The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.

We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset.

Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions.

The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves.

Triadic percolation induces dynamical topological patterns in higher-order networks.

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos.

Antimicrobial Resistance of Helicobacter pylori Isolated From Latin American Children and Adolescents (2008-2023): A Systematic Review.

Background: Latin America has a high prevalence of Helicobacter pylori in children that may lead to peptic ulcer disease and eventually gastric cancer in adulthood. Successful eradication is hindered by rising antimicrobial resistance. We summarize H.

Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks.

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements.

Physics Clues on the Mind Substrate and Attributes.

The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings.

EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness.

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases.

Growth strategy determines the memory and structural properties of brain networks.

The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories.

Explosive Higher-Order Kuramoto Dynamics on Simplicial Complexes.

The higher-order interactions of complex systems, such as the brain, are captured by their simplicial complex structure and have a significant effect on dynamics. However, the existing dynamical models defined on simplicial complexes make the strong assumption that the dynamics resides exclusively on the nodes. Here we formulate the higher-order Kuramoto model which describes the interactions between oscillators placed not only on nodes but also on links, triangles, and so on.

Chimera states in hybrid coupled neuron populations.

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.

How Memory Conforms to Brain Development.

Nature exhibits countless examples of , whose topology evolves constantly coupled with the activity due to its function. The brain is an illustrative example of a system in which a dynamic complex network develops by the generation and pruning of synaptic contacts between neurons while memories are acquired and consolidated. Here, we consider a recently proposed brain developing model to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes.

Synchronization in network geometries with finite spectral dimension.

Recently there is a surge of interest in network geometry and topology. Here we show that the spectral dimension plays a fundamental role in establishing a clear relation between the topological and geometrical properties of a network and its dynamics. Specifically we explore the role of the spectral dimension in determining the synchronization properties of the Kuramoto model.

Synchronization-induced spike termination in networks of bistable neurons.

We observe and study a self-organized phenomenon whereby the activity in a network of spiking neurons spontaneously terminates. We consider different types of populations, consisting of bistable model neurons connected electrically by gap junctions, or by either excitatory or inhibitory synapses, in a scale-free connection topology. We find that strongly synchronized population spiking events lead to complete cessation of activity in excitatory networks, but not in gap junction or inhibitory networks.

Complex Network Geometry and Frustrated Synchronization.

The dynamics of networks of neuronal cultures has been recently shown to be strongly dependent on the network geometry and in particular on their dimensionality. However, this phenomenon has been so far mostly unexplored from the theoretical point of view. Here we reveal the rich interplay between network geometry and synchronization of coupled oscillators in the context of a simplicial complex model of manifolds called Complex Network Manifold.

Inverse stochastic resonance in networks of spiking neurons.

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.

Double inverse stochastic resonance with dynamic synapses.

We investigate the behavior of a model neuron that receives a biophysically realistic noisy postsynaptic current based on uncorrelated spiking activity from a large number of afferents. We show that, with static synapses, such noise can give rise to inverse stochastic resonance (ISR) as a function of the presynaptic firing rate. We compare this to the case with dynamic synapses that feature short-term synaptic plasticity and show that the interval of presynaptic firing rate over which ISR exists can be extended or diminished.

Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems.

In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world.

Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons.

In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations.

Brain Performance versus Phase Transitions.

We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation.

Efficient transmission of subthreshold signals in complex networks of spiking neurons.

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances--that naturally balances the network with excitatory and inhibitory synapses--and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase.

Emerging phenomena in neural networks with dynamic synapses and their computational implications.

In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilization of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli.

Robust short-term memory without synaptic learning.

Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds).

Stochastic resonance crossovers in complex networks.

Here we numerically study the emergence of stochastic resonance as a mild phenomenon and how this transforms into an amazing enhancement of the signal-to-noise ratio at several levels of a disturbing ambient noise. The setting is a cooperative, interacting complex system modelled as an Ising-Hopfield network in which the intensity of mutual interactions or "synapses" varies with time in such a way that it accounts for, e.g.

Enhancing neural-network performance via assortativity.

The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations--assortativity--on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved.