生物神经系统是由数量极其巨大的神经元相互联结的信息网络系统,在生物体的感觉、认知和运动控制中发挥关键性的作用.首先介绍神经元、大脑和一些生物神经网络的生理结构和理论模型,然后分别介绍其放电活动和网络动态特性的一些重要问题,包括神经元的复杂放电模式、耦合神经元网络系统的同步活动和时空动力学、大脑联合皮层神经微回路的网络结构特征,以及工作记忆和抉择过程的动力学机制等. 最后对今后研究给出一些展望.

生物神经系统是由数量极其巨大的神经元相互联结的信息网络系统,在生物体的感觉、认知和运动控制中发挥关键性的作用.首先介绍神经元、大脑和一些生物神经网络的生理结构和理论模型,然后分别介绍其放电活动和网络动态特性的一些重要问题,包括神经元的复杂放电模式、耦合神经元网络系统的同步活动和时空动力学、大脑联合皮层神经微回路的网络结构特征,以及工作记忆和抉择过程的动力学机制等. 最后对今后研究给出一些展望.

摘要：采用动力有限元方法，参照爆区现场的一实际三层砌体房屋结构，建立三维空间实体模型，并将底层的爆破地震波监测信号加载到砌体结构模型的基底，求解出有限元模拟的砌体结构其它各层的爆破振动响应值，并与监测结果进行了比较分析。对常见的2～4层砌体结构房屋，各取一种典型结构在底部节点垂直方向上施加不同主频的爆破地震波进行动力有限元建模并模拟开裂过程，通过定义结构单元的临界失效状态来寻求典型砌体结构房屋的爆破振动安全标准值，并给出了2～4层典型砌体结构房屋在各主频段爆破地震波作用下的爆破振动安全标准。结果表明，现行的《爆破安全规程》中基于工程经验及宏观调查资料基础上制定出的爆破振动安全标准过于粗糙和笼统，对于同类建筑，没有考虑到结构个体的振动特性差异，并不能保证所有结构物的安全，不具有普适性。

摘要：采用动力有限元方法，参照爆区现场的一实际三层砌体房屋结构，建立三维空间实体模型，并将底层的爆破地震波监测信号加载到砌体结构模型的基底，求解出有限元模拟的砌体结构其它各层的爆破振动响应值，并与监测结果进行了比较分析。对常见的2～4层砌体结构房屋，各取一种典型结构在底部节点垂直方向上施加不同主频的爆破地震波进行动力有限元建模并模拟开裂过程，通过定义结构单元的临界失效状态来寻求典型砌体结构房屋的爆破振动安全标准值，并给出了2～4层典型砌体结构房屋在各主频段爆破地震波作用下的爆破振动安全标准。结果表明，现行的《爆破安全规程》中基于工程经验及宏观调查资料基础上制定出的爆破振动安全标准过于粗糙和笼统，对于同类建筑，没有考虑到结构个体的振动特性差异，并不能保证所有结构物的安全，不具有普适性。

探讨了局部脑皮层网络活动中，耦合条件下的大规模神经振子群的能量消耗与神经信号编码之间的内禀关系，得到了神经元集群在阈下和阈上互相耦合时神经元膜电位变化的函数. 这个能量函数能够精确地再现神经电生理学实验中的EPSP,IPSP,动作电位以及动作电流. 最近功能性核磁共振实验证明了神经信号的编码是与能量的消耗紧密地耦合在一起的，因此研究结果表明利用能量原理研究大脑在神经网络层次上是如何进行编码的这一重大科学问题的讨论是十分有益的. 可以预计得到的能量函数将是生物学神经网络动力学稳定性计算的基础.

探讨了局部脑皮层网络活动中，耦合条件下的大规模神经振子群的能量消耗与神经信号编码之间的内禀关系，得到了神经元集群在阈下和阈上互相耦合时神经元膜电位变化的函数. 这个能量函数能够精确地再现神经电生理学实验中的EPSP,IPSP,动作电位以及动作电流. 最近功能性核磁共振实验证明了神经信号的编码是与能量的消耗紧密地耦合在一起的，因此研究结果表明利用能量原理研究大脑在神经网络层次上是如何进行编码的这一重大科学问题的讨论是十分有益的. 可以预计得到的能量函数将是生物学神经网络动力学稳定性计算的基础.

在John Schmitt和Philip Holmes等人工作的基础上,基于被动力学建立了一个考虑阻尼效应的昆虫LLS(lateral leg-spring)爬行模型.通过在Matlab环境下对模型进行数值仿真,发现引入阻尼后的模型更加符合实际情况,并表现出较好的稳定性.证明在施加的控制作用十分有限的情况下,昆虫本身身体结构及其爬行姿态在维持运动的稳定性方面发挥着重要作用,事实上这样的控制策略具有重要的生物学意义,通过被动力学的机械反馈,生物体可以在有限的神经控制作用下保持稳定运动,从而在受到地面扰动时及时做出反馈,并提高能效性.同时,我们考虑了引入偏摆角扰动时系统的响应,发现相对于其他变量扰动,偏摆角可以有效地改变模型的前进方向,并具有较好的鲁棒性,提出了可能的方向控制策略.

在John Schmitt和Philip Holmes等人工作的基础上,基于被动力学建立了一个考虑阻尼效应的昆虫LLS(lateral leg-spring)爬行模型.通过在Matlab环境下对模型进行数值仿真,发现引入阻尼后的模型更加符合实际情况,并表现出较好的稳定性.证明在施加的控制作用十分有限的情况下,昆虫本身身体结构及其爬行姿态在维持运动的稳定性方面发挥着重要作用,事实上这样的控制策略具有重要的生物学意义,通过被动力学的机械反馈,生物体可以在有限的神经控制作用下保持稳定运动,从而在受到地面扰动时及时做出反馈,并提高能效性.同时,我们考虑了引入偏摆角扰动时系统的响应,发现相对于其他变量扰动,偏摆角可以有效地改变模型的前进方向,并具有较好的鲁棒性,提出了可能的方向控制策略.

根据信息论的基本原理和方法,运用最小互信息和最大熵原理对神经编码进行研究和分析.通过对两个原理的基本介绍,描述了最小互信息和最大熵原理是如何用于评估神经反应中的信息量.研究结果表明神经信息的表达和神经能量的利用率密切相关,并发现高度进化的神经系统在能量的消耗和利用上严格遵循着经济性和高效性两个基本原则.为了验证神经信息处理与能量利用率的关系,提出了信能比的新概念,用于衡量最大熵原理对应神经系统在能量利用率上的经济性和高效性.并通过数值计算证实了一个猜想,即神经系统所消耗的能量反映了神经信息处理的内在规律,这为进一步研究一种崭新的神经信息处理原理——能量神经编码奠定了重要的理论基础.

根据信息论的基本原理和方法,运用最小互信息和最大熵原理对神经编码进行研究和分析.通过对两个原理的基本介绍,描述了最小互信息和最大熵原理是如何用于评估神经反应中的信息量.研究结果表明神经信息的表达和神经能量的利用率密切相关,并发现高度进化的神经系统在能量的消耗和利用上严格遵循着经济性和高效性两个基本原则.为了验证神经信息处理与能量利用率的关系,提出了信能比的新概念,用于衡量最大熵原理对应神经系统在能量利用率上的经济性和高效性.并通过数值计算证实了一个猜想,即神经系统所消耗的能量反映了神经信息处理的内在规律,这为进一步研究一种崭新的神经信息处理原理——能量神经编码奠定了重要的理论基础.

We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified for recorded spike trains from cerebellum and cerebral cortex. We found that ASTs generated with this method can closely follow the firing rate and local as well as global spike time variance and power spectrum. The method is primarily intended to generate well-controlled spike train populations as inputs for dynamic clamp studies or biophysically realistic multicompartmental models. Such inputs are essential to study detailed properties of synaptic integration with well-controlled input patterns that mimic the in vivo situation while allowing manipulation of input rate covariances at different time scales.

For more than 30 years, the fundamental goal in molecular motility has been to resolve force-generating motor protein structural changes. Although low-resolution structural studies have provided evidence for force-generating myosin rotations upon muscle activation, these studies did not resolve structural states of myosin in contracting muscle. Using electron paramagnetic resonance, we observed two distinct orientations of a spin label attached specifically to a single site on the light chain domain of myosin in relaxed scallop muscle fibers. The two probe orientations, separated by a 36 degrees +/- 5 degrees axial rotation, did not change upon muscle activation, but the distribution between them changed substantially, indicating that a fraction (17% +/- 2%) of myosin heads undergoes a large (at least 30 degrees) axial rotation of the myosin light chain domain upon force generation and muscle contraction. The resulting model helps explain why this observation has remained so elusive and provides insight into the mechanisms by which motor protein structural transitions drive molecular motility.

For the perception of the hardness of compliant materials, several cues are available. In this paper, the relative roles of force/displacement and surface deformation cues are investigated. We have measured discrimination thresholds with silicone rubber stimuli of differing thickness and compliance. Also, the influence of the finger span is assessed. When compliance is expressed as the Young's modulus, the thresholds in the different conditions follow Weber's law with a Weber fraction of 15 percent. When the surface deformation cue was removed, thresholds more than trebled. Under the assumption of optimal cue combination, this suggests that a large fraction of the information comes from the surface deformation cue. Using a matching experiment, we found that differences in object thickness are correctly taken into account. When cues appear to contradict each other, the conflict is resolved by means of a compromise.

The fundamentally stochastic nature of neuronal growth has hardly been addressed in neuroscience. We report on the stochastic fluctuations of a neuronal growth cone's leading edge movement, the basic step in neuronal growth. Describing the edge movement as a stochastic bistable process leads to an isotropic noise parameter that is successfully used to test the model. An analysis of growth cone motility confirms the model, and predicts that linear changes of the bistable potential, as known from stochastic filtering, result in directed growth cone translocation.

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in

The mammalian brain contains glycogen, which is located predominantly in astrocytes, but its function is unclear. A principal role for brain glycogen as an energy reserve, analogous to its role in the periphery, had been universally dismissed based on its relatively low concentration, an assumption apparently reinforced by the limited duration that the brain can function in the absence of glucose. However, during insulin-induced hypoglycaemia, where brain glucose availability is limited, glycogen content falls first in areas with the highest metabolic rate, suggesting that glycogen provides fuel to support brain function during pathological hypoglycaemia. General anaesthesia results in elevated brain glycogen suggesting quiescent neurones allow glycogen accumulation, and as long ago as the 1950s it was shown that brain glycogen accumulates during sleep, is mobilized upon waking, and that sleep deprivation results in region-specific decreases in brain glycogen, implying a supportive functional role for brain glycogen in the conscious, awake brain. Interest in brain glycogen has recently been re-awakened by the first continuous in vivo measurements using NMR spectroscopy, by the general acceptance of metabolic coupling between glia and neurones involving intercellular transfer of energy substrate, and by studies supporting a prominent physiological role for brain glycogen as a provider of supplemental energy substrate during periods of increased tissue energy demand, when ambient normoglycaemic glucose is unable to meet immediate energy requirements.

Abstract

We tested the hypothesis that astrocytic glycogen supports axon function under both pathological and physiological conditions. Functional activity of the rat (RON) or mouse optic nerve (MON), representative central white matter tracts, was assessed electrophysiologically as the area under the supramaximal compound action potential (CAP). During aglycaemia the CAP area of rodent optic nerve persisted for up to 30 min, after which the CAP rapidly failed. Glycogen content measured biochemically during the aglycaemic insult fell with a time course compatible with its rapid degradation in the absence of glucose. Pharmacological up-regulation of glycogen content prior to the aglycaemic insult with incubation in hyperglycaemic ambient glucose delayed CAP failure, whereas down-regulation of glycogen content induced by nor-adrenaline accelerated CAP failure. Inhibiting lactate transfer between astrocytes and axons during aglycaemia, where glycogen is the only utilisable energy reserve, resulted in accelerated CAP failure, implying that glycogen-derived lactate supports function when exogenous energy metabolites are withdrawn. Under normoglycaemic conditions glycogen content decreased during high frequency axon discharge, although CAP function was fully maintained. Both prior depletion of glycogen content, or blocking axonal lactate uptake rendered nerves incapable of fully supporting CAP function during high frequency firing in the presence of normoglycaemic glucose. These results indicated that during aglycaemia and increased metabolic demand, astrocytic glycogen was degraded to form lactate, which was used as a supplemental energy source when ambient normoglycaemic glucose was incapable of meeting immediate tissue energy demands.

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

This personal recollection looks at the evolution of ideas about the dynamics of the blood oxygenation level dependent (BOLD) signal, with an emphasis on the balloon model. From the first detection of the BOLD response it has been clear that the signal exhibits interesting dynamics, such as a pronounced and long-lasting post-stimulus undershoot. The BOLD response, reflecting a change in local deoxyhemoglobin, is a combination of a hemodynamic response, related to changes in blood flow and venous blood volume, and a metabolic response related to oxygen metabolism. Modeling is potentially a way to understand the complex path from changes in neural activity to the BOLD signal. In the early days of fMRI it was hoped that the hemodynamic/metabolic response could be modeled in a unitary way, with blood flow, oxygen metabolism, and venous blood volume the physiological factors that affect local deoxyhemoglobin all tightly linked. The balloon model was an attempt to do this, based on the physiological ideas of limited oxygen delivery at baseline and a slow recovery of venous blood volume after the stimulus (the balloon effect), and this simple model of the physiology worked well to simulate the BOLD response. However, subsequent experiments suggest a more complicated picture of the underlying physiology, with blood flow and oxygen metabolism driven in parallel, possibly by different aspects of neural activity. In addition, it is still not clear whether the post-stimulus undershoot is a hemodynamic or a metabolic phenomenon. although the original venous balloon effect is unlikely to be the full explanation, and a flow undershoot is likely to be important. Although our understanding of the physics of the BOLD response is now reasonably solid, our understanding of the underlying physiological relationships is still relatively poor, and this is the primary hurdle for future models of BOLD dynamics. (C) 2012 Elsevier Inc.

The basal ganglia (BG), serving as an intermediate bridge between the cerebral cortex and thalamus, are believed to play crucial roles in controlling absence seizure activities generated by the pathological corticothalamic system. Inspired by recent experiments, here we systematically investigate the contribution of a novel identified GABAergic pallido-cortical pathway, projecting from the globus pallidus externa (GPe) in the BG to the cerebral cortex, to the control of absence seizures. By computational modelling, we find that both increasing the activation of GPe neurons and enhancing the coupling strength of the inhibitory pallido-cortical pathway can suppress the bilaterally synchronous 2-4 Hz spike and wave discharges (SWDs) during absence seizures. Appropriate tuning of several GPe-related pathways may also trigger the SWD suppression, through modulating the activation level of GPe neurons. Furthermore, we show that the previously discovered bidirectional control of absence seizures due to the competition between other two BG output pathways also exists in our established model. Importantly, such bidirectional control is shaped by the coupling strength of this direct GABAergic pallido-cortical pathway. Our work suggests that the novel identified pallido-cortical pathway has a functional role in controlling absence seizures and the presented results might provide testable hypotheses for future experimental studies.

Absence epilepsy is believed to be associated with the abnormal interactions between the cerebral cortex and thalamus. Besides the direct coupling, anatomical evidence indicates that the cerebral cortex and thalamus also communicate indirectly through an important intermediate bridge-basal ganglia. It has been thus postulated that the basal ganglia might play key roles in the modulation of absence seizures, but the relevant biophysical mechanisms are still not completely established. Using a biophysically based model, we demonstrate here that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. Under certain conditions, these two types of seizure control are observed to coexist in the same network. More importantly, due to the competition between the inhibitory SNr-TRN and SNr-SRN pathways, we find that both decreasing and increasing the activation of SNr neurons from the normal level may considerably suppress the generation of spike-and-slow wave discharges in the coexistence region. Overall, these results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder.

Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.

Energy homeostasis in the brain is maintained by oxidative metabolism of glucose, primarily to fulfil the energy demand associated with ionic movements in neurons and astrocytes. In this contribution we review the experimental evidence that grounds a specific role of glycogen metabolism in supporting the functional energetic needs of astrocytes during the removal of extracellular potassium. Based on theoretical considerations, we further discuss the hypothesis that the mobilization of glycogen in astrocytes serves the purpose to enhance the availability of glucose for neuronal glycolytic and oxidative metabolism at the onset of stimulation. Finally, we provide an evolutionary perspective for explaining the selection of glycogen as carbohydrate reserve in the energy-sensing machinery of cell metabolism.

Event-related potentials (ERPs) and oscillations (EROs) are reliable measures of cognition, but they require time-locked electroencephalographic (EEG) data to repetitive triggers that are not available in continuous sensory input streams. However, such real-life-like stimulation by videos or virtual-reality environments may serve as powerful means of creating specific cognitive or affective states and help to investigate dysfunctions in psychiatric and neurological disorders more efficiently. This study aims to develop a method to generate ERPs and EROs during watching videos. Repeated luminance changes were introduced on short video segments, while EEGs of 10 subjects were recorded. The ERP/EROs time-locked to these distortions were analyzed in time and time-frequency domains and tested for their cognitive significance through a long term memory test that included frames from the watched videos. For each subject, ERPs and EROs corresponding to video segments of recalled images with 25% shortest and 25% longest reaction times were compared. ERPs produced by transient luminance changes displayed statistically significant fluctuations both in time and time-frequency domains. Statistical analyses showed that a positivity around 450 ms, a negativity around 500 ms and delta and theta EROs correlated with memory performance. Few studies mixed video streams with simultaneous ERP/ERO experiments with discrete task-relevant or passively presented auditory or somatosensory stimuli, while the present study, by obtaining ERPs and EROs to task-irrelevant events in the same sensory modality as that of the continuous sensory input, produces minimal interference with the main focus of attention on the video stream.

In this paper, we develop a theory for viscoelastic behavior of large membrane deformations and apply the analysis to the relaxation of projections produced by small micropipette aspiration of red cell discocytes. We show that this relaxation is dominated by the membrane viscosity and that the cytoplasmic and extracellular fluid flow have negligible influence on the relaxation time and can be neglected. From preliminary data, we estimate the total membrane

It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.

Single-unit studies in monkeys have demonstrated that neurons in the prefrontal cortex predict the reward type, reward amount or reward availability associated with a stimulus. To examine contributions of pyramidal cells and interneurons in reward processing, single-unit activity was extracellularly recorded in prefrontal cortices of four monkeys performing a reward prediction task. Based on their shapes of spike waveforms, prefrontal neurons were classified into broad-spike and narrow-spike units that represented putative pyramidal cells and interneurons, respectively. We mainly observed that narrow-spike neurons showed higher firing rates but less bursty discharges than did broad-spike neurons. Both narrow-spike and broad-spike cells selectively responded to the stimulus, reward and their interaction, and the proportions of each type of selective neurons were similar between the two cell classes. Moreover, the two types of cells displayed equal reliability of reward or stimulus discrimination. Furthermore, we found that broad-spike and narrow-spike cells showed distinct mechanisms for encoding reward or stimulus information. Broad-spike neurons raised their firing rate relative to the baseline rate to represent the preferred reward or stimulus information, whereas narrow-spike neurons inhibited their firing rate lower than the baseline rate to encode the non-preferred reward or stimulus information. Our results suggest that narrow-spike and broad-spike cells were equally involved in reward and stimulus processing in the prefrontal cortex. They utilized a binary strategy to complementarily represent reward or stimulus information, which was consistent with the task structure in which the monkeys were required to remember two reward conditions and two visual stimuli.

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

Understanding how skin microstructure affects slowly adapting type I (SA-I) mechanoreceptors in encoding edge discontinuities is fundamental to understanding our sense of touch. Skin microstructure, in particular papillary ridges, has been thought to contribute to edge and gap sensation. Cauna's 1954 model of touch sensibility describes a functional relationship between papillary ridges and edge sensation. His lever arm model proposes that the papillary ridge (exterior fingerprint line) and underlying intermediate ridge operate as a single unit, with the intermediate ridge acting as a lever which magnifies indentation imposed at the papillary ridge. This paper contests the validity of the lever arm model. While correctly representing the anatomy, this mechanism inaccurately characterizes the function of the papillary ridges. Finite element analysis and assessment of the critical anatomy indicate that papillary ridges have little direct effect on how SA-I receptors respond to the indentation of static edges. Our analysis supports a revised (stiff shell-elastic bending support) interpretation where the epidermis is split into two major layers with a stiff, deformable shell over an elastic bending support. Recent physiological, electrophysiological, and psychophysical findings support our conclusion that the function of the intermediate ridge is distinct from the function of the papillary ridge.

Our ability to perceive and discriminate textures relies on the transduction and processing of complex, high-frequency vibrations elicited in the fingertip as it is scanned across a surface. How naturalistic vibrations, and by extension texture, are encoded in the responses of neurons in primary somatosensory cortex (S1) is unknown. Combining single unit recordings in awake macaques and perceptual judgments obtained from human subjects, we show that vibratory amplitude is encoded in the strength of the response evoked in S1 neurons. In contrast, the frequency composition of the vibrations, up to 800 Hz, is not encoded in neuronal firing rates, but rather in the phase-locked responses of a subpopulation of neurons. Moreover, analysis of perceptual judgments suggests that spike timing not only conveys stimulus information but also shapes tactile perception. We conclude that information about the amplitude and frequency of natural vibrations is multiplexed at different time scales in S1, and encoded in the rate and temporal patterning of the response, respectively.

Deflection of the hair bundle atop a sensory hair cell modulates the open probability of mechanosensitive ion channels. In response to sustained deflections, hair cells adapt. Two fundamentally distinct models have been proposed to explain transducer adaptation. Both models support the notion that channel open probability is modulated by calcium that enters via the transduction channels. Both also suggest that the primary effect of adaptation is to shift the deflection-response [I(X)] relationship in the direction of the applied stimulus, thus maintaining hair bundle sensitivity. The models differ in several respects. They operate on different time scales: the faster on the order of a few milliseconds or less and the slower on the order of 10 ms or more. The model proposed to explain fast adaptation suggests that calcium enters and binds at or near the transduction channels to stabilize a closed conformation. The model proposed to explain the slower adaptation suggests that adaptation is mediated by an active, force-generating process that regulates the effective stimulus applied to the transduction channels. Here we discuss the evidence in support of each model and consider the possibility that both may function to varying degrees in hair cells of different species and sensory organs.

Reduction of the cerebral metabolic rate of glucose is one of the most predominant abnormalities generally found in the Alzheimer brain, whereas the cerebral metabolic rate of oxygen is only slightly diminished or not at all the beginning of this dementive disorder. This metabolic abnormality may induce severe functional disturbances, obviously preceding morphobiological changes. From the cerebral metabolic rates of oxidized glucose and oxygen, the cerebral ATP formation rate was calculated in incipient early-onset, incipient late-onset and stable advanced dementia of Alzheimer type. A reduction of ATP formation was found from at least 7% in incipient early-onset, to around 20% in incipient late-onset DAT, and from 35% to more than 50% in stable advanced dementia. This approximation was adjusted to findings demonstrating diminished activities of enzymes active in glucose metabolism and formation of oxidation equivalents for ATP production from substrates other than glucose. A reduction for energy formation to the same range was found, as was also recently reported, in vivo in Alzheimer patients. From this rather theoretical point of view, a permanent loss of energy by at least 7-20% in incipient and progressively advancing dementia of the Alzheimer type may be assumed, with an increasing tendency in stable advanced dementia to around 50% energy loss. This energy deficit may have drastic impacts on brain function.

We study the impact of human activity patterns on information diffusion. To this end we ran a viral email experiment involving 31,183 individuals in which we were able to track a specific piece of information through the social network. We found that, contrary to traditional models, information travels at an unexpectedly slow pace. By using a branching model which accurately describes the experiment, we show that the large heterogeneity found in the response time is responsible for the slow dynamics of information at the collective level. Given the generality of our result, we discuss the important implications of this finding while modeling human dynamical collective phenomena.

Two different bifurcation scenarios of firing patterns with decreasing extracellular calcium concentrations were observed in identical sciatic nerve fibers of a chronic constriction injury (CCI) model when the extracellular 4-aminopyridine concentrations were fixed at two different levels. Both processes proceeded from period-1 bursting to period-1 spiking via complex or simple processes. Multiple typical experimental examples manifested dynamics closely matching those simulated in a recently proposed 4-dimensional model to describe the nonlinear dynamics of the CCI model, which included most cases of the bifurcation scenarios. As the extracellular 4-aminopyridine concentrations is increased, the structure of the bifurcation scenario becomes more complex. The results provide a basic framework for identifying the relationships between different neural firing patterns and different bifurcation scenarios and for revealing the complex nonlinear dynamics of neural firing patterns. The potential roles of the basic bifurcation structures in identifying the information process mechanism are discussed.

During object manipulation tasks, the brain selects and implements action-phase controllers that use sensory predictions and afferent signals to tailor motor output to the physical properties of the objects involved. Analysis of signals in tactile afferent neurons and central processes in humans reveals how contact events are encoded and used to monitor and update task performance.

We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabasi-Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D, in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree [Formula: see text] and the asymmetry parameter [Formula: see text] in the SFN, and Matthew effects are also found to occur by varying [Formula: see text] and [Formula: see text]. Furthermore, emergences of LTP and LTD of synaptic strengths are investigated in details via our own microscopic methods based on both the distributions of time delays between the burst onset times of the pre- and the post-synaptic neurons and the pair-correlations between the pre- and the post-synaptic instantaneous individual burst rates (IIBRs). Finally, a multiplicative STDP case (depending on states) with soft bounds is also investigated in comparison with the additive STDP case (independent of states) with hard bounds. Due to the soft bounds, a Matthew effect with some quantitative differences is also found to occur for the case of multiplicative STDP.

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabasi-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l * and the asymmetry parameter Delta l in the SFN.

Neurons use significant amounts of energy to generate signals. Recent studies of retina and brain connect this energy usage to the ability to transmit information. The identification of energy-efficient neural circuits and codes suggests new ways of understanding the function, design and evolution of nervous systems.

In 1969 Barlow introduced the phrase

Different global patterns of brain activity are associated with distinct arousal and behavioral states of an animal, but how the brain rapidly switches between different states remains unclear. We here report that repetitive high-frequency burst spiking of a single rat cortical neuron could trigger a switch between the cortical states resembling slow-wave and rapid-eye-movement sleep. This is reflected in the switching of the membrane potential of the stimulated neuron from slow UP/DOWN oscillations to a persistent-UP state or vice versa, with concurrent changes in the temporal pattern of cortical local field potential (LFP) recorded several millimeters away. These results point to the power of single cortical neurons in modulating the behavioral state of an animal.

The purpose of this study was to investigate activation-induced hypermetabolism and hyperemia by using a multifrequency (4, 8, and 16 Hz) reversing-checkerboard visual stimulation paradigm. Specifically, we sought to (i) quantify the relative contributions of the oxidative and nonoxidative metabolic pathways in meeting the increased energy demands [i.e., ATP production (J(ATP))] of task-induced neuronal activation and (ii) determine whether task-induced cerebral blood flow (CBF) augmentation was driven by oxidative or nonoxidative metabolic pathways. Focal increases in CBF, cerebral metabolic rate of oxygen (CMRO(2); i.e., index of aerobic metabolism), and lactate production (J(Lac); i.e., index of anaerobic metabolism) were measured by using physiologically quantitative MRI and spectroscopy methods. Task-induced increases in J(ATP) were small (12.2-16.7%) at all stimulation frequencies and were generated by aerobic metabolism (approximately 98%), with %DeltaJ(ATP) being linearly correlated with the percentage change in CMRO(2) (r = 1.00, P < 0.001). In contrast, task-induced increases in CBF were large (51.7-65.1%) and negatively correlated with the percentage change in CMRO(2) (r = -0.64, P = 0.024), but positively correlated with %DeltaJ(Lac) (r = 0.91, P < 0.001). These results indicate that (i) the energy demand of task-induced brain activation is small (approximately 15%) relative to the hyperemic response (approximately 60%), (ii) this energy demand is met through oxidative metabolism, and (iii) the CBF response is mediated by factors other than oxygen demand.

Sensory neurons innervating the skin encode the familiar sensations of temperature, touch and pain. An explosion of progress has revealed unanticipated cellular and molecular complexity in these senses. It is now clear that perception of a single stimulus, such as heat, requires several transduction mechanisms. Conversely, a given protein may contribute to multiple senses, such as heat and touch. Recent studies have also led to the surprising insight that skin cells might transduce temperature and touch. To break the code underlying somatosensation, we must therefore understand how the skin's sensory functions are divided among signalling molecules and cell types.

BACKGROUND: Stroke is the second leading cause of death worldwide and the most common cause of adult-acquired disability in many nations. Thus, attenuating the damage after ischemic injury and improving patient prognosis are of great importance. We have indicated that ischemic preconditioning (IP) can effectively reduce the damage of ischemia reperfusion and that inhibition of gap junctions may further reduce this damage. Although we confirmed that the function of gap junctions is closely associated with glutamate, we did not investigate the mechanism. In the present study, we aimed to clarify whether the blockade of cellular communication at gap junctions leads to significant reductions in the levels of glutamate released by astrocytes following cerebral ischemia. METHODS: To explore this hypothesis, we utilized the specific blocking agent carbenoxolone (CBX) to inhibit the opening and internalization of connexin 43 channels in an in vitro model of oxygen-glucose deprivation/re-oxygenation (OGD/R), following IP. RESULTS: OGD/R resulted in extensive astrocytic glutamate release following upregulation of hemichannel activity, thus increasing reactive oxygen species (ROS) generation and subsequent cell death. However, we observed significant increases in neuronal survival in neuron-astrocyte co-cultures that were subjected to IP prior to OGD/R. Moreover, the addition of CBX enhanced the protective effects of IP during the re-oxygenation period following OGD, by means of blocking the release of glutamate, increasing the level of the excitatory amino acid transporter 1, and downregulating glutamine expression. CONCLUSIONS: Our results suggest that combined use of IP and CBX represents a novel therapeutic strategy to attenuate damage from cerebral ischemia with minimal adverse side effects.

Touch submodalities, such as flutter and pressure, are mediated by somatosensory afferents whose terminal specializations extract tactile features and encode them as action potential trains with unique activity patterns. Whether non-neuronal cells tune touch receptors through active or passive mechanisms is debated. Terminal specializations are thought to function as passive mechanical filters analogous to the cochlea's basilar membrane, which deconstructs complex sounds into tones that are transduced by mechanosensory hair cells. The model that cutaneous specializations are merely passive has been recently challenged because epidermal cells express sensory ion channels and neurotransmitters; however, direct evidence that epidermal cells excite tactile afferents is lacking. Epidermal Merkel cells display features of sensory receptor cells and make 'synapse-like' contacts with slowly adapting type I (SAI) afferents. These complexes, which encode spatial features such as edges and texture, localize to skin regions with high tactile acuity, including whisker follicles, fingertips and touch domes. Here we show that Merkel cells actively participate in touch reception in mice. Merkel cells display fast, touch-evoked mechanotransduction currents. Optogenetic approaches in intact skin show that Merkel cells are both necessary and sufficient for sustained action-potential firing in tactile afferents. Recordings from touch-dome afferents lacking Merkel cells demonstrate that Merkel cells confer high-frequency responses to dynamic stimuli and enable sustained firing. These data are the first, to our knowledge, to directly demonstrate a functional, excitatory connection between epidermal cells and sensory neurons. Together, these findings indicate that Merkel cells actively tune mechanosensory responses to facilitate high spatio-temporal acuity. Moreover, our results indicate a division of labour in the Merkel cell-neurite complex: Merkel cells signal static stimuli, such as pressure, whereas sensory afferents transduce dynamic stimuli, such as moving gratings. Thus, the Merkel cell-neurite complex is an unique sensory structure composed of two different receptor cell types specialized for distinct elements of discriminative touch.

Astrocytes can release the excitatory transmitter glutamate which is capable of modulating activity in nearby neurons. This astrocytic glutamate release can occur through six known mechanisms: (i) reversal of uptake by glutamate transporters (ii) anion channel opening induced by cell swelling, (iii) Ca2+-dependent exocytosis, (iv) glutamate exchange via the cystine-glutamate antiporter, (v) release through ionotropic purinergic receptors and (vi) functional unpaired connexons,

Precise timing of spikes and temporal locking are key elements of neural computation. Here we demonstrate how even strongly heterogeneous, deterministic neural networks with delayed interactions and complex topology can exhibit periodic patterns of spikes that are precisely timed. We develop an analytical method to find the set of all networks exhibiting a predefined pattern dynamics. Such patterns may be arbitrarily long and of complicated temporal structure. We point out that the same pattern can exist in very different networks and have different stability properties.

Living cells sense the rigidity of their environment and adapt their activity to it. In particular, cells cultured on elastic substrates align their shape and their traction forces along the direction of highest stiffness and preferably migrate towards stiffer regions. Although numerous studies investigated the role of adhesion complexes in rigidity sensing, less is known about the specific contribution of acto-myosin based contractility. Here we used a custom-made single-cell technique to measure the traction force as well as the speed of shortening of isolated myoblasts deflecting microplates of variable stiffness. The rate of force generation increased with increasing stiffness and followed a Hill force-velocity relationship. Hence, cell response to stiffness was similar to muscle adaptation to load, reflecting the force-dependent kinetics of myosin binding to actin. These results reveal an unexpected mechanism of rigidity sensing, whereby the contractile acto-myosin units themselves can act as sensors. This mechanism may translate anisotropy in substrate rigidity into anisotropy in cytoskeletal tension, and could thus coordinate local activity of adhesion complexes and guide cell migration along rigidity gradients.

Electrical activities of excitable cells produce diverse spiking-bursting patterns. The dynamics of the neuronal responses can be changed due to the variations of ionic concentrations between outside and inside the cell membrane. We investigate such type of spiking-bursting patterns under the effect of an electromagnetic induction on an excitable neuron model. The effect of electromagnetic induction across the membrane potential can be considered to analyze the collective behavior for signal processing. The paper addresses the issue of the electromagnetic flow on a modified Hindmarsh-Rose model (H-R) which preserves biophysical neurocomputational properties of a class of neuron models. The different types of firing activities such as square wave bursting, chattering, fast spiking, periodic spiking, mixed-mode oscillations etc. can be observed using different injected current stimulus. The improved version of the model includes more parameter sets and the multiple electrical activities are exhibited in different parameter regimes. We perform the bifurcation analysis analytically and numerically with respect to the key parameters which reveals the properties of the fast-slow system for neuronal responses. The firing activities can be suppressed/enhanced using the different external stimulus current and by allowing a noise induced current. To study the electrical activities of neural computation, the improved neuron model is suitable for further investigation.

We developed a framework to study brain dynamics under cognition. In particular, we investigated the spatiotemporal properties of brain state switches under cognition. The lack of electroencephalography stationarity is exploited as one of the signatures of the metastability of brain states. We correlated power law exponents in the variables that we proposed to describe brain states, and dynamical properties of non-stationarities with cognitive conditions. This framework was successfully tested with three different datasets: a working memory dataset, an Alzheimer disease dataset, and an emotions dataset. We discuss the temporal organization of switches between states, providing evidence suggesting the need to reconsider the piecewise model, in which switches appear at discrete times. Instead, we propose a more dynamically rich view, in which besides the seemingly discrete switches, switches between neighbouring states occur all the time. These micro switches are not (physical) noise, as their properties are also affected by cognition.

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

Glutamate, released at a majority of excitatory synapses in the central nervous system, depolarizes neurons by acting at specific receptors. Its action is terminated by removal from the synaptic cleft mostly via Na(+)-dependent uptake systems located on both neurons and astrocytes. Here we report that glutamate, in addition to its receptor-mediated actions on neuronal excitability, stimulates glycolysis--i.e., glucose utilization and lactate production--in astrocytes. This metabolic action is mediated by activation of a Na(+)-dependent uptake system and not by interaction with receptors. The mechanism involves the Na+/K(+)-ATPase, which is activated by an increase in the intracellular concentration of Na+ cotransported with glutamate by the electrogenic uptake system. Thus, when glutamate is released from active synapses and taken up by astrocytes, the newly identified signaling pathway described here would provide a simple and direct mechanism to tightly couple neuronal activity to glucose utilization. In addition, glutamate-stimulated glycolysis is consistent with data obtained from functional brain imaging studies indicating local nonoxidative glucose utilization during physiological activation.

In the end of 2019, a new type of coronavirus first appeared in Wuhan. Through the real-data of COVID-19 from January 23 to March 18, 2020, this paper proposes a fractional SEIHDR model based on the coupling effect of inter-city networks. At the same time, the proposed model considers the mortality rates (exposure, infection and hospitalization) and the infectivity of individuals during the incubation period. By applying the least squares method and prediction-correction method, the proposed system is fitted and predicted based on the real-data from January 23 to March 18 - m where m represents predict days. Compared with the integer system, the non-network fractional model has been verified and can better fit the data of Beijing, Shanghai, Wuhan and Huanggang. Compared with the no-network case, results show that the proposed system with inter-city network may not be able to better describe the spread of disease in China due to the lock and isolation measures, but this may have a significant impact on countries that has no closure measures. Meanwhile, the proposed model is more suitable for the data of Japan, the USA from January 22 and February 1 to April 16 and Italy from February 24 to March 31. Then, the proposed fractional model can also predict the peak of diagnosis. Furthermore, the existence, uniqueness and boundedness of a nonnegative solution are considered in the proposed system. Afterward, the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number R 0

The notion that animals can detect the Earth's magnetic field was once ridiculed, but is now well established. Yet the biological nature of such magnetosensing phenomenon remains unknown. Here, we report a putative magnetic receptor (Drosophila CG8198, here named MagR) and a multimeric magnetosensing rod-like protein complex, identified by theoretical postulation and genome-wide screening, and validated with cellular, biochemical, structural and biophysical methods. The magnetosensing complex consists of the identified putative magnetoreceptor and known magnetoreception-related photoreceptor cryptochromes (Cry), has the attributes of both Cry- and iron-based systems, and exhibits spontaneous alignment in magnetic fields, including that of the Earth. Such a protein complex may form the basis of magnetoreception in animals, and may lead to applications across multiple fields.

Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.

In vitro and in vivo studies support the involvement of connexin 43-based cell-cell channels and hemichannels in cell death propagation induced by ischemia-reperfusion. In this context, open connexin hemichannels in the plasma membrane have been proposed to act as accelerators of cell death. Progress on the mechanisms underlying the cell permeabilization induced by ischemia-reperfusion reveals the involvement of several factors leading to an augmented open probability and increased number of hemichannels on the cell surface. While open probability can be increased by a reduction in extracellular concentration of divalent cations and changes in covalent modifications of connexin 43 (oxidation and phosphorylation), increase in number of hemichannels requires an elevation of the intracellular free Ca(2+) concentration. Reversal of connexin 43 redox changes and membrane permeabilization can be induced by intracellular, but not extracellular, reducing agents, suggesting a cytoplasmic localization of the redox sensor(s). In agreement, hemichannels formed by connexin 45, which lacks cytoplasmic cysteines, or by connexin 43 with its C-terminal domain truncated to remove its cysteines are insensitive to reducing agents. Although further studies are required for a precise localization of the redox sensor of connexin 43 hemichannels, modulation of the redox potential is proposed as a target for the design of pharmacological tools to reduce cell death induced by ischemia-reperfusion in connexin 43-expressing cells.

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

Contraction at the cellular level is vital for living organisms. The most prominent type of contractile cells are heart muscle cells, a less-well-known example is blood platelets. Blood platelets activate and interlink at injured blood vessel sites, finally contracting to form a compact blood clot. They are ideal model cells to study the mechanisms of cellular contraction, as they are simple, having no nucleus, and their activation can be triggered and synchronized by the addition of thrombin. We have studied contraction using human blood platelets, employing traction force microscopy, a single-cell technique that enables time-resolved measurements of cellular forces on soft substrates with elasticities in the physiological range ( approximately 4 kPa). We found that platelet contraction reaches a steady state after 25 min with total forces of approximately 34 nN. These forces are considerably larger than what was previously reported for platelets in aggregates, demonstrating the importance of a single-cell approach for studies of platelet contraction. Compared with other contractile cells, we find that platelets are unique, because force fields are nearly isotropic, with forces pointing toward the center of the cell area.

Functional brain imaging is based on the display of computer-derived images of changes in physiological and/or biochemical functions altered by activation or depression of local functional activities in the brain. This article reviews the physiological and biochemical mechanisms involved.

Differentiation of the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) is a persistent clinical challenge [1]. Based on positron emission tomography (PET) studies with [(18)F]-fluorodeoxyglucose (FDG) during sleep and anesthesia, the global cerebral metabolic rate of glucose has been proposed as an indicator of consciousness [2, 3]. Likewise, FDG-PET may contribute to the clinical diagnosis of disorders of consciousness (DOCs) [4, 5]. However, current methods are non-quantitative and have important drawbacks deriving from visually guided assessment of relative changes in brain metabolism [4]. We here used FDG-PET to measure resting state brain glucose metabolism in 131 DOC patients to identify objective quantitative metabolic indicators and predictors of awareness. Quantitation of images was performed by normalizing to extracerebral tissue. We show that 42% of normal cortical activity represents the minimal energetic requirement for the presence of conscious awareness. Overall, the cerebral metabolic rate accounted for the current level, or imminent return, of awareness in 94% of the patient population, suggesting a global energetic threshold effect, associated with the reemergence of consciousness after brain injury. Our data further revealed that regional variations relative to the global resting metabolic level reflect preservation of specific cognitive or sensory modules, such as vision and language comprehension. These findings provide a simple and objective metabolic marker of consciousness, which can readily be implemented clinically. The direct correlation between brain metabolism and behavior further suggests that DOCs can fundamentally be understood as pathological neuroenergetic conditions and provide a unifying physiological basis for these syndromes.

In this paper, the transitions of burst synchronization are explored in a neuronal network consisting of subnetworks. The studied network is composed of electrically coupled bursting Hindmarsh-Rose neurons. Numerical results show that two types of burst synchronization transitions can be induced not only by the variations of intra- and intercoupling strengths but also by changing the probability of random links between different subnetworks and the number of subnetworks. Furthermore, we find that the underlying mechanisms for these two bursting synchronization transitions are different: one is due to the change of spike numbers per burst, while the other is caused by the change of the bursting type. Considering that changes in the coupling strengths and neuronal connections are closely interlaced with brain plasticity, the presented results could have important implications for the role of the brain plasticity in some functional behavior that are associated with synchronization.

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of

Brain hypoxia-ischemia is a relatively common and serious problem in neonates and in adults. Its consequences include long-term histological and behavioral changes and reduction in seizure threshold. Gap junction intercellular communication is pivotal in the spread of hypoxia-ischemia related injury and in mediating its long-term effects. This review provides a comprehensive and critical review of hypoxia-ischemia and hypoxia in the brain and the potential role of gap junctions in the spread of the neuronal injury induced by these insults. It also presents the effects of hypoxia-ischemia and of hypoxia on the state of gap junctions in vitro and in vivo. Understanding the mechanisms involved in gap junction-mediated neuronal injury due to hypoxia will lead to the development of novel therapeutic strategies.

OBJECTIVE: To show that PET investigations of brain function in patients with major depression can contribute with valuable pathophysiological knowledge about brain function of these states. METHODS: PET studies of cerebral blood flow or glucose metabolism in patients with unipolar or bipolar depression were reviewed. RESULTS: The studies have great discrepancies related to sample size, subject selection, imaging protocol and image analysis. In spite of this shortcoming, there is evidence that patients with major depression have reduced blood flow and metabolism in the prefrontal cortex, particularly when they exhibit psychomotor retardation. Abnormalities are also found in the anterior cingulate gyrus and the basal ganglia. A few studies point to the possibility that response to antidepressant treatment can be predicted from PET scans. CONCLUSION: This evidence is consistent with the hypothesis that depressive symptoms are caused by dysfunction of regions of the limbic system and the frontal lobes in close connection with the basal ganglia.

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional

Excessive synchronization of neurons in cerebral cortex is believed to play a crucial role in the emergence of neuropsychological disorders such as Parkinson's disease, epilepsy and essential tremor. This study, by constructing a modular neuronal network with modified Oja's learning rule, explores how to eliminate the pathological synchronized rhythm of interacted busting neurons numerically. When all neurons in the modular neuronal network are strongly synchronous within a specific range of coupling strength, the result reveals that synaptic plasticity with large learning rate can suppress bursting synchronization effectively. For the relative small learning rate not capable of suppressing synchronization, the technique of nonlinear delayed feedback control including differential feedback control and direct feedback control is further proposed to reduce the synchronized bursting state of coupled neurons. It is demonstrated that the two kinds of nonlinear feedback control can eliminate bursting synchronization significantly when the control parameters of feedback strength and feedback delay are appropriately tuned. For the former control technique, the control domain of effective synchronization suppression is similar to a semi-elliptical domain in the simulated parameter space of feedback strength and feedback delay, while for the latter one, the effective control domain is similar to a fan-shaped domain in the simulated parameter space.

By re-examining the neuronal activity energy model, we show the inadequacies in the current understanding of the energy consumption associated with neuron activity. Specifically, we show computationally that a neuron first absorbs and then consumes energy during firing action potential, and this result cannot be produced from any current neuron models or biological neural networks. Based on this finding, we provide an explanation for the observation that when neurons are excited in the brain, blood flow increases significantly while the incremental oxygen consumption is very small. We can also explain why external stimulation and perception emergence are synchronized. We also show that negative energy presence in neurons at the sub-threshold state is an essential reason that leads to blood flow incremental response time in the brain rather than neural excitation to delay.

Based on the principle of energy coding, an energy function of a variety of electric potentials of a neural population in cerebral cortex is formulated. The energy function is used to describe the energy evolution of the neuronal population with time and the coupled relationship between neurons at the subthreshold and the suprathreshold states. The Hamiltonian motion equation with the membrane potential is obtained from the neuroelectrophysiological data contaminated by Gaussian white noise. The results of this research show that the mean membrane potential is the exact solution of the motion equation of the membrane potential developed in a previously published paper. It also shows that the Hamiltonian energy function derived in this brief is not only correct but also effective. Particularly, based on the principle of energy coding, an interesting finding is that in some subsets of neurons, firing action potentials at the suprathreshold and some others simultaneously perform activities at the subthreshold level in neural ensembles. Notably, this kind of coupling has not been found in other models of biological neural networks.

This paper mainly discusses and summarize that the changes of biological energy in the brain can be expressed by the biophysical energy we constructed. Different from the electrochemical energy, the biophysical energy proposed in the paper not only can be used to simulate the activity of neurons but also be used to simulate the neural activity of large scale cortical networks, so that the scientific nature of the neural energy coding was discussed.

Rodent animal can accomplish self-locating and path-finding task by forming a cognitive map in the hippocampus representing the environment. In the classical model of the cognitive map, the system (artificial animal) needs large amounts of physical exploration to study spatial environment to solve path-finding problems, which costs too much time and energy. Although Hopfield's mental exploration model makes up for the deficiency mentioned above, the path is still not efficient enough. Moreover, his model mainly focused on the artificial neural network, and clear physiological meanings has not been addressed. In this work, based on the concept of mental exploration, neural energy coding theory has been applied to the novel calculation model to solve the path-finding problem. Energy field is constructed on the basis of the firing power of place cell clusters, and the energy field gradient can be used in mental exploration to solve path-finding problems. The study shows that the new mental exploration model can efficiently find the optimal path, and present the learning process with biophysical meaning as well. We also analyzed the parameters of the model which affect the path efficiency. This new idea verifies the importance of place cell and synapse in spatial memory and proves that energy coding is effective to study cognitive activities. This may provide the theoretical basis for the neural dynamics mechanism of spatial memory.

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.

Spatial representation is a crucial function of animal's brain. However, there is still no uniform explanation of how the spatial code is formed in different dimensional spaces to date. The main reason why place cell exhibits unique activity pattern is that the animal needs to retrieve and process spatial information. In this paper, we constructed a constrained optimization model based on information theory to explain the place field formation across species in different dimensional spaces. We proposed the following question that, using only limited amount of neural energy, how to organize the spiking locations (place field) in the available environment to obtain the most efficient spatial information representation? We solved this conditional functional extremum problem by variational techniques. The results showed that on the condition of limited neural energy, the place field will comply with a Gaussian-form distribution automatically to convey the largest amount information per spike. We also found that the animal's natural habitat property and locomotion experience statistics affected the symmetry of spatial representation in different dimensions. These findings not only reconcile the argument of whether the spatial codes of place cell are isotropic, but also provide an explanation of place field formation by an information-theoretic approach. Furtherly, this research revealed the energy economical and information efficient properties underlie the spatial representation system of the brain.

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naive Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.

Studying neural coding through neural energy is a novel view. In this paper, based on previously proposed single neuron model, the correlation between the energy consumption and the parameters of the cortex networks (amount of neurons, coupling strength, and transform delay) under an oscillational condition were researched. We found that energy distribution varies orderly as these parameters change, and it is closely related to the synchronous oscillation of the neural network. Besides, we compared this method with traditional method of relative coefficient, which shows energy method works equal to or better than the traditional one. It is novel that the synchronous activity and neural network parameters could be researched by assessing energy distribution and consumption. Therefore, the conclusion of this paper will refine the framework of neural coding theory and contribute to our understanding of the coding mechanism of the cerebral cortex. It provides a strong theoretical foundation of a novel neural coding theory-energy coding.

This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.

Our mysterious brain is believed to operate near a non-equilibrium point and generate critical self-organized avalanches in neuronal activity. A central topic in neuroscience is to elucidate the underlying circuitry mechanisms of neuronal avalanches in the brain. Recent experimental evidence has revealed significant heterogeneity in both synaptic input and output connectivity, but whether the structural heterogeneity participates in the regulation of neuronal avalanches remains poorly understood. By computational modeling, we predict that different types of structural heterogeneity contribute distinct effects on avalanche neurodynamics. In particular, neuronal avalanches can be triggered at an intermediate level of input heterogeneity, but heterogeneous output connectivity cannot evoke avalanche dynamics. In the criticality region, the co-emergence of multi-scale cortical activities is observed, and both the avalanche dynamics and neuronal oscillations are modulated by the input heterogeneity. Remarkably, we show similar results can be reproduced in networks with various types of in- and out-degree distributions. Overall, these findings not only provide details on the underlying circuitry mechanisms of nonrandom synaptic connectivity in the regulation of neuronal avalanches, but also inspire testable hypotheses for future experimental studies.

The phenomenon of spontaneous periodic up and down transitions is considered to be a significant characteristic of slow oscillations. Our previous theoretical studies have shown that the single neuron and network model can both exhibit spontaneous up and down transitions. Another characteristic of up and down dynamics is the synchronicity. So in this paper, we focused on the synchronized characteristic of up and down transitions in the network based on stimulations. Spontaneous activities showed no synchronous transitions between neurons. However, the external stimulation, mainly the stimulation frequency and the number of neurons stimulated on were related to the synchronous transitions of up and down states. The simulation results suggested that simultaneous high frequency excitation or firing of neurons in the network was responsible for the generation of synchronous transitions of up and down states. Through the observation and analysis of the findings, we have tried to explain the reason for synchronous up and down transitions and to lay the foundation for further work on the role of these synchronized transitions in cortex activity.

The present study aimed to identify the mechanism of tactile sensation by analyzing the regularity of the firing pattern of Merkel cell-neurite complex (MCNC) under the stimulation of different compression depths. The fingertips were exposed to the contact pressure of a spherical object to sense external stimuli in this study. The distribution structure of slowly adapting type I (SAI) mechanoreceptors was considered for analyzing the neural coding of tactile stimuli, especially the firing pattern of SAI neural network for perceiving the external stimulation. The numerical simulation results showed that (1) when the skin was pressed by the same sphere and the depth of the pressing finger skin and position of the force application point remained unchanged, the firing rate of the neuron depended on the synergistic effect of the number of receptors connected with the neuron and the distance between the neuron and the force application point. (2) When the fingertip was pressed by the same sphere at a constant depth and the different contact position, the overall firing rate of the MCNC neural network increased with the number of SAI mechanoreceptors in the area where the force application point was located.

Based on the FitzHugh-Nagumo (FHN) neuron model subjected to sine-Wiener (SW) noise, impacts of SW noise on weak periodic signal detection are investigated by calculating response measure Q for characterizing synchronization between the input signal and the output temporal activities of the neuron. It is numerically demonstrated that the response measure Q can achieve the optimal value under appropriate and moderate intensity or correlation time of SW noise, suggesting the occurrence of SW-noise-induced stochastic resonance. Furthermore, the optimal value of Q is sensitive to correlation time. Consequently, the correlation time of SW noise has a great influence on the performance of signal detection in the FHN neuron.

High-frequency deep brain stimulation (HF-DBS) of the subthalamic nucleus (STN), globus pallidus interna (GPi) and globus pallidus externa (GPe) are often considered as effective methods for the treatment of Parkinson's disease (PD). However, the stimulation of a single nucleus by HF-DBS can cause specific physical damage, produce side effects and usually consume more electrical energy. Therefore, we use a biophysically-based model of basal ganglia-thalamic circuits to explore more effective stimulation patterns to reduce adverse effects and save energy. In this paper, we computationally investigate the combined DBS of two nuclei with the phase deviation between two stimulation waveforms (CDBS). Three different stimulation combination strategies are proposed, i.e., STN and GPe CDBS (SED), STN and GPi CDBS (SID), as well as GPi and GPe CDBS (GGD). Resultantly, it is found that anti-phase CDBS is more effective in improving parkinsonian dynamical properties, including desynchronization of neurons and the recovery of the thalamus relay ability. Detailed simulation investigation shows that anti-phase SED and GGD are superior to SID. Besides, the energy consumption can be largely reduced by SED and GGD (72.5% and 65.5%), compared to HF-DBS. These results provide new insights into the optimal stimulation parameter and target choice of PD, which may be helpful for the clinical practice.

Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin-Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons' responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons' responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level.

The damage of dopaminergic neurons that innervate the striatum has been considered to be the proximate cause of Parkinson's disease (PD). In the dopamine-denervated state, the loss of dendritic spines and the decrease of dendritic length may prevent medium spiny neuron (MSN) from receiving too much excitatory stimuli from the cortex, thereby reducing the symptom of Parkinson's disease. However, the reduction in dendritic spine density obtained by different experiments is significantly different. We developed a biological-based network computational model to quantify the effect of dendritic spine loss and dendrites tree degeneration on basal ganglia (BG) signal regulation. Through the introduction of error index (EI), which was used to measure the attenuation of the signal, we explored the amount of dendritic spine loss and dendritic trees degradation required to restore the normal regulatory function of the network, and found that there were two ranges of dendritic spine loss that could reduce EI to normal levels in the case of dopamine at a certain level, this was also true for dendritic trees. However, although these effects were the same, the mechanisms of these two cases were significant difference. Using the method of phase diagram analysis, we gained insight into the mechanism of signal degradation. Furthermore, we explored the role of cortex in MSN morphology changes dopamine depletion-induced and found that proper adjustments to cortical activity do stop the loss in dendritic spines induced by dopamine depleted. These results suggested that modifying cortical drive onto MSN might provide a new idea on clinical therapeutic strategies for Parkinson's disease.

Hyperpolarization-activated cyclic nucleotide-gated cation current (Ih ) plays important roles in the achievement of many physiological/pathological functions in the nervous system by modulating the electrophysiological activities, such as the rebound (spike) to hyperpolarization stimulations, subthreshold membrane resonance to sinusoidal currents, and spike-timing precision to stochastic factors. In the present paper, with increasing gh (conductance of Ih ), the rebound (spike) and subthreshold resonance appear and become stronger, and the variability of the interspike intervals (ISIs) becomes lower, i.e., the enhancement of spike-timing precision, which are simulated in a conductance-based theoretical model and well explained by the nonlinear concept of bifurcation. With increasing gh , the stable node to stable focus, to coexistence behavior, and to firing via the codimension-1 bifurcations (Hopf bifurcation, saddle-node bifurcation, saddle-node bifurcations on an invariant circle, and saddle homoclinic orbit) and codimension-2 bifurcations such as Bogdanov-Takens (BT) point related to the transition between saddle-node and Hopf bifurcations, are acquired with 1- and 2-parameter bifurcation analysis. The decrease of variability of ISIs with increasing gh is induced by the fast decrease of the standard deviation of ISIs, which is related to the increase of the capacity of resisting noisy disturbance due to the firing becomes far away from the bifurcation point. The enhancement of the rebound (spike) with increasing gh builds up a relationship to the decrease of the capacity of resisting disturbance like the hyperpolarization stimulus as the resting state approaches the bifurcation point. The

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naive Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.

Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail. We find that during the depolarization of spikes in bursting this power becomes negative, which was also discovered in previous research with another energy model. We also find that the neuron's energy consumption during bursting is minimal. Especially in the spontaneous state without stimulation, the total energy consumption (2.152 x 10(-7) J) during 30 s of bursting is very similar to the biological energy consumption (2.468 x 10(-7) J) during the generation of a single action potential, as shown in Wang et al. (Neural Plast 2017, 2017a). Our results suggest that this property of low energy consumption could simply be the consequence of the biophysics of generating bursts, which is consistent with the principle of energy minimization. Our results also imply that neural energy plays a critical role in neural coding, which opens a new avenue for research of a central challenge facing neuroscience today.

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.