Connectomics to Semantomics: Addressing the Brain's Big Data Challenge1

Arsiwalla, Xerxes D.; Dalmazzo, David; Zucca, Riccardo; Betella, Alberto; Brandi, Santiago; Martínez, Enrique Fernández; Omedas, Pedro; Verschure, Paul F. M. J.

doi:10.1016/j.procs.2015.07.278

Cited by 16 publications

(11 citation statements)

References 11 publications

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“…For this reason, we have embarked on a series of DAC control architectures for humanoid robots that in particular advance human-level embodied social interaction [226]. In order to realize its convergent validation, we have also integrated this humanoid platform with state-of-the-art human connectome models using brainx3 [181,182] that are augmented with fully simulated subcortical structures. DACtoc thus predicts that human-level quale parsing will result from the convergence of humanoid robotics and whole brain modelling following the convergent validation methodology.…”

Section: Discussionmentioning

confidence: 99%

Synthetic consciousness: the distributed adaptive control perspective

Verschure

2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

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Section: Discussionmentioning

confidence: 99%

Synthetic consciousness: the distributed adaptive control perspective

Verschure

2016

Phil. Trans. R. Soc. B

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“…1A. Building and visualizing functional networks was performed using the BrainX 3 platform 33, 42–44 .…”

Section: Methodsmentioning

confidence: 99%

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

Zucca

Arsiwalla

et al. 2019

Preprint

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Are degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tail's heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological s...

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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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Connectomics to Semantomics: Addressing the Brain's Big Data Challenge1

Cited by 16 publications

References 11 publications

Synthetic consciousness: the distributed adaptive control perspective

Synthetic consciousness: the distributed adaptive control perspective

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

The Morphospace of Consciousness

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