Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality

What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system's dynamics reveals how informational complexity is governed by eigenmodes of both, the network's covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Beyond neural coding? Lessons from perceptual control theory

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms

The relation between cardiac 123I-mIBG scintigraphy and functional response 1 year after CRT implantation

Cardiac resynchronization therapy (CRT) is a disease-modifying therapy in patients with chronic heart failure (CHF). Current guidelines ascribe CRT eligibility on three parameters only: left ventricular ejection fraction (LVEF), QRS duration, and New York Heart Association (NYHA) functional class. However, one-third of CHF patients does not benefit from CRT. This study evaluated whether 123I-meta-iodobenzylguanidine (123I-mIBG) assessed cardiac sympathetic activity could optimize CRT patient selection

Switching operation modes in the neocortex via cholinergic neuromodulation

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty

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

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 system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes

Growing-up hand in hand with robots: Designing and evaluating child-robot interaction from a developmental perspective

Robots are becoming part of children's care, entertainment, education, social assistance and therapy. A steadily growing body of Human-Robot Interaction (HRI) research shows that child-robot interaction (CRI) holds promises to support children's development in novel ways. However, research has shown that technologies that do not take into account children's needs, abilities, interests, and developmental characteristics may have a limited or even negative impact on their physical, cognitive, social, emotional, and moral development. As a result, robotic technology that aims to support children via means of social interaction has to take the developmental perspective into consideration. With this workshop (the third of a series of workshops focusing CRI research), we aim to bring together researchers to discuss how a developmental perspective play a role for smart and natural interaction between robots and children. We invite participants to share their experiences on the challenges of taking the developmental perspective in CRI, such as long-term sustained interactions in the wild, involving children and other stakeholders in the design process and more. Looking across disciplinary boundaries, we hope to stimulate thought-provoking discussions on epistemology, methods, approaches, techniques, interaction scenarios and design principles focused on supporting children's development through interaction with robotic technology. Our goal does not only focus on the conception and formulation of the outcomes in the context of the workshop venue, but also on their establishment and availability for the HRI community in different forms