The Dynamic Connectome: A Tool For Large-Scale 3D Reconstruction Of Brain Activity In Real-Time

Arsiwalla, Xerxes D.; Betella, Alberto; Bueno, Enrique Martínez; Omedas, Pedro; Zucca, Riccardo; Verschure, Paul F. M. J.

doi:10.7148/2013-0865

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…Additionally each neuronal population module is stochastic, having Gaussian noise. This was demonstrated in earlier work (Arsiwalla et al, 2013 ). The non-linear model is similar to above except that the linear-threshold filter is replaced by a sigmoidal filter with decay.…”

Section: Methodssupporting

confidence: 81%

“…can simulate large neuronal systems up to 500k neurons and connections and can be directly interfaced to external sensors and effectors. In order to enable real-time user interaction with the reconstructed data, user input from Unity is sent to (Arsiwalla et al, 2013 ). The neuronal simulator computes the processes and broadcasts the output of the simulation back to the Unity engine in the XIM.…”

Section: Methodsmentioning

confidence: 99%

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Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Arsiwalla

Zucca

Betella

et al. 2015

Front. Neuroinform.

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BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

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Section: Methodssupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Arsiwalla

Zucca

Betella

et al. 2015

Front. Neuroinform.

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“…The data used for this computation consists of 998 voxels with approximately 28,000 weighted symmetric connections (Hagmann et al, 2008 ). Arsiwalla and Verschure ( 2016b ) show that the topology and dynamics of the healthy human brain in the resting-state generates greater information complexity than a weight-preserving random rewiring of the same network (for network dynamics of the connectome refer to Arsiwalla et al, 2013 ; Betella et al, 2014 ; Arsiwalla et al, 2015a , b ). While this formulation of integrated information indeed works for very large networks, it is still limited to linearized dynamics.…”

Section: Measures Of Integrated Informationmentioning

confidence: 99%

Measuring the Complexity of Consciousness

Arsiwalla

Verschure

2018

Front. Neurosci.

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The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

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“…Notably the work of [15] is of particular significance in the context of this discussion as it develops large-scale network computations of integrated information, applied to the human brain's connectome data. The human connectome data consists of structural connectivity of white matter fiber tracts in the cerebral cortex, extracted using diffusion spectrum imaging and tractography [41], [46] (see [4], [16], [5] for neurodynamical models used on this network). Compared to a randomly re-wired network, it was seen that the particular topology of the human brain generates greater information complexity for all allowed couplings associated to the network's attractor states, as well as to its non-stationary dynamical states [15].…”

Section: Measures Of Consciousnessmentioning

confidence: 99%

The Morphospace of Consciousness

Arsiwalla¹,

Moulin-Frier²,

Herreros³

et al. 2017

Preprint

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In this paper, we construct a complexity-based morphospace wherein one can study systems-level properties of conscious and intelligent systems based on information-theoretic measures. The axes of this space labels three distinct complexity types, necessary to classify conscious machines, namely, autonomous, cognitive and social complexity. In particular, we use this morphospace to compare biologically conscious agents ranging from bacteria, bees, C. elegans, primates and humans with artificially intelligence systems such as deep networks, multi-agent systems, social robots, AI applications such as Siri and computational systems as Watson. Given recent proposals to synthesize consciousness, a generic complexitybased conceptualization provides a useful framework for identifying defining features of distinct classes of conscious and synthetic systems. Based on current clinical scales of consciousness that measure cognitive awareness and wakefulness, this article takes a perspective on how contemporary artificially intelligent machines and synthetically engineered life forms would measure on these scales. It turns out that awareness and wakefulness can be associated to computational and autonomous complexity respectively. Subsequently, building on insights from cognitive robotics, we examine the function that consciousness serves, and argue the role of consciousness as an evolutionary game-theoretic strategy. This makes the case for a third type of complexity necessary for describing consciousness, namely, social complexity. Having identified these complexity types, allows for a representation of both, biological and synthetic systems in a common morphospace. A consequence of this classification is a taxonomy of possible conscious machines. In particular, we identify four types of consciousness, based on embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii) group consciousness (resulting from group interactions), and (iv) simulated consciousness (embodied by virtual agents within a simulated reality). This taxonomy helps in the investigation of comparative signatures of consciousness across domains, in order to highlight design principles necessary to engineer conscious machines. This is particularly relevant in the light of recent developments at the arXiv:1705.11190v3 [q-bio.NC] 24 Nov 2018The Morphospace of Consciousness 2 crossroads of cognitive neuroscience, biomedical engineering, artificial intelligence and biomimetics.

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The Dynamic Connectome: A Tool For Large-Scale 3D Reconstruction Of Brain Activity In Real-Time

Cited by 15 publications

References 11 publications

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Measuring the Complexity of Consciousness

The Morphospace of Consciousness

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