Information Processing In Brain Research Articles

Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains.

The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. Yet how network connectivity relates to function is poorly understood, and the functional capabilities of models of spiking networks are still rudimentary. The lack of both theoretical insight and practical algorithms to find the necessary connectivity poses a major impediment to both studying information processing in the brain and building efficient neuromorphic hardware systems. The training algorithms that solve this problem for artificial neural networks typically rely on gradient descent. But doing so in spiking networks has remained challenging due to the nondifferentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients affect learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative's scale can substantially affect learning performance. When we combine surrogate gradients with suitable activity regularization techniques, spiking networks perform robust information processing at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.

Delay-weight plasticity-based supervised learning in optical spiking neural networks

We propose a modified supervised learning algorithm for optical spiking neural networks, which introduces synaptic time-delay plasticity on the basis of traditional weight training. Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity. A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method. Moreover, the proposed algorithm is also applied to two benchmark data sets for classification. In a simple network structure with only a few optical neurons, the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning. The introduction of delay adjusting improves the learning efficiency and performance of the algorithm, which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.

Proteome profile of telencephalon associates attenuated neurogenesis with chronic stress induced mood disorder phenotypes in zebrafish model

Debilitating mental illness like depression and related mood disorders is due to the disruption in circuitry that controls emotion, motivation, and reward, characterized by disparate phenotypes like decrease in socialization, motivation, threshold for threat apprehension, etc. Chronic stress is a major factor in the etiology of these disorders. Here, using a chronic unpredictable stress (CUS) paradigm the characterization of an array of mood disorder phenotypes in adult zebrafish, in comparison to normal control unstressed fish, was achieved using a battery of behavioral assays including novel ones comprising social interaction test, feed approach test, threat response test and novel tank test. For the predictive validity of the model for mood disorders, the mitigative role of a slow (imipramine) and fast (ketamine) acting antidepressant was assessed. The molecular changes associated with CUS-induced mood disorder phenotype was investigated utilizing a high throughput method called isobaric tag for relative and absolute quantification (iTRAQ) in telencephalon, the region critically associated with the processing of emotional information in the fish brain. Out of 222 proteins identified to be significantly altered, 58 were differentially expressed across the stress and antidepressant-treatment groups at more than one fold (in log2) change. Of these proteins, some were implicated in earlier studies on mood disorders such as CABP1, PER2, mTOR, etc. The enrichment of altered proteins by Ingenuity Pathway Analysis (IPA) led us to mTOR and opioid signaling pathways, the top canonical pathways affected in the fish telencephalon. Interestingly, most of the pathways affected converge at the one controlling cell proliferation thus indicating altered neurogenesis, which was validated using immunohistochemistry for cell proliferation markers BrdU, SOX2, and BLBP. The study concludes that molecules that regulate telencephalon neural progenitor cell proliferation or neurogenesis are crucially involved in chronic stress-induced mood disorders by affecting the circuitry that controls emotion and reward.

Niobium Neuron: RSFQ Based Bio-Inspired Circuit

Neuromorphic and bio-inspired circuits have reached considerable attention since Moore's Law is coming to its limitations. Information processing in mammalian brains takes place in a far more energy-efficient manner and significantly faster than in the best computing architecture nowadays. We propose an approach to bring those benefits to a superconducting information processing circuit. Since the computation in a neuronal network is considered as analogue and the computation as digital, the design is grown around a Josephson comparator with its inherent non-linearity in the transfer function as the central information processing unit. Furthermore, a modified version of the Josephson Transmission Line is used to realize an adaptable coupling between neuron cells. This circuit design benefits of the noise in a 4.2 K environment and is therefore more resilient to noise and switching errors than conventional digital circuits. The proposed circuit behavior in a 2-neuron configuration and the integration in a network topology will be investigated.

Multiplexed neurochemical transmission emulated using a dual-excitatory synaptic transistor

The ability to emulate multiplexed neurochemical transmission is an important step toward mimicking complex brain activities. Glutamate and dopamine are neurotransmitters that regulate thinking and impulse signals independently or synergistically. However, emulation of such simultaneous neurotransmission is still challenging. Here we report design and fabrication of synaptic transistor that emulates multiplexed neurochemical transmission of glutamate and dopamine. The device can perform glutamate-induced long-term potentiation, dopamine-induced short-term potentiation, or co-release-induced depression under particular stimulus patterns. More importantly, a balanced ternary system that uses our ambipolar synaptic device backtrack input ‘true’, ‘false’ and ‘unknown’ logic signals; this process is more similar to the information processing in human brains than a traditional binary neural network. This work provides new insight for neuromorphic systems to establish new principles to reproduce the complexity of a mammalian central nervous system from simple basic units.

Human Factors Modelling Approach: Application to a Safety Device Supporting Crane Operations in Major Hazard Industries

The European Directive on Safety and Health at Work and the following normatives have the scope to provide high levels of health and safety at work, based on some general principles managing activities and including the risk assessment to continuously improve processes and workplaces. However, the working area changes and brings new risks and challenges for workers. Several of them are associated with new technologies, which determine complex human–machine interactions, leading to an increased mental and emotional strain. To reduce these emerging risks, their understanding and assessment are important. Although great efforts have already been made, there is still a lack of conceptual frameworks for analytically assessing human–machine interaction. This paper proposes a systematic approach that, beyond including the classification in domains to explain the complexity of the human–machine interaction, accounts for the information processing of the human brain. Its validation is shown in a major accident hazard industry where a smart safety device supporting crane related operations is used. The investigation is based on the construction of a questionnaire for the collection of answers about the feeling of crane operators when using the device and the evaluation of the Cronbach’s alpha to measure of the reliability of the assessment.

An ecologically motivated image dataset for deep learning yields better models of human vision

Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition Challenge. This dataset comprises images from 1,000 categories, selected to provide a challenging testbed for automated visual object recognition systems. Moving beyond this common practice, we here introduce ecoset, a collection of >1.5 million images from 565 basic-level categories selected to better capture the distribution of objects relevant to humans. Ecoset categories were chosen to be both frequent in linguistic usage and concrete, thereby mirroring important physical objects in the world. We test the effects of training on this ecologically more valid dataset using multiple instances of two neural network architectures: AlexNet and vNet, a novel architecture designed to mimic the progressive increase in receptive field sizes along the human ventral stream. We show that training on ecoset leads to significant improvements in predicting representations in human higher-level visual cortex and perceptual judgments, surpassing the previous state of the art. Significant and highly consistent benefits are demonstrated for both architectures on two separate functional magnetic resonance imaging (fMRI) datasets and behavioral data, jointly covering responses to 1,292 visual stimuli from a wide variety of object categories. These results suggest that computational visual neuroscience may take better advantage of the deep learning framework by using image sets that reflect the human perceptual and cognitive experience. Ecoset and trained network models are openly available to the research community.

Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

Background: Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied. Methods: Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes). Results: Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities. Limitations: Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results. Conclusions: Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

Surprise me Softly: The Element of Surprise in Designing Museum Experiences

Experience design has become a widely discussed topic. Museums use experience design for engaging their visitors and culture offers exceptional tools for it. Visual arts and music are particularly effective in eliciting visitors’ emotions. However, there are a number of visual and acoustic cues that influence museum visitor response behaviours. Understanding the ways in which the human brain processes information provides a basis for furthering experience design principles. This study focuses on the emotion of surprise, considered especially effective for engaging visitor attention, providing meaning and affecting memory. The methodology involved monitoring psychophysiological responses and self-reports to assess research participants’ reactions to visual/acoustic stimuli. The aim was to confirm/detect types of sensory stimuli that generate the emotion of surprise, to see if participants have similar reactions to stimuli and whether individuals’ self-reports are aligned with their psychophysiological reactions. The results showed that musical stimuli are more effective than visual arts in eliciting surprise. While the study showed no clear indications that visual cues have an effect on surprise, musical cues, such as rapid attack, large pitch variation, higher harmonics, slow tempo with a sudden interruption, and sudden change in loudness do seem to play a role. Other cues, such as major key, 4/4 meter, timbral difference, and diatonic harmony also have an impact on the elicitation of surprise. These are important implications for designing museum experiences.

Interactions neurogliales en physiopathologie cérébrale / Neuroglial interactions in cerebral physiopathology

Recherche Page web : https://www.college-de-france.fr/site/en-cirb/rouach.htm. The main goal of our group is to determine whether and how the underexplored glial cells, which are the very abundant non neuronal but yet active cells of the brain, play a role in brain information processing. We investigate the molecular modalities and functional outcomes of neuroglial interactions in physiological and pathological conditions, focusing ex vivo or in vivo on neuronal excitability, synaptic transmi...

Correlation between octopaminergic signalling and foraging task specialisation in honeybees.

Regulation of pollen and nectar foraging in honeybees is linked to differences in the sensitivity to the reward. Octopamine (OA) participates in the processing of reward-related information in the bee brain, being a candidate to mediate and modulate the division of labour among pollen and nectar foragers. Here we tested the hypothesis that OA affects the resource preferences of foragers. We first investigated whether oral administration of OA is involved in the transition from nectar to pollen foraging. We quantified the percentage of OA-treated bees that switched from a sucrose solution to a pollen feeder when the sugar concentration was decreased experimentally. We also evaluated if feeding the colonies sucrose solution containing OA increases the rate of bees collecting pollen. Finally, we quantified OA and tyramine (TYR) receptor genes expression of pollen and nectar foragers in different parts of the brain, as a putative mechanism that affects the decision-making process regarding the resource type collected. Adding OA in the food modified the probability that foragers switch from nectar to pollen collection. The proportion of pollen foragers also increased after feeding colonies with OA-containing food. Furthermore, the expression level of the AmoctαR1 was upregulated in foragers arriving at pollen sources compared with those arriving at sugar-water feeders. Using age-matched pollen and nectar foragers that returned to the hive, we detected an upregulated expression of a TYR receptor gene in the suboesophageal ganglia. These findings support our prediction that OA signalling affects the decision in honeybee foragers to collect pollen or nectar.

Design and Implementation of an App for Secondary English Teacher Recruitment Exams: Based on Brain Memory and Information Processing Mechanisms

Design and Implementation of an App for Secondary English Teacher Recruitment Exams: Based on Brain Memory and Information Processing Mechanisms

Colored visual stimuli evoke spectrally tuned neuronal responses across the central nervous system of zebrafish larvae

BackgroundVisually guided behaviors such as optomotor and optokinetic responses, phototaxis, and prey capture are crucial for survival in zebrafish and become apparent after just a few days of development. Color vision, which in zebrafish is based on a spatially anisotropic tetrachromatic retina, provides an additional important component of world representation driving fundamental larval behaviors. However, little is known about the central nervous system (CNS) circuitry underlying color vision processing downstream of the retina, and its activity correlates with behavior. Here, we used the transparent larva of zebrafish to image CNS neurons and their activity in response to colored visual stimuli.ResultsTo investigate the processing of chromatic information in the zebrafish larva brain, we mapped with cellular resolution, spectrally responsive neurons in the larva encephalon and spinal cord. We employed the genetically encoded calcium indicator GCaMP6s and two-photon microscopy to image the neuronal activity while performing visual stimulation with spectrally distinct stimuli at wavelengths matching the absorption peaks of the four zebrafish cone types. We observed the presence of a high number of wavelength-selective neurons not only in the optic tectum, but also in all other regions of the CNS, demonstrating that the circuitry involved in processing spectral information and producing color-selective responses extends to the whole CNS.ConclusionsOur measurements provide a map of neurons involved in color-driven responses, revealing that spectral information spreads in all regions of the CNS. This suggests the underlying complexity of the circuits involved and opens the way to their detailed future investigation.

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain's intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity.

Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.

Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems.

The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.

Brain-information Processing for Quick and Stable Human Movements--Stretch-reflex Regulation Based on Visually Updated Body Representation

Brain-information Processing for Quick and Stable Human Movements--Stretch-reflex Regulation Based on Visually Updated Body Representation

Automatic drowsiness detection using electroencephalogram signal

Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real-time working environment such as driving, monitoring power generation and patient health etc. These hazards can be sidestepped by introducing the automatic drowsiness detection system. This Letter suggested the electroencephalogram (EEG)-based automatic drowsiness detection method. The clustering variational mode decomposition (CVMD) explores the non-stationary behaviour of EEG for drowsiness detection. In CVMD optimum allocation sampling analyses non-homogenous EEG signals and converts those into homogeneous EEG clusters. These clusters are then decomposed into band-limited modes. The oscillatory mode characteristics are extracted in terms of several features. These features are fed as input into the least-squares support vector machine classifier. The proposed method provides better drowsiness detection performance in comparison with the different methods using the same data set.

Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.

Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.