Network Design and the Brain

Modification of Neural Circuit Functions by Microglial P2Y6 Receptors in Health and Neurodegeneration

Neural circuits consisting of neurons and glial cells help to establish all functions of the CNS. Microglia, the resident immunocytes of the CNS, are endowed with UDP-sensitive P2Y6 receptors (P2Y6Rs) which regulate phagocytosis/pruning of excessive synapses during individual development and refine synapses in an activity-dependent manner during adulthood. In addition, this type of receptor plays a decisive role in primary (Alzheimer's disease, Parkinson's disease, neuropathic pain) and secondary (epilepsy, ischemic-, mechanical-, or irradiation-induced) neurodegeneration. A whole range of microglial cytokines controlled by P2Y6Rs, such as the interleukins IL-1β, IL-6, IL-8, and tumor necrosis factor-α (TNF-α), leads to neuroinflammation, resulting in neurodegeneration. Hence, small molecular antagonists of P2Y6Rs and genetic knockdown of this receptor provide feasible ways to alleviate inflammation-induced neurological disorders but might also interfere with the regulation of the synaptic circuitry. The present review aims at investigating this dual role of P2Y6Rs in microglia, both in shaping neural circuits by targeted phagocytosis and promoting neurodegenerative illnesses by fostering neuroinflammation through multiple transduction mechanisms.

A neural algorithm for computing bipartite matchings

Finding optimal bipartite matchings-e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review-is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems.

Collective behavior in relation with changing environments: Dynamics, modularity, and agency

Collective behavior operates without central control, using local interactions among participants to adjust to changing conditions. Many natural systems operate collectively, and by specifying what objectives are met by the system, the idea of agency helps to describe how collective behavior is embedded in the conditions it deals with. Ant colonies function collectively, and the enormous diversity of more than 15K species of ants, in different habitats, provides opportunities to look for general ecological patterns in how collective behavior operates. The foraging behavior of harvester ants in the desert regulates activity to manage water loss, while the trail networks of turtle ants in the canopy tropical forest respond to rapidly changing resources and vegetation. These examples illustrate some broad correspondences in natural systems between the dynamics of collective behavior and the dynamics of the surroundings. To outline how interactions among participants, acting in relation with changing surroundings, achieve collective outcomes, I focus on three aspects of collective behavior: the rate at which interactions adjust to conditions, the feedback regime that stimulates and inhibits activity, and the modularity of the network of interactions. To characterize the dynamics of the surroundings, I consider gradients in stability, energy flow, and the distribution of resources and demands. I then propose some hypotheses that link how collective behavior operates with changing environments.

Brain-inspired neural circuit evolution for spiking neural networks

In biological neural systems, different neurons are capable of self-organizing to form different neural circuits for achieving a variety of cognitive functions. However, the current design paradigm of spiking neural networks is based on structures derived from deep learning. Such structures are dominated by feedforward connections without taking into account different types of neurons, which significantly prevent spiking neural networks from realizing their potential on complex tasks. It remains an open challenge to apply the rich dynamical properties of biological neural circuits to model the structure of current spiking neural networks. This paper provides a more biologically plausible evolutionary space by combining feedforward and feedback connections with excitatory and inhibitory neurons. We exploit the local spiking behavior of neurons to adaptively evolve neural circuits such as forward excitation, forward inhibition, feedback inhibition, and lateral inhibition by the local law of spike-timing-dependent plasticity and update the synaptic weights in combination with the global error signals. By using the evolved neural circuits, we construct spiking neural networks for image classification and reinforcement learning tasks. Using the brain-inspired Neural circuit Evolution strategy (NeuEvo) with rich neural circuit types, the evolved spiking neural network greatly enhances capability on perception and reinforcement learning tasks. NeuEvo achieves state-of-the-art performance on CIFAR10, DVS-CIFAR10, DVS-Gesture, and N-Caltech101 datasets and achieves advanced performance on ImageNet. Combined with on-policy and off-policy deep reinforcement learning algorithms, it achieves comparable performance with artificial neural networks. The evolved spiking neural circuits lay the foundation for the evolution of complex networks with functions.

Mental stress recognition on the fly using neuroplasticity spiking neural networks

Mental stress is found to be strongly connected with human cognition and wellbeing. As the complexities of human life increase, the effects of mental stress have impacted human health and cognitive performance across the globe. This highlights the need for effective non-invasive stress detection methods. In this work, we introduce a novel, artificial spiking neural network model called Online Neuroplasticity Spiking Neural Network (O-NSNN) that utilizes a repertoire of learning concepts inspired by the brain to classify mental stress using Electroencephalogram (EEG) data. These models are personalized and tested on EEG data recorded during sessions in which participants listen to different types of audio comments designed to induce acute stress. Our O-NSNN models learn on the fly producing an average accuracy of 90.76% (σ = 2.09) when classifying EEG signals of brain states associated with these audio comments. The brain-inspired nature of the individual models makes them robust and efficient and has the potential to be integrated into wearable technology. Furthermore, this article presents an exploratory analysis of trained O-NSNNs to discover links between perceived and acute mental stress. The O-NSNN algorithm proved to be better for personalized stress recognition in terms of accuracy, efficiency, and model interpretability.

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

Therapeutic Potential of Chinese Medicine for Endogenous Neurogenesis: A Promising Candidate for Stroke Treatment

Strokes are a leading cause of morbidity and mortality in adults worldwide. Extensive preclinical studies have shown that neural-stem-cell-based treatments have great therapeutic potential for stroke. Several studies have confirmed that the effective components of traditional Chinese medicine can protect and maintain the survival, proliferation, and differentiation of endogenous neural stem cells through different targets and mechanisms. Therefore, the use of Chinese medicines to activate and promote endogenous nerve regeneration and repair is a potential treatment option for stroke patients. Here, we summarize the current knowledge regarding neural stem cell strategies for ischemic strokes and the potential effects of these Chinese medicines on neuronal regeneration.

Nine insights from internet engineering that help us understand brain network communication

Philosophers have long recognized the value of metaphor as a tool that opens new avenues of investigation. By seeing brains as having the goal of representation, the computer metaphor in its various guises has helped systems neuroscience approach a wide array of neuronal behaviors at small and large scales. Here I advocate a complementary metaphor, the internet. Adopting this metaphor shifts our focus from computing to communication, and from seeing neuronal signals as localized representational elements to seeing neuronal signals as traveling messages. In doing so, we can take advantage of a comparison with the internet's robust and efficient routing strategies to understand how the brain might meet the challenges of network communication. I lay out nine engineering strategies that help the internet solve routing challenges similar to those faced by brain networks. The internet metaphor helps us by reframing neuronal activity across the brain as, in part, a manifestation of routing, which may, in different parts of the system, resemble the internet more, less, or not at all. I describe suggestive evidence consistent with the brain's use of internet-like routing strategies and conclude that, even if empirical data do not directly implicate internet-like routing, the metaphor is valuable as a reference point for those investigating the difficult problem of network communication in the brain and in particular the problem of routing.

Systems Medicine as a Strategy to Deal with Alzheimer's Disease

The traits of Alzheimer's disease (AD) include amyloid plaques made of Aβ1-40 and Aβ1-42, and neurofibrillary tangles by the hyperphosphorylation of tau protein. AD is a complex disorder that is heterogenous in genetical, neuropathological, and clinical contexts. Current available therapeutics are unable to cure AD. Systems medicine is a strategy by viewing the body as a whole system, taking into account each individual's unique health profile, provide treatment and associated nursing care clinically for the patient, aiming for precision. Since the onset of AD can lead towards cognitive impairment, it is vital to intervene and diagnose early and prevent further progressive loss of neurons. Moreover, as the individual's brain functions are impaired due to neurodegeneration in AD, it is essential to reconstruct the neurons or brain cells to enable normal brain functions. Although there are different subtypes of AD due to varied pathological lesions, in the majority cases of AD, neurodegeneration and severe brain atrophy develop at the chronic stage. Novel approaches including RNA based gene therapy, stem cell based technology, bioprinting technology, synthetic biology for brain tissue reconstruction are researched in recent decades in the hope to decrease neuroinflammation and restore normal brain function in individuals of AD. Systems medicine include the prevention of disease, diagnosis and treatment by viewing the individual's body as a whole system, along with systems medicine based nursing as a strategy against AD that should be researched further.

On the Application of Developmental Cognitive Neuroscience in Educational Environments

The paper overviews components of neurologic processing efficiencies to develop innovative methodologies and thinking to school-based applications and changes in educational leadership based on sound findings in the cognitive neurosciences applied to schools and learners. Systems science can allow us to better manage classroom-based learning and instruction on the basis of relatively easily evaluated efficiencies or inefficiencies and optimization instead of simply examining achievement. "Medicalizing" the learning process with concepts such as "learning disability" or employing grading methods such as pass-fail does little to aid in understanding the processes that learners employ to acquire, integrate, remember, and apply information learned. The paper endeavors to overview and provided reference to tools that can be employed that allow a better focus on nervous system-based strategic approaches to classroom learning.

A Graph Network Model for Neural Connection Prediction and Connection Strength Estimation

Objective. Reconstruction of connectomes at the cellular scale is a prerequisite for understanding the principles of neural circuits. However, due to methodological limits, scientists have reconstructed the connectomes of only a few organisms such as C. elegans, and estimated synaptic strength indirectly according to their size and number. Approach. Here, we propose a graph network model to predict synaptic connections and estimate synaptic strength by using the calcium activity data from C. elegans. Main results. The results show that this model can reliably predict synaptic connections in the neural circuits of C. elegans, and estimate their synaptic strength, which is an intricate and comprehensive reflection of multiple factors such as synaptic type and size, neurotransmitter and receptor type, and even activity dependence. In addition, the excitability or inhibition of synapses can be identified by this model. We also found that chemical synaptic strength is almost linearly positively correlated to electrical synaptic strength, and the influence of one neuron on another is non-linearly correlated with the number between them. This reflects the intrinsic interaction between electrical and chemical synapses. Significance. Our model is expected to provide a more accessible quantitative and data-driven approach for the reconstruction of connectomes in more complex nervous systems, as well as a promising method for accurately estimating synaptic strength.

A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, “synaptic pruning” and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, “information channels” are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The “information channels” are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images.

Neuroregeneration and functional recovery after stroke: advancing neural stem cell therapy toward clinical application

Stroke is a main cause of death and disability worldwide. The ability of the brain to self-repair in the acute and chronic phases after stroke is minimal; however, promising stem cell-based interventions are emerging that may give substantial and possibly complete recovery of brain function after stroke. Many animal models and clinical trials have demonstrated that neural stem cells (NSCs) in the central nervous system can orchestrate neurological repair through nerve regeneration, neuron polarization, axon pruning, neurite outgrowth, repair of myelin, and remodeling of the microenvironment and brain networks. Compared with other types of stem cells, NSCs have unique advantages in cell replacement, paracrine action, inflammatory regulation and neuroprotection. Our review summarizes NSC origins, characteristics, therapeutic mechanisms and repair processes, then highlights current research findings and clinical evidence for NSC therapy. These results may be helpful to inform the direction of future stroke research and to guide clinical decision-making.

Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in "information spreading" (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

Pathogenetical and Neurophysiological Features of Patients with Autism Spectrum Disorder: Phenomena and Diagnoses

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is accompanied by social deficits, repetitive and restricted interests, and altered brain development. The majority of ASD patients suffer not only from ASD itself but also from its neuropsychiatric comorbidities. Alterations in brain structure, synaptic development, and misregulation of neuroinflammation are considered risk factors for ASD and neuropsychiatric comorbidities. Electroencephalography has been developed to quantitatively explore effects of these neuronal changes of the brain in ASD. The pineal neurohormone melatonin is able to contribute to neural development. Also, this hormone has an inflammation-regulatory role and acts as a circadian key regulator to normalize sleep. These functions of melatonin may play crucial roles in the alleviation of ASD and its neuropsychiatric comorbidities. In this context, this article focuses on the presumable role of melatonin and suggests that this hormone could be a therapeutic agent for ASD and its related neuropsychiatric disorders.

Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases

The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.