Brain Simulation

The human brain exhibits a relatively stable spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how and why local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our results highlight the importance of a periphery-tocore hierarchy to determine the effect of local stimulation on the brain network. We also provide novel resources to orient empirical work aiming at manipulating functional connectivity using noninvasive brain stimulation.

Questo lavoro esplora il campo delle neuroscienze computazionali e delle simulazioni cerebrali, con un focus sulle tecnologie emergenti, le applicazioni in ambito medico e le implicazioni etiche. Attraverso l'analisi delle neuroscienze, dell'intelligenza artificiale e delle biotecnologie, il documento esamina come i modelli matematici e le simulazioni possano migliorare la comprensione dei processi cognitivi, neurologici e comportamentali. Viene discusso l'uso di simulazioni cerebrali nella diagnosi e nel trattamento delle malattie neurologiche, con particolare attenzione a patologie come l'Alzheimer e il Parkinson. Inoltre, il lavoro affronta i rischi di abuso delle tecnologie, le sfide relative alla privacy e al controllo delle informazioni cerebrali, nonché le questioni morali e sociali legate all'utilizzo delle simulazioni cognitive. Infine, il paper considera le prospettive future di queste tecnologie, analizzando il loro impatto sulla medicina, sulla società e sull'etica scientifica.

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Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

The characterization of the coupling direction between brain regions is fundamental for disclosing brain functioning. To this end, several computational methods have been developed that exploit either the temporal or the spectral characteristics of electrophysiological signals measured by e.g. EEG and MEG. Among these methods, the Phase Slope Index (PSI) estimates the directionality of frequency-specific neural interactions by relying on the sine of the phase slopes of the complex coherencies between time series, which is just an approximation for small angles of the actual phase slopes. The purpose of our study is to: 1) build a directionality estimator, namely id  , which directly takes into account the non-approximated phase slopes; 2) assess the performance in estimating the coupling direction of PSI and id  in exhaustive simulations. Our findings show that while id  obtains better performance than PSI for the no noise case, a Signal-to-Noise Ratio equal or lower than one completely reverses the results in favour of PSI.

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics p...

The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, ...

Today, biomimetic approaches to unconventional computing have been exploring neurotransmitters' roles. But hormonal aspects of artificial cognition are still unexplored. And from a cognitive perspective, such a complex biochemical combination allows fundamental mechanisms of information processing, from abductive tasks to attention or intentionality. This paper will explore the theoretical ways by which hormonal computers could open a different way to create not only innovative machines but also programming ways of dealing with such bioinspiration.

The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, ...

Inspired by the function, power, and volume of the organic brain, we are developing TrueNorth, a novel modular, non-von Neumann, ultra-low power, compact architecture. TrueNorth consists of a scalable network of neurosynaptic cores, with each core containing neurons, dendrites, synapses, and axons. To set sail for TrueNorth, we developed Compass, a multi-threaded, massively parallel functional simulator and a parallel compiler that maps a network of long-distance pathways in the macaque monkey brain to TrueNorth. We demonstrate near-perfect weak scaling on a 16 rack IBM® Blue Gene®/Q (262144 CPUs, 256 TB memory), achieving an unprecedented scale of 256 million neurosynaptic cores containing 65 billion neurons and 16 trillion synapses running only 388× slower than real time with an average spiking rate of 8.1 Hz. By using emerging PGAS communication primitives, we also demonstrate 2× better real-time performance over MPI primitives on a 4 rack Blue Gene/P (16384 CPUs, 16 TB memory).

what is the mind? Neither scientists nor philosophers agree on a universal definition or specification. Colloquially, we understand the mind as a collection of processes of sensation, perception, action, emotion, and cognition. The mind can integrate ambiguous information from sight, hearing, touch, taste, and smell; it can form spatiotemporal associations and abstract concepts; it can make decisions and initiate sophisticated coordinated actions.

In this paper we present the following hypothesis: the neuromodulatory
mechanisms that control the emotional states of mammals could be translated and
re-implemented in a computer by means of controlling the computational performance
of a hosted computational system. In our specific implementation we represent
the simulation of the fear-like state based on the three dimensional
neuromodulatory model of affects (here the basic emotional inborn states) that we
have inherited from works of Hugo Lövheim. We have managed to simulate
1000 ms of work of the dopamine system using NEST Neural Simulation Tool and
the rat brain as the model. We also present the results of that simulation and
evaluate them to validate the overall correctness of our hypothesis.

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: (1) Blue Brain, used for particular simulations of the cortical column in hybrid models, and (2) Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler.

This is a brief overview of current cognitive computing research in neuromorphic computation. In recent times, cognitive computing has been actively explored in artificial intelligence and in different fields of neuroscience. Researchers have been studying possibilities to reproduce biological brain design in computer simulation for the past twenty years. Since 2008, a team of researchers at the IBM Almaden Center in association with scientists from five universities has been working on an ambitious project named SyNAPSE2
to create a brain-like architecture with the ultimate goal of constructing a unified computational theory of human cognition. The choice of computer simulation as a method is based on the “understanding by building” approach (Todd, 2012, p.ix) which assumes that human brain can be better studied through large-scale modeling of its neural networks.