10 research outputs found
Critical synchronization dynamics of the Kuramoto model on connectome and small world graphs
The hypothesis, that cortical dynamics operates near criticality also
suggests, that it exhibits universal critical exponents which marks the
Kuramoto equation, a fundamental model for synchronization, as a prime
candidate for an underlying universal model. Here, we determined the
synchronization behavior of this model by solving it numerically on a large,
weighted human connectome network, containing 804092 nodes, in an assumed
homeostatic state. Since this graph has a topological dimension , a real
synchronization phase transition is not possible in the thermodynamic limit,
still we could locate a transition between partially synchronized and
desynchronized states. At this crossover point we observe power-law--tailed
synchronization durations, with , away from experimental
values for the brain. For comparison, on a large two-dimensional lattice,
having additional random, long-range links, we obtain a mean-field value:
. However, below the transition of the connectome we
found global coupling control-parameter dependent exponents ,
overlapping with the range of human brain experiments. We also studied the
effects of random flipping of a small portion of link weights, mimicking a
network with inhibitory interactions, and found similar results. The
control-parameter dependent exponent suggests extended dynamical criticality
below the transition point.Comment: 12 pages, 9 figures + Supplemenraty material pdf 2 pages 4 figs, 1
table, accepted version in Scientific Report
Homeostatic plasticity and external input shape neural network dynamics
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.
Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols
Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004â2006 the concept of a âreal-timeâ comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called âreal-timeâ z-scores. The âreal-timeâ or âliveâ normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patientâs symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback
Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges
Smart Biofeedback
Smart biofeedback is receiving attention because of the widespread availability of advanced technologies and smart devices that are used in effective collection, analysis, and feedback of physiologic data. Researchers and practitioners have been working on various aspects of smart biofeedback methodologies and applications by using wireless communications, the Internet of Things (IoT), wearables, biomedical sensors, artificial intelligence, big data analytics, clinical virtual reality, smartphones, and apps, among others. The current paradigm shift in information and communication technologies (ICT) has been propelling the rapid pace of innovation in smart biofeedback. This book addresses five important topics of the perspectives and applications in smart biofeedback: brain networks, neuromeditation, psychophysiological psychotherapy, physiotherapy, and privacy, security, and integrity of data
The role of excitation and inhibition in learning and memory formation
The neurons in the mammalian brain can be classified into two broad categories: excitatory and inhibitory neurons. The former has been historically associated to information processing whereas the latter has been linked to network homeostasis. More recently, inhibitory neurons have been related to several computational roles such as the gating of signal propagation, mediation of network competition, or learning. However, the ways by which excitation and inhibition can regulate learning have not been exhaustively explored. Here we explore several model systems to investigate the role of excitation and inhibition in learning and memory formation. Additionally, we investigate the effect that third factors such as neuromodulators and network state exert over this process. Firstly, we explore the effect of neuromodulators onto excitatory neurons and excitatory plasticity. Next, we investigate the plasticity rules governing excitatory connections while the neural network oscillates in a sleep-like cycle, shifting between Up and Down states. We observe that this plasticity rule depends on the state of the network. To study the role of inhibitory neurons in learning, we then investigate the mechanisms underlying place field emergence and consolidation. Our simulations suggest that dendrite-targeting interneurons play an important role in both promoting the emergence of new place fields and in ensuring place field stabilization. Soma-targeting interneurons, on the other hand, are suggested to be related to quick, context-specific changes in the assignment of place and silent cells. We next investigate the mechanisms underlying the plasticity of synaptic connections from specific types of interneurons. Our experiments suggest that different types of interneurons undergo different synaptic plasticity rules. Using a computational model, we implement these plasticity rules in a simplified network. Our simulations indicate that the interaction between the different forms of plasticity account for the development of stable place fields across multiple environments. Moreover, these plasticity rules seems to be gated by the postsynaptic membrane voltage. Inspired by these findings, we propose a voltage-based inhibitory synaptic plasticity rule. As a consequence of this rule, the network activity is kept controlled by the imposition of a maximum pyramidal cell firing rate. Remarkably, this rule does not constrain the postsynaptic firing rate to a narrow range. Overall, through multiple stages of interactions between experiments and computational simulations, we investigate the effect of excitation and inhibition in learning. We propose mechanistic explanations for experimental data, and suggest possible functional implications of experimental findings. Finally, we proposed a voltage-based inhibitory synaptic plasticity model as a mechanism for flexible network homeostasis.Open Acces
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)