700 research outputs found
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions
Quantum correlations and synchronization measures
The phenomenon of spontaneous synchronization is universal and only recently
advances have been made in the quantum domain. Being synchronization a kind of
temporal correlation among systems, it is interesting to understand its
connection with other measures of quantum correlations. We review here what is
known in the field, putting emphasis on measures and indicators of
synchronization which have been proposed in the literature, and comparing their
validity for different dynamical systems, highlighting when they give similar
insights and when they seem to fail.Comment: book chapter, 18 pages, 7 figures, Fanchini F., Soares Pinto D.,
Adesso G. (eds) Lectures on General Quantum Correlations and their
Applications. Quantum Science and Technology. Springer (2017
Persistent homology of time-dependent functional networks constructed from coupled time series
We use topological data analysis to study "functional networks" that we
construct from time-series data from both experimental and synthetic sources.
We use persistent homology with a weight rank clique filtration to gain
insights into these functional networks, and we use persistence landscapes to
interpret our results. Our first example uses time-series output from networks
of coupled Kuramoto oscillators. Our second example consists of biological data
in the form of functional magnetic resonance imaging (fMRI) data that was
acquired from human subjects during a simple motor-learning task in which
subjects were monitored on three days in a five-day period. With these
examples, we demonstrate that (1) using persistent homology to study functional
networks provides fascinating insights into their properties and (2) the
position of the features in a filtration can sometimes play a more vital role
than persistence in the interpretation of topological features, even though
conventionally the latter is used to distinguish between signal and noise. We
find that persistent homology can detect differences in synchronization
patterns in our data sets over time, giving insight both on changes in
community structure in the networks and on increased synchronization between
brain regions that form loops in a functional network during motor learning.
For the motor-learning data, persistence landscapes also reveal that on average
the majority of changes in the network loops take place on the second of the
three days of the learning process.Comment: 17 pages (+3 pages in Supplementary Information), 11 figures in many
text (many with multiple parts) + others in SI, submitte
Markers of criticality in phase synchronization
The concept of the brain as a critical dynamical system is very attractive because systems close to criticality are thought to maximize their dynamic range of information processing and communication. To date, there have been two key experimental observations in support of this hypothesis: (i) neuronal avalanches with power law distribution of size and (ii) long-range temporal correlations (LRTCs) in the amplitude of neural oscillations. The case for how these maximize dynamic range of information processing and communication is still being made and because a significant substrate for information coding and transmission is neural synchrony it is of interest to link synchronization measures with those of criticality. We propose a framework for characterizing criticality in synchronization based on an analysis of the moment-to-moment fluctuations of phase synchrony in terms of the presence of LRTCs. This framework relies on an estimation of the rate of change of phase difference and a set of methods we have developed to detect LRTCs. We test this framework against two classical models of criticality (Ising and Kuramoto) and recently described variants of these models aimed to more closely represent human brain dynamics. From these simulations we determine the parameters at which these systems show evidence of LRTCs in phase synchronization. We demonstrate proof of principle by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated. The existence of LRTCs in fluctuations of phase synchronization suggests that these fluctuations are governed by non-local behavior, with all scales contributing to system behavior. This has important implications regarding the conditions under which one should expect to see LRTCs in phase synchronization. Specifically, brain resting states may exhibit LRTCs reflecting a state of readiness facilitating rapid task-dependent shifts toward and away from synchronous states that abolish LRTCs
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Couple and family therapies for post-traumatic stress disorder (PTSD)
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: The objectives of this review will be to: assess the efficacy of couple and family therapies for adult PTSD, relative to 'no treatment' conditions, 'standard care', and structured or non‐specific individual psychological therapies; examine the clinical characteristics of studies that influence the relative efficacy of these therapies; and critically evaluate methodological features of studies that bias research findings
Model-agnostic network inference enhancement from noisy measurements via curriculum learning
Noise is a pervasive element within real-world measurement data,
significantly undermining the performance of network inference models. However,
the quest for a comprehensive enhancement framework capable of bolstering noise
resistance across a diverse array of network inference models has remained
elusive. Here, we present an elegant and efficient framework tailored to
amplify the capabilities of network inference models in the presence of noise.
Leveraging curriculum learning, we mitigate the deleterious impact of noisy
samples on network inference models. Our proposed framework is model-agnostic,
seamlessly integrable into a plethora of model-based and model-free network
inference methods. Notably, we utilize one model-based and three model-free
network inference methods as the foundation. Extensive experimentation across
various synthetic and real-world networks, encapsulating diverse nonlinear
dynamic processes, showcases substantial performance augmentation under varied
noise types, particularly thriving in scenarios enriched with clean samples.
This framework's adeptness in fortifying both model-free and model-based
network inference methodologies paves the avenue towards a comprehensive and
unified enhancement framework, encompassing the entire spectrum of network
inference models. Available Code: https://github.com/xiaoyuans/MANIE
Untangling cross-frequency coupling in neuroscience
Cross-frequency coupling (CFC) has been proposed to coordinate neural
dynamics across spatial and temporal scales. Despite its potential relevance
for understanding healthy and pathological brain function, the standard CFC
analysis and physiological interpretation come with fundamental problems. For
example, apparent CFC can appear because of spectral correlations due to common
non-stationarities that may arise in the total absence of interactions between
neural frequency components. To provide a road map towards an improved
mechanistic understanding of CFC, we organize the available and potential novel
statistical/modeling approaches according to their biophysical
interpretability. While we do not provide solutions for all the problems
described, we provide a list of practical recommendations to avoid common
errors and to enhance the interpretability of CFC analysis.Comment: 47 pages, 12 figures, including supplementary materia
Capturing interpersonal coordination processes in association football : from dyads to collectives
Doutoramento em Motricidade Humana, na especialidade de Ciências do DesportoThe purpose of this thesis was to investigate how football performers coordinate their
behaviours in different levels of social organisation. We began with a position paper
proposing the re-conceptualisation of sport teams as functional integrated
superorganisms to frame a deeper understanding of the interpersonal coordination
processes emerging between team players. Time-motion analysis procedures and
innovative tools were developed and presented in order to capture the
superorganismic properties of sports teams and the interpersonal coordination
tendencies developed by players. These tendencies were captured and analysed in
representative 1vs1 and 3vs3 sub-phases, as well as in the 11-a-side game format. Data
showed higher levels of variability at the individual level compared to the team level.
This finding suggested that micro-variability may contribute to stabilise the
behavioural dynamics at the collective level. Moreover, the specificities of the
interpersonal coordination tendencies displayed within attacking-defending dyads
demonstrated to have influenced the performance outcome. Attacking players tend to
succeed when they were more synchronised in space and time with the defenders, and
their interaction were more unpredictable/irregular. Besides, the time-evolving
dynamics of the collective behaviours (i.e., at 11-a-side level) during competitive
football performance indicated a tendency for an increase in the predictability (i.e.,
more regularity). These data were interpreted as evidencing co-adaptation processes
between opponent players, which suggest that team players may shift from prevalent
explorative and irregular behaviours to more predictable behaviours emerging due
changes in their functional movement possibilities. However, some game events such
as goals scored, halftime and stoppages in play seemed to break this continuum and
acted as relevant performance constraints.FCT - Fundação para Ciência e a Tecnologi
- …