Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder.

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

The Effects and Regulatory Mechanism of Casein-Derived Peptide VLPVPQK in Alleviating Insulin Resistance of HepG2 Cells.

The liver plays a key role in keeping the homeostasis of glucose and lipid metabolism. Insulin resistance of the liver induced by extra glucose and lipid ingestion contributes greatly to chronic metabolic disease, which is greatly threatening to human health. The small peptide, VLPVPQK, originating from casein hydrolysates of milk, shows various health-promoting functions. However, the effects of VLPVPQK on metabolic disorders of the liver are still not fully understood. Therefore, in the present study, the effects and regulatory mechanism of VLPVPQK on insulin-resistant HepG2 cells was further investigated. The results showed that VLPVPQK exerted strong scavenging capacities against various free radicals, including oxygen radicals, hydroxyl radicals, and cellular reactive oxygen species. In addition, supplementation of VLPVPQK (62.5, 125, and 250 µM) significantly reversed the high glucose and fat (30 mM glucose and 0.2 mM palmitic acid) induced decrement of glucose uptake in HepG2 cells without affecting cell viability. Furthermore, VLPVPQK intervention affected the transcriptomic profiling of the cells. The differentially expressed (DE) genes (FDR < 0.05, and absolute fold change (FC) > 1.5) between VLPVPQK and the model group were mostly enriched in the carbohydrate metabolism-related KEGG pathways. Interestingly, the expression of two core genes (HKDC1 and G6PC1) involved in the above pathways was dramatically elevated after VLPVPQK intervention, which played a key role in regulating glucose metabolism. Furthermore, supplementation of VLPVPQK reversed the high glucose and fat-induced depression of AKR1B10. Overall, VLPVPQK could alleviate the metabolic disorder of hepatocytes by elevating the glucose uptake and eliminating the ROS, while the HKDC1 and AKR1B10 genes might be the potential target genes and play important roles in the process.

Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models.

Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.

A visual-degradation-inspired model with HSV color-encoding for contour detection.

BACKGROUND: Given energy metabolism, visual information degradation plays an essential role in the retina- lateral geniculate nucleus (LGN)-primary visual cortex (V1)-secondary visual cortex (V2) pathway, and is a pivotal issue for visual information processing. Degradation helps the visual nervous system conserve brain energy and efficiently perceive the real world even though a small fraction of visual information reaches the early visual areas. The coding of contour features (edge and corner) is achieved in the retina-LGN-V1-V2 pathway. Based on the above, we proposed a contour detection model based on degradation (CDMD). NEW METHOD: Inspired by pupillary light reflex regulation, we took into consideration the novel approach of the hue-saturation-value (HSV) module for color encoding to meet the subtle chromaticity change rather than using the traditional red-green-blue (RGB) module, following the mechanisms of dark (DA) and light (LA) adaptation processes in photoreceptors. Meanwhile, the degradation mechanism was introduced as a novel strategy focusing only on the essential information to detect contour features, mimicking contour detection by visual perception under the restriction of axons in each optic nerve biologically. Ultimately, we employed the feedback mechanism achieving the optimal HSV value for each pixel of the experimental datasets. RESULTS: We used the publicly available Berkeley Segmentation Data Set 500 (BSDS500) to assess the effectiveness of our CDMD model, introduced the F-measure to evaluate the results. The F-measure score was 0.65, achieved by our model. Moreover, CDMD with HSV has a better sensitivity for subtle chromaticity changes than CDMD with RGB. COMPARISON WITH EXISTING METHODS: Experimental results demonstrated that our CDMD model, which functions close to the real visual system, achieved a more competitive performance with low computational cost than some state-of-the-art non-deep-learning and biologically inspired models. Compared with deep-learning-based algorithms, our model contains fewer parameters and computation time, does not require additional visual features, as well as an extra training process. CONCLUSIONS: Our proposed CDMD model is a novel approach for contour detection, which mimics the cognitive function of contour detection in early visual areas, and realizes a competitive performance in image processing. It contributes to bridging the gap between the biological visual system and computer vision.

Biophysical mechanism of the interaction between default mode network and working memory network.

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09674-1.

Neural mechanism of visual information degradation from retina to V1 area.

The information processing mechanism of the visual nervous system is an unresolved scientific problem that has long puzzled neuroscientists. The amount of visual information is significantly degraded when it reaches the V1 after entering the retina; nevertheless, this does not affect our visual perception of the outside world. Currently, the mechanisms of visual information degradation from retina to V1 are still unclear. For this purpose, the current study used the experimental data summarized by Marcus E. Raichle to investigate the neural mechanisms underlying the degradation of the large amount of data from topological mapping from retina to V1, drawing on the photoreceptor model first. The obtained results showed that the image edge features of visual information were extracted by the convolution algorithm with respect to the function of synaptic plasticity when visual signals were hierarchically processed from low-level to high-level. The visual processing was characterized by the visual information degradation, and this compensatory mechanism embodied the principles of energy minimization and transmission efficiency maximization of brain activity, which matched the experimental data summarized by Marcus E. Raichle. Our results further the understanding of the information processing mechanism of the visual nervous system.

Modeling the grid cell activity on non-horizontal surfaces based on oscillatory interference modulated by gravity.

Internal representation of the space is a fundamental and crucial function of the animal's brain. Grid cells in the medial entorhinal cortex are thought to provide an environment-invariant metric system for the navigation of the animal. Most experimental and theoretical studies have focused on the horizontal planar codes of grid cell, while how this metric coordinate system is configured in the actual three-dimensional space remains unclear. Evidence has implied the spatial cognition may not be fully volumetric. We proposed an oscillatory interference model with a novel gravity and body plane modulation to simulate grid cell activity in complex space for rodents. The animal can perceive the rotation of its body plane along the local surface by sensing the gravity, causing the modulation to the dendritic oscillations. The results not only reproduce the firing patterns of the grid cell recorded from known experiments, but also predict the grid codes in novel environments. It further demonstrates that the gravity signal is indispensable for the animal's navigation, and supports the hypothesis that the periodic firing of the grid cell is intrinsically not a volumetric code in three-dimensional space. This will provide new insights to understand the spatial representation of the actual world in the brain.

Energy features in spontaneous up and down oscillations.

Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.

The Relationship between Sparseness and Energy Consumption of Neural Networks.

About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin's studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.

Instantaneous mental workload assessment using time-frequency analysis and semi-supervised learning.

The real-time assessment of mental workload (MWL) is critical for development of intelligent human-machine cooperative systems in various safety-critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.

A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level.

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.

Neurodynamic analysis of Merkel cell-neurite complex transduction mechanism during tactile sensing.

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.

The place cell activity is information-efficient constrained by energy.

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.

Energy expenditure computation of a single bursting neuron.

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 × 10-7 J) during 30 s of bursting is very similar to the biological energy consumption (2.468 × 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.

Simulation of retinal ganglion cell response using fast independent component analysis.

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 "burst" of neuronal population transmits information efficiently.

An Energy Model of Place Cell Network in Three Dimensional Space.

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.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

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.

Permutation Mutual Information: A Novel Approach for Measuring Neuronal Phase-Amplitude Coupling.

Cross-frequency phase-amplitude coupling (PAC) in neuronal oscillations network plays an important functional role in large scale neuronal communication and neuronal encoding. In the present study, a novel approach named permutation mutual information (PMI) was applied in measuring PAC. It is derived from the permutation entropy based on the mutual information theory, by which the mutual information of permutations of two time series can be evaluated. In order to verify the ability of PMI, a numerical test was performed by using both simulation data and experimental data. The performances of PMI were compared with that of two well-known methods, which were the mean vector length (MVL) and the modulation index (MI). It was found that the performance of PMI was similar to that of MI when measuring PAC intensity, but the coupling sensitivity of PMI was the highest among all these three approaches. Moreover, there was the lowest sensitivity in the MVL measurement, suggesting that MVL was a more conservative approach in detecting the existence of PAC. In addition, an ROC analysis showed that PMI performed better in measuring PAC compared to that of others. Furthermore, the experimental data, obtained from rats' hippocampal CA3 regions, were analyzed by using the three approaches. The result was essentially in line with that of the simulation performances. In a word, the results suggest that PMI is a better choice for assessing PAC under the certain conditions.