Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain. Encoding models typically comprise a nonlinear transformation of stimuli to features (feature model) and a linear transformation of features to responses (response model). While there has been extensive work on developing better feature models, the work on developing better response models has been rather limited. Here, we investigate the extent to which recurrent neural network models can use their internal memories for nonlinear processing of arbitrary feature sequences to predict feature-evoked response sequences as measured by functional magnetic resonance imaging. We show that the proposed recurrent neural network models can significantly outperform established response models by accurately estimating long-term dependencies that drive hemodynamic responses. The results open a new window into modeling the dynamics of brain activity in response to sensory stimuli.
Triple Configuration of Brain Networks Based on Recurrent Neural Networks: The Synergistic Effects of Exogenous Stimuli, Task Demands, and Spontaneous Activity
Self-localization during navigation with noisy sensors in an ambiguous world is computationally challenging, yet animals and humans excel at it. In robotics, Simultaneous Location and Mapping (SLAM) algorithms solve this problem though joint sequential probabilistic inference of their own coordinates and those of external spatial landmarks. We generate the first neural solution to the SLAM problem by training recurrent LSTM networks to perform a set of hard 2D navigation tasks that include generalization to completely novel trajectories and environments. The hidden unit representations exhibit…
Characterising brain activity at rest is of paramount importance to our understanding both of general principles of brain functioning and of the way brain dynamics is affected in the presence of neurological or psychiatric pathologies. We measured the time-reversal symmetry of spontaneous electroencephalographic brain activity recorded from three groups of patients and their respective control group under two experimental conditions (eyes open and closed). We evaluated differences in time irreversibility in terms of possible underlying physical generating mechanisms. The results showed that re…
Standard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how the ability of neuroimaging to extract genuine functional brai…
Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activi…
A key problem with neuroprostheses and brain monitoring interfaces is that they need extreme energy efficiency. One way of lowering energy is to use the low power modes avail- able on the processors embedded in these devices. We present a technique to predict when neuronal activity of interest is likely to occur, so that the processor can run at nominal operating frequency at those times, and be placed in low power modes otherwise. To achieve this, we discover that branch predictors can also predict brain activity. By performing brain surgeries on awake and anesthetized mice, we evaluate sever…
Mental disorders may exhibit pathological brain rhythms and neurostimulation promises to alleviate of patients' symptoms by modifying these rhythms. Today, most neurostimulation schemes are open-loop, i.e. administer experimental stimulation protocols independent of the patients brain activity which may yield a sub-optimal treatment. We propose a closed-loop feedback control scheme estimating an optimal stimulation based on observed brain activity. The optimal stimulation is chosen according to a user-defined target frequency distribution, which permits frequency tuning of the brain activity i…
The effects of temperature on various aspects of neural activity from single cell to neural circuit level have long been known. However, how temperature affects the system-level of activity typical of experiments using non-invasive imaging techniques, such as magnetic brain imaging of electroencephalography, where neither its direct measurement nor its manipulation are possible, is essentially unknown. Starting from its basic physical definition, we discuss possible ways in which temperature may be used both as a parameter controlling the evolution of other variables through which brain activi…
The human brain exhibits a complex structure made of scale-free highly connected modules loosely interconnected by weaker links to form a small-world network. These features appear in healthy patients whereas neurological diseases often modify this structure. An important open question concerns the role of brain modularity in sustaining the critical behaviour of spontaneous activity. Here we analyse the neuronal activity of a model, successful in reproducing on non-modular networks the scaling behaviour observed in experimental data, on a modular network implementing the main statistical featu…
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of human thought. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states -- characterized by minimal energy -- display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-ac…
High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer, which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuro…
Most brain models focus on associative memory or calculation capability, experimentally inaccessible using physiological methods. Here we present a model explaining a basic feature of electroencephalograms (EEG). Our model is based on an electrical network with threshold firing and plasticity of synapses that reproduces very robustly the measured exponent 0.8 of the medical EEG spectra, a solid evidence for self-organized criticality. Our result are also valid on small-world lattices. We propose that an universal scaling behaviour characterizes many physiological signal spectra for brain contr…
We present an approach to study functional segregation and integration in the living brain based on community structure decomposition determined by maximum modularity. We demonstrate this method with a network derived from functional imaging data with nodes defined by individual image pixels, and edges in terms of correlated signal changes. We found communities whose anatomical distributions correspond to biologically meaningful structures and include compelling functional subdivisions between anatomically equivalent brain regions.
A diverse white matter network and finely tuned neuronal membrane properties allow the brain to transition seamlessly between cognitive states. However, it remains unclear how static structural connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain's trajectories through a high-dimensional activity space at the level of single time point activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task. We find that specific state…
We propose a neuronal network model which undergoes a saddle-node bifurcation on an invariant circle as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.
Current advances in neurosciences deal with the functional architecture of the central nervous system, paving the way for holistic theories that improve our understanding of brain activity. From topology, a strong concept comes into play in the understanding of brain signals, namely, continuous mapping of the signals onto a hyper-sphere, a 4-dimensional space equipped with a doughnut-like shape that is not detected by observers living in a 3-dimensional world. We evaluate the features of the imperceptible 4th dimension based on resting state functional magnetic resonance imaging series. In par…
Istanbul Technical University（伊斯坦布尔技术大学）；University of Manouba（马努巴大学）
Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities. Neurological findings suggest that early diagnosis of brain disorders, such as mild cognitive impairment (MCI), can prevent and even reverse its development into Alzheimer's disease (AD). In this context, recent studies aimed to predict the evolution of brain connectivities over time by proposing machine learning models that work on brain images. However, such an approach is costly and time-consuming. Here, we propose to use brain connectivities as a more efficient alternat…
Understanding mind-brain-environment relations is one of the key topics in psychology. Kurt Lewin, inspired by theoretical physics, tried to establish topological and vector psychology analyzing patterns of interaction between the individual and her/his environment. The time is ripe to reformulate his ambitious goals, searching for ways to interpret objectively measured brain processes in terms of suitable psychological constructs. Connecting cognitive and social psychology constructs to neurophenomics, as it is done now in psychiatry, should ground them in physical reality.
A new generation of molecular tools for analyzing neural activity has been contributing to elucidate classical open questions in neuroscience. With enhanced GEVIs and advanced optical imaging techniques, voltage-imaging technologies have been increasingly used to observe the dynamics of large circuits. In this chapter, we describe how to combine cortical wide-field voltage imaging with hippocampal electrophysiology in awake behaving mice. Furthermore, we highlight how this method can be useful for different possible investigations.
Despite intense recent research, the neural correlates of conscious visual perception remain elusive. The most established paradigm for studying brain mechanisms underlying conscious perception is to keep the physical sensory inputs constant and identify brain activities that correlate with the changing content of conscious awareness. However, such a contrast based on conscious content alone would not only reveal brain activities directly contributing to conscious perception, but also include brain activities that precede or follow it. To address this issue, we devised a paradigm whereby we co…
The continuous interaction between brain regions "at rest" defines the so-called resting state networks (RSN) which can be reconstructed from the analysis of functional magnetic resonance imaging (fMRI) data. What dynamical mechanism allows for a flexible large-scale organization of the RSN still remains an important challenge. Here, three key novel properties of the RSN are uncovered. First, the correlation length (i.e., the length at which correlation between two regions vanishes) diverges with the cluster's size considered. Second, this divergence it is observed also for measures of mutual …
University of Pennsylvania（宾夕法尼亚大学）；Wu Tsai Institute, Yale University（耶鲁大学吴氏研究所）；Northeastern University（东北大学）；Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；Yale University（耶鲁大学）
Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important t for efficient neural functioning. Experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to stabilize dynamics and maintain them across a range of situations. Here, we e…
Conventional neuroimaging analyses have revealed the computational specificity of localized brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity and use a methodology t…
The human brain exhibits a relatively stable spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how and why local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activi…
A theoretical quantum brain model is proposed using a nonlinear Schroedinger wave equation. The model proposes that there exists a quantum process that mediates the collective response of a neural lattice (classical brain). The model is used to explain eye movements when tracking moving targets. Using a Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical brain, a wave packet is triggered in the quantum brain. This wave packet moves like a particle. Second, when the ey…
Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress towards this goal might be accelerated by working towards an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulation…
As Brain-computer interface (BCI) technology develops it is likely it may be incorporated into protocols that complement and supplement existing movements of the user. Two possible scenarios for such a control could be: the increasing interest to control artificial supernumerary prosthetics, or in cases following brain injury where BCI can be incorporated alongside residual movements to recover ability. In this study we explore the extent to which the human motor cortex is able to concurrently control movements via a BCI and overtly executed movements. Crucially both movement types are driven …
Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)~D^-\b{eta}) on the hub Degree (D) is a signature …
Motor imagery-based brain-computer interfaces (BCIs) use an individuals ability to volitionally modulate localized brain activity as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Previous research suggests that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these …
Conventional deep brain stimulation (DBS) of basal ganglia uses high-frequency regular electrical pulses to treat Parkinsonian motor symptoms and has a series of limitations. Relatively new and not yet clinically tested optogenetic stimulation is an effective experimental stimulation technique to affect pathological network dynamics. We compared the effects of electrical and optogenetic stimulation of the basal ganglia on the pathological parkinsonian rhythmic neural activity. We studied the network response to electrical stimulation and excitatory and inhibitory optogenetic stimulations. Diff…
Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively. Inspired by the bidirectional information flows, we proposed a bidirectional recurrent neural network (BRNN)-based method to decode the categories from fMRI data. The forward and bac…
Neurofeedback is a non-invasive brain training with long-term medical and non-medical applications. Despite the existence of several emotion regulation studies using neurofeedback, further investigation is needed to understand interactions of the brain regions involved in the process. We implemented EEG neurofeedback with simultaneous fMRI using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. The results showed increased activity of prefrontal, occipital, parietal, and limbic regions and increased functional connectivity between prefrontal, …
As the field of connectomics has matured, it has expanded from mapping the existence of connections between brain components to measuring the strength of connections. This information is increasingly accessible via methodologies such as pairing functional magnetic resonance (MR) imaging and MR tractography in the same human subject, as well as novel methods in non-human animals using optogenetics. Systems and network neuroscience have in recent years focused extensively on explaining correlation patterns of functional activity in the brain in terms of the degree of connectedness of brain compo…
There is consensus in the current literature that stable states of asynchronous irregular spiking activity require (i) large networks of 10 000 or more neurons and (ii) external background activity or pacemaker neurons. Yet already in 1963, Griffith showed that networks of simple threshold elements can be persistently active at intermediate rates. Here, we extend Griffith's work and demonstrate that sparse networks of integrate-and-fire neurons assume stable states of self-sustained asynchronous and irregular firing without external input or pacemaker neurons. These states can be robustly indu…
Networks of living neurons exhibit an avalanche mode of activity, experimentally found in organotypic cultures. Here we present a model based on self-organized criticality and taking into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG). The model consists in an electrical network with threshold firing and activity-dependent synapse strenghts. The system exhibits an avalanche activity power law distributed. The analysis of the power spectra of the electrical signal reproduces very robustly the power law behaviour with the exponent 0.8, experiment…
Learning how to estimate a connectional brain template(CBT) from a population of brain multigraphs, where each graph (e.g., functional) quantifies a particular relationship between pairs of brain regions of interest (ROIs), allows to pin down the unique connectivity patterns shared across individuals. Specifically, a CBT is viewed as an integral representation of a set of highly heterogeneous graphs and ideally meeting the centeredness (i.e., minimum distance to all graphs in the population) and discriminativeness (i.e., distinguishes the healthy from the disordered population) criteria. So fa…
Facebook AI Research（脸书人工智能研究院）；ENSAM（巴黎高等工程技术学院）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；École normale supérieure, PSL University（巴黎文理研究大学高等师范学院）；CNRS（法国国家科学研究中心）
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics. These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues. Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the brain responses of multiple subjects at once. We successfully test this approach on…
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subjects movement intentions without actual actions. Despite the extensive research of MI-EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal noise ratio and incom…
The relation between spontaneous and stimulated global brain activity is a fundamental problem in the understanding of brain functions. This question is investigated both theoretically and experimentally within the context of nonequilibrium fluctuation-dissipation relations. We consider the stochastic coarse-grained Wilson-Cowan model in the linear noise approximation and compare analytical results to experimental data from magnetoencephalography (MEG) of human brain. The short time behavior of the autocorrelation function for spontaneous activity is characterized by a double-exponential decay…
Anatomy of the human brain constrains the formation of large-scale functional networks. Here, given measured brain activity in gray matter, we interpolate these functional signals into the white matter on a structurally-informed high-resolution voxel-level brain grid. The interpolated volumes reflect the underlying anatomical information, revealing white matter structures that mediate functional signal flow between temporally coherent gray matter regions. Functional connectivity analyses of the interpolated volumes reveal an enriched picture of the default mode network (DMN) and its subcompone…
Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of information flow between them. Noise is ubiquitous in neural systems, and often considered an irritant to be overcome. Here we suggest that noise could play a functional role in synaptic pruning, allowing the brain to probe network…
Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a …
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…
A recurrent loop consisting of a single neuron is considered which is influenced by a chemical excitatory delayed synaptic feedback. We show the response of the system is dependent to the duration of the activity of the synapse which is determined by the deactivation time constant of the synapse. We show that loops with slow synapses, those which the effect of the synaptic activation remains for time constants comparable to the period of firing, show more predictable results where the effect of the fast synapses is tightly dependent on the loop delay time. The results are compared to those of …
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions. Here we study fMRI activity in ten subjects watching color natural movies and compute deep representations of these movies with an architecture that relies on optical flow and image content. The association of activity in visual areas with the different layers of the deep architecture displays complexity-related contrasts across visual areas and reveals a striking foveal/peripheral dichotomy.
