Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrateComment: 39 pages, 15 figures, to appear in PLOS Computational Biolog

Spontaneous eyeblinks during breaking continuous flash suppression are associated with increased detection times

An eyeblink has a clear effect on low-level information processing because it temporarily occludes all visual information. Recent evidence suggests that eyeblinks can also modulate higher level processes (e.g. attentional resources), and vice versa. Despite these putative effects on different levels of information processing, eyeblinks are typically neglected in vision and in consciousness research. The main aim of this study was to investigate the timing and the effect of eyeblinks in an increasingly popular paradigm in consciousness research, namely breaking continuous flash suppression (b-CFS). Results show that participants generally refrain from blinking during a trial, that is, when they need to detect a suppressed stimulus. However, when they do blink during a trial, we observed a sharp increase in suppression time. This suggests that one needs to control for blinking when comparing detection times between conditions that could elicit phasic changes in blinking.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Neural assemblies as core elements for modeling neural networks in the brain

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important role in information processing. Therefore, it is worthwhile to investigate modeling of neural networks. Experiments extract different kinds of datasets (ranging from pair-wise connectivity to membrane potential of individual neurons) and provide an understanding of neuronal activity. However, due to technical limitations of experiments, and complexity and variety of neural architectures, the experimental datasets do not yield a model of neural networks on their own. Roughly speaking, the experimental datasets are not enough for modeling neural networks. Therefore, in addition to these datasets, we have to utilize assumptions, hand-tuned features, parameter tuning and heuristic methods for modeling networks. In this thesis, we present different models of neural networks that are able to produce several behaviors observed in mammalian brain and cell cultures, e.g., up-state/down-state oscillations, different stimulus-evoked responses of cortical layers, activity propagation with tunable speed and several activity patterns of mice barrel cortex. An element which is embedded in all of these models is a network feature called neural assembly. A neural assembly is a group (also called population) of neurons with dense recurrent connectivity and strong internal synaptic weights. We study the dynamics of neural assemblies using analytical approaches and computer simulations. We show that network models containing assemblies exhibit dynamics similar to activity observed in the brain

Stany świadomości w świetle neuronauk

The human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity (optimal organization of different brain network associated with rapid information propagation, minimal wiring costs, as well as a balance between local processing and global integration) in which activity of Default Mode Network play important role in different states of consciousness. The small-world structure deviation reflects genuine changes in levels of consciousness: 1) coma state: increased activity of precuneus and posterior cingulate cortex and domination alpha band over theta and delta band connected with low activity of alpha, theta, and delta band, 2) vegetative state: increased connectivity in Default Mode Network and increased power in delta, theta, beta, and low alpha band, 3) state of minimal consciousness: decreased activation of the Default Mode Network during tasks, domination alpha band over theta and delta band connected with high activity of alpha, theta, and delta band, 4) meditation state: reorganization of central and peripheral elements of the brain network, changes in quantity, quality, orientation of longitudinal (antero-posterior) and transverse (right-left) edges, and its frequency of activity, increased longitudinal integration and hemispherical synchronization. Future neuroimaging investigations are needed. Especially the new sophisticated spectral metrics could be potentially effective in clinical setting and have diagnostic, therapeutic and prognostic relevance.Mózg ludzki może być rozumiany jako kompleksowa sieć, która na poziomie anatomicznym i funkcjonalnym ma strukturę „małych światów” (optymalna organizacja sieciowa aktywacji różnych elementów struktury mózgowej, która jest powiązana z wysoką szybkością propagacji informacji, niską podatnością na błędy i ataki oraz minimalizacją kosztów aktywacji neurofizjologicznej), ważną rolę odgrywa w niej sieć wzbudzeń podstawowych (jej aktywacja jest utrzymana we wszystkich stanach świadomości). Odchylenia od struktury małych światów znajdują odzwierciedlenie w istotnych zmianach poziomu świadomości, takich jak: 1) stan śpiączki: podwyższona aktywacja przedklinka i tylnej części zakrętu obręczy oraz dominacja aktywacji fal alfa nad delta i theta przy relatywnie niskim ich nasileniu, 2) stan wegetatywny: podwyższona koherencja sieci wzbudzeń podstawowych oraz podwyższone nasilenie aktywacji delta, theta, beta i niskich alfa, 3) stan minimalnej świadomości: spadki aktywacji sieci wzbudzeń podstawowych podczas wykonywania zadań oraz przewaga aktywacji fal alfa nad delta i theta przy relatywnie wysokim ich nasileniu, 4) stan medytacyjny: reorganizacja centralnych elementów sieciowych względem peryferyjnych, istotne zmiany w zakresie ilości, jakości, orientacji połączeń odległych podłużnych i poprzecznych oraz częstotliwości ich aktywacji, wzrost podłużnej integracji i hemisferycznej synchronizacji. Potrzebne są dalsze badania, szczególnie oparte o wyrafinowane narzędzia i metody badawcze, które mogą się istotnie przyczynić do poprawy trafności oraz efektywności diagnostycznej, terapeutycznej i prognostycznej

Exact neural mass model for synaptic-based working memory

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to get insight on Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are associated to stimulus-locked transient oscillations followed by a steady-state activity in the $\beta-\gamma$ band, thus resembling what observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the $\gamma$ power increases with the number of loaded items, as reported in many experiments, while $\theta$ and $\beta$ power reveal non monotonic behaviours.Comment: 47 pages, 14 figure

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure