain simulations performed with The Virtual Brain (TVB), a recently developed tool for large scale simulations of the brain. Our method provides an efficient way of calculating electric and magnetic fields from mean-field models. This method exhibits a great potential for its application in large-scale or whole-brain simulations, where calculations via detailed biological models are not feasible.

Distribution of spiking and bursting in Rulkov’s neuron model

Large-scale brain simulations require the investigation of large networks of realistic neuron models, usually represented by sets of differential equations. Here we report a detailed fine-scale study of the dynamical response over extended parameter ranges of a computationally inexpensive model, the two-dimensional Rulkov map, which reproduces well the spiking and spiking-bursting activity of real biological neurons. In addition, we provide evidence of the existence of nested arithmetic progressions among periodic pulsing and bursting phases of Rulkov’s neuron. We find that specific remarkably complex nested sequences of periodic neural oscillations can be expressed as simple linear combinations of pairs of certain basal periodicities. Moreover, such nested progressions are robust and can be observed abundantly in diverse control parameter planes which are described in detail. We believe such findings to add significantly to the knowledge of Rulkov neuron dynamics and to be potentially helpful in large-scale simulations of the brain and other complex neuron networks.

Visions of unification and integration: Building brains and communities in the European Human Brain Project

The Human Brain Project (HBP) was launched in October 2013 by the European Commission to build an information and communication technology infrastructure that would support large-scale brain modelling and simulation. Less than a year after its launch, more than 800 neuroscientists signed a letter that claimed the HBP ‘would fail to meet its goals’. Based on multi-sited ethnographic fieldwork conducted between February 2014 and January 2017 in France, Germany, the United Kingdom and the HBP headquarters in Switzerland, and over 40 interviews with scientists, engineers and project administrators, this article traces how competing visions over how brain models should be built became tied into debates over how scientific communities should be governed. Articulations of these different kinds of models and communities appealed to competing imaginaries of Europe itself – of Europe and European science as unified or pluralistic. This article argues that scientific models are sites of contestation over social and political futures. The tensions between visions of scientific unification and pluralism in the HBP mirrored the tensions between imaginaries of European political unification and pluralism.

Biological accuracy in large-scale brain simulations.

The advancement of computing technology makes it possible to build extremely accurate digital reconstructions of brain circuits. Are such unprecedented levels of biological accuracy essential for brain simulations to play the roles they are expected to play in neuroscientific research? The main goal of this paper is to clarify this question by distinguishing between various roles played by large-scale simulations in contemporary neuroscience, and by reflecting about what makes a simulation biologically accurate. It is argued that large-scale simulations may play model-oriented and prediction-oriented roles in brain research, and that the concept of biological accuracy can be interpreted as related to the plausibility of the theoretical model implemented in the simulation system, to the accuracy of the computer implementation, and to the level of details of the implemented model. Building on these observations and distinctions, it is argued that biological accuracy is not essential for a computer simulation to play the epistemic roles it is expected to play in brain research.

Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain Simulation

Establishing a connection between intrinsic and task-evoked brain activities is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from nontask neural elements while giving us the effects of task execution on nontask regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step toward elucidating the intrinsic versus task-evoked connection. In this study we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our large-scale neural modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks. We used the simulated blood oxygen level-dependent functional magnetic resonance imaging time series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain FC during task execution. Our results represent a step toward establishing a connection between intrinsic and task-related brain activities.

Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues.

Modeling and simulations have gained a leading position in contemporary attempts to describe, explain, and quantitatively predict the human brain’s operations. Computer models are highly sophisticated tools developed to achieve an integrated knowledge of the brain with the aim of overcoming the actual fragmentation resulting from different neuroscientific approaches. In this paper we investigate the plausibility of simulation technologies for emulation of consciousness and the potential clinical impact of large-scale brain simulation on the assessment and care of disorders of consciousness (DOCs), e.g., Coma, Vegetative State/Unresponsive Wakefulness Syndrome, Minimally Conscious State. Notwithstanding their technical limitations, we suggest that simulation technologies may offer new solutions to old practical problems, particularly in clinical contexts. We take DOCs as an illustrative case, arguing that the simulation of neural correlates of consciousness is potentially useful for improving treatments of patients with DOCs.

Human Brain inspired Artificial Intelligence & Developmental Robotics: A Review

Along with the developments in the field of the robotics, fascinating contributions and developments can be seen in the field of Artificial intelligence (AI). In this paper we will discuss about the developments is the field of artificial intelligence focusing learning algorithms inspired from the field of Biology, particularly large scale brain simulations, and developmental Psychology. We will focus on the emergence of the Developmental robotics and its significance in the field of AI.

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered.

Why build a virtual brain? Large-scale neural simulations as jump start for cognitive computing

Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates this claim and argues that the main challenge this era is facing is not the lack of biological realism. The challenge lies in identifying general neurocomputational principles for the design of artificial systems, which could display the robust flexibility characteristic of biological intelligence.

Large-scale brain dynamics: effect of connectivity resolution

Large-scale brain dynamics recently started to be modeled numerically based on both heterogeneous large-scale networks build from diffusion MRI, that is, a connectome, and local homogeneous connectivity kernel representing intracortical synaptic connections. However, topological properties of a connectome can significantly change with resolution and parcellation [1]. Furthermore, the sampling of the cerebral surfaces, resulting in a geometric model, and, in this way, the local connectivity kernel play crucial roles in the formation of spatial patterns on the cerebral surfaces as well as on the sensor level (e.g., EEG electrodes) by a forward calculation [2]. However, the effect of sampling and parcellation on modeling brain dynamics has not been studied so far. Here, we investigate qualitatively and quantitatively: (i) how different parcellation resolutions affect the dynamics of the network; and (ii) how the local connectivity affects the network dynamics. To do so, we used the neuroinformatics platform for large-scale brain simulations, called The Virtual Brain (TVB) [3] and developed a preprocessing pipeline to incorporate experimental data (e.g., structural MRI, diffusion-weighted MRI) in TVB [4]. We prepared ten individual models based on ten randomly selected subjects from the Human Connectome Project dataset [5]. For each individual model we performed simulations under two conditions during rest: (i) noise driven, using a bistable neural mass model, and (ii) after stimulation, using an excitable neural mass model. We investigated the effect of heterogeneous and homogeneous connectivity on large-scale brain dynamics by different numbers of regions in the parcellation (70 to 2240) and by varying the local connectivity coupling strength. To introduce experimental data (i.e., structural and diffusion MRI) into TVB we tackled issues such as surface downsampling (for achieving moderate simulation times) and mapping between surface and parcellation (to consistently use heterogeneous and homogeneous connectivity) by developing the Surface and Connectivity Reconstruction with an Imaging Pipeline for TVB Simulations, short SCRIPTS [4]. When considering slow dynamics, the major fiber bundles best reflected in the coarsest parcellation appeared to be mainly responsible for the emergence of the network attractors with limited changes over different parcellations and different local coupling strengths. For fast dynamics, new qualitative solutions appeared, but only in the presence of delays. Figure 1 A. Spatial attractors. For each value of the global (G) or local (G_local) coupling parameter, correlation with in-strength (blue points) and with s-core (red points) for ten different initial conditions. The blue square indicates the critical interval. ...

Biological Neural Network Structure and Spike Activity Prediction Based on Multi-Neuron Spike Train Data

The micro-scale neural network structure for the brain is essential for the investigation on the brain and mind. Most of the previous studies typically acquired the neural network structure through brain slicing and reconstruction via nanoscale imaging. Nevertheless, this method still cannot scale well, and the observation on the neural activities based on the reconstructed neural network is not possible. Neuron activities are based on the neural network of the brain. In this paper, we propose that multi-neuron spike train data can be used as an alternative source to predict the neural network structure. And two concrete strategies for neural network structure prediction based on such kind of data are introduced, namely, the time-ordered strategy and the spike co-occurrence strategy. The proposed methods can even be applied to in vivo studies since it only requires neural spike activities. Based on the predicted neural network structure and the spreading activation theory, we propose a spike prediction method. For neural network structure reconstruction, the experimental results reveal a significantly improved accuracy compared to previous network reconstruction strategies, such as Cross-correlation, Pearson, and the Spearman method. Experiments on the spikes prediction results show that the proposed spreading activation based strategy is potentially effective for predicting neural spikes in the biological neural network. The predictions on the neural network structure and the neuron activities serve as foundations for large scale brain simulation and explorations of human intelligence.

Selected Abstracts From the 2013 International Neuroethics Society Annual Meeting

Selected Abstracts From the 2013 International Neuroethics Society Annual Meeting

Linking brain behavior to underlying cellular mechanisms via large-scale brain modeling and simulation

To understand brain behaviors, it is important to directly associate the network level activities to the underlying biophysical mechanisms, which require large-scale simulations with biophysically realistic neural models like Hodgkin–Huxley models. However, when simulations are conducted on models with sufficient biophysical details, great challenges arise from limited computer power, thereby restricting most existing computational works with biophysical models only to small-scale networks. On the other hand, with the emergence of powerful computing platforms, many recent works are geared to performing large-scale simulations with simple spiking models. However, the applicability of those works is limited by the nature of the underlying phenomenological model. To bridge the gap, an intermediate step is taken to construct a scalable brain model with sufficient biophysical details. In this work, great efforts are devoted to taking into account not only local cortical microcircuits but also the global brain architecture, and efficient techniques are proposed and adopted to address the associated computational challenges in simulation of networks of such complexity. With the customized simulator developed, we are able to simulate the brain model to generate not only sleep spindle and delta waves but also the spike-and-wave pattern of absence seizures, and directly link those behaviors to underlying biophysical mechanism. Those initial results are interesting because they show the possibility to determine underlying causes of diseases by simulating the biologically realistic brain model. With further development, the work is geared to assisting the clinicians in selecting the optimal treatment on an individual basis in the future.

A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

A number of leading cognitive architectures that are inspired by the human brain, at various levels of granularity, are reviewed and compared, with special attention paid to the way their internal structures and dynamics map onto neural processes. Four categories of Biologically Inspired Cognitive Architectures (BICAs) are considered, with multiple examples of each category briefly reviewed, and selected examples discussed in more depth: primarily symbolic architectures (e.g. ACT-R), emergentist architectures (e.g. DeSTIN), developmental robotics architectures (e.g. IM-CLEVER), and our central focus, hybrid architectures (e.g. LIDA, CLARION, 4D/RCS, DUAL, MicroPsi, and OpenCog). Given the state of the art in BICA, it is not yet possible to tell whether emulating the brain on the architectural level is going to be enough to allow rough emulation of brain function; and given the state of the art in neuroscience, it is not yet possible to connect BICAs with large-scale brain simulations in a thoroughgoing way. However, it is nonetheless possible to draw reasonably close function connections between various components of various BICAs and various brain regions and dynamics, and as both BICAs and brain simulations mature, these connections should become richer and may extend further into the domain of internal dynamics as well as overall behavior.

A world survey of artificial brain projects, Part I: Large-scale brain simulations

Driven by rapid ongoing advances in computer hardware, neuroscience and computer science, Artificial Brain research and development are blossoming. This article constitutes the first half of a two-part world survey of artificial brain projects: this part dealing with large-scale brain simulations, and the second part with biologically inspired cognitive architectures (BICAs). The large-scale brain simulations we consider in depth here include those by Markram, Modha, Boahen, Horwitz, Edelman, Izhikevich, and Just. As well as reviewing the particulars of these simulation projects, we position them in a broader perspective, comparing at the different underlying definitions of the concept of “simulation,” noting that in many ways the projects are modeling neurosystems at different levels as well as using different methodologies.

MMINi-DASS - Large-scale brain circuit construction and simulation for interconnectivity prediction

Event Abstract Back to Event MMINi-DASS - Large-scale brain circuit construction and simulation for interconnectivity prediction Venkateswaran Nagarajan1*, Karthik Srinivasan1, Ashutosh Mohan1, Vijay Daniel1, Vijay R1, Harish Chandran1 and Vignesh J1 1 Waran Research Foundation, India The MMINi-DASS framework[1,2] starts off with an arbitrary biological neural network which is statistically correct for a given brain region and a set of desired dynamics in the form of fMRI BOLD images. The constructed network is then simulated using computationally efficient and realistic models on a custom-made and event-driven simulator. Some models have been designed exclusively for the project while others are optimized implementations of existing models. The dynamics obtained from the simulation are then analyzed during run-time and compared with the desired dynamics by a Simulated Annealing Optimization engine. This then makes changes to the structure of the constructed network and invokes the simulation again, thus starting another loop. Evidently, the most interesting aspect of the MMINi-DASS project is the possibility of gaining structural, biophysical, biochemical and electrical knowledge from merely knowledge of the dynamics of a region initially. Since the simulation is large-scale while also being detailed, the simulator is optimized for handling the large number of generated events efficiently. A Linux-based MPI (Message Passing Interface) cluster is setup. To handle the large amount of data generated, dedicated database architecture is used. The whole system including the API (Application Programming Interface), simulator and analyzer are generic and are based on XML specifications. This eliminates the need to rebuild or reconfigure the system each time the logic for circuit building, models or analysis is modified. A change in the XML specifications automatically ripples throughout the system. Among the most important problems for neuronal network simulations on a cluster are scalability, performance evaluation and improvement. The MMINi-DASS project uses a custom-made Performance Evaluator. This, while measuring the performance and resource utilization during run-time, also provides pointers to how performance scaling can be made linear with respect to problem size and architectural scaling within the current context and limitations. In the context of neuronal simulations, performance evaluation packages like HPL don’t provide accurate estimates. Thus, efforts are underway to use the BENSIM benchmarking package developed in-house for a realistic estimate of efficiency of both the implementation and the architecture. tn_Figure 1 Figure 1

Language models based on Hebbian cell assemblies

This paper demonstrates how associative neural networks as standard models for Hebbian cell assemblies can be extended to implement language processes in large-scale brain simulations. To this end the classical auto- and hetero-associative paradigms of attractor nets and synfire chains (SFCs) are combined and complemented by conditioned associations as a third principle which allows for the implementation of complex graph-like transition structures between assemblies. We show example simulations of a multiple area network for object-naming, which categorises objects in a visual hierarchy and generates different specific syntactic motor sequences ("words") in response. The formation of cell assemblies due to ongoing plasticity in a multiple area network for word learning is studied afterwards. Simulations show how assemblies can form by means of percolating activity across auditory and motor-related language areas, a process supported by rhythmic, synchronized propagating waves through the network. Simulations further reproduce differences in own EEG&MEG experiments between responses to word- versus non-word stimuli in human subjects.