arXiv subjects

Gustavo Deco

Publications and source records attributed to Gustavo Deco.

29 records · Page 2Linked to original sources

arXiv2020

Generative embeddings of brain collective dynamics using variational autoencoders

We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We used variational autoencoders (VAE) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations resulted in an embedding that represented the whole repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first followed this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two endpoints of this trajectory; next, we showed that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology

q-bio.NC↗

arXiv2020

The effect of noise on the synchronization dynamics of the Kuramoto model on a large human connectome graph

We have extended the study of the Kuramoto model with additive Gaussian noise running on the KKI-18 large human connectome graph. We determined the dynamical behavior of this model by solving it numerically in an assumed homeostatic state, below the synchronization crossover point we determined previously. The de-synchronization duration distributions exhibit power-law tails, characterized by the exponent in the range $1.1 < τ_t < 2$, overlapping the in vivo human brain activity experiments by Palva et al. We show that these scaling results remain valid, by a transformation of the ultra-slow eigen-frequencies to Gaussian with unit variance. We also compare the connectome results with those, obtained on a regular cube with $N=10^6$ nodes, related to the embedding space, and show that the quenched internal frequencies themselves can cause frustrated synchronization scaling in an extended coupling space.

nlin.AO↗

arXiv2019

Modeling the relationship between regional activation and functional connectivity during wakefulness and sleep

Global brain activity self-organizes into discrete patterns characterized by distinct behavioral observables and modes of information processing. The human thalamocortical system is a densely connected network where local neural activation reciprocally influences coordinated collective dynamics. We introduce a semi-empirical model to investigate the relationship between regional activation and long-range functional connectivity in the different brain states visited during the natural wake-sleep cycle. Our model combines functional magnetic resonance imaging (fMRI) data, in vivo estimates of structural connectivity, and anatomically-informed priors that constrain the independent variation of regional activation. As expected, priors based on functionally coherent networks resulted in the best fit between empirical and simulated brain activity. We show that sleep progressively divided the cortex into regions presenting opposite dynamical behavior: frontoparietal regions approached a bifurcation towards local oscillatory dynamics, while sensorimotor regions presented stable dynamics governed by noise. In agreement with human electrophysiological experiments, sleep onset induced subcortical deactivation and uncoupling, which was subsequently reversed for deeper stages. Finally, we introduced external forcing of variable intensity to simulate external perturbations, and identifiedthe key regionsespecially relevant for the recovery of wakefulness from deep sleep. Our model represents sleep as a state where long-range decoupling and regional deactivation coexist with the latent capacity for a rapid transition towards wakefulness. The mechanistic insights provided by our simulations allow the in silico parametric exploration of such transitions in terms of external perturbations, with potential applications for the control of physiological and pathological brain states.

q-bio.NC↗

arXiv2018

Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour

This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.

cs.AI↗

arXiv2018

A novel framework to analyze complex network dynamics

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagation-like processes such as random walks. However, networks are often inferred from real data forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the \emph{flow} that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a new way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

physics.soc-ph↗

arXiv2016

How structure sculpts function: unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

Intrinsic brain activity is characterized by highly structured co-activations between different regions, whose origin is still under debate. In this paper, we address the question whether it is possible to unveil how the underlying anatomical connectivity shape the brain's spontaneous correlation structure. We start from the assumption that in order for two nodes to exhibit large covariation, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along an unique route, but rather travels along all possible paths. In real networks the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. We use these notions to derive a novel analytical measure, $\mathcal{T}$ , which quantifies the similarity of the whole-network input patterns arriving at any two nodes only due to the underlying topology, in what is a generalization of the matching index. We show that this measure of topological similarity can indeed be used to predict the contribution of network topology to the expected correlation structure, thus unveiling the mechanism behind the tight but elusive relationship between structure and function in complex networks. Finally, we use this measure to investigate brain connectivity, showing that information about the topology defined by the complex fabric of brain axonal pathways specifies to a large extent the time-average functional connectivity observed at rest.

q-bio.NC↗

arXiv2016

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

q-bio.NC↗

arXiv2015

Distinct criticality of phase and amplitude dynamics in the resting brain

Converging research suggests that the resting brain operates at the cusp of dynamic instability signified by scale-free temporal correlations. We asked if the scaling properties of these correlations differ between amplitude and phase fluctuations, which may reflect different aspects of cortical functioning. Using source-reconstructed magneto-encephalographic signals, we found power-law scaling for the collective amplitude and for phase synchronization, both capturing whole-brain activity. The temporal changes of the amplitude comprise slow, persistent memory processes, whereas phase synchronization exhibits less temporally structured and more complex correlations, indicating a fast and flexible coding. This distinct temporal scaling supports the idea of different roles of amplitude and phase in cortical functioning.

q-bio.NC↗

arXiv2015

Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them have been variously recognized in previous works as expressions of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of their determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed `quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

q-bio.NC↗

arXiv2015

Task-driven intra- and interarea communications in primate cerebral cortex

Neural correlations during a cognitive task are central to study brain information processing and computation. However, they have been poorly analyzed due to the difficulty of recording simultaneous single neurons during task performance. In the present work, we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task. Upon modeling spike trains as binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making, and motor report. We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals. Specifically, information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor, and motor areas when decision making takes place. Crucially, when sensory comparison is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all cortical areas.

q-bio.NC↗

arXiv2013

Complexity Reduction of Rate-Equations Models for Two-Choice Decision-Making

We are concerned with the complexity reduction of a stochastic system of differential equations governing the dynamics of a neuronal circuit describing a decision-making task. This reduction is based on the slow-fast behavior of the problem and holds on the whole phase space and not only locally around the spontaneous state. Macroscopic quantities, such as performance and reaction times, computed applying this reduction are in agreement with previous works in which the complexity reduction is locally performed at the spontaneous point by means of a Taylor expansion.

math.AP↗