Based on the H–H equation, this study has proposed the calculation and analysis of energy expenditure for a single neuron which is activated at sup-threshold and subthreshold, as well as the ...criterion of the energy expenditure of neurons activated sup-threshold and subthreshold, which was the maximum power of a sodium ion pump. Results of the study showed that not only the electrophysiological activities of neurons were strictly restricted by the energy levels in the brain, but also the activities of neurons also had dual nature, meaning that subthreshold neurons were mainly with energy expenditure, while sup-threshold neurons were with both energy absorption and energy expenditure. These new findings were compared with the novel neuro-biophysical models that we have published last year, uncovering that the two models were essentially equivalent.
Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. ...However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors.
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.
•Augmented energy computational approach for Hodgkin-Huxley models.•Investigated energy coding patterns of major depressive disorder.•Explained biological experiments from energy consumption perspective.•Insights into study of psychiatric disorders using energy approach.
The mechanisms of MDD remain unknown due to the high complexity of the brain. Although there have been many studies exploring its mechanisms, including electrophysiology, EEG, and BOLD imaging of fMRI, these experiments cannot be performed simultaneously limited by the techniques. That makes these experiments and results independent of each other, and can only explain some aspects of major depressive disorder. Considering this, it has become a key question of how to find the connections between these experiments at the whole-brain level, especially how to explain the coding patterns of abnormal behaviors by the distinct dynamics of the whole-brain system of major depressive disorder. Here, we augmented and applied the neural energy methodology to the research of MDD, since neural energy can bridge the small-scale (neuronal activities, ion channel kinetics) and the large-scale network features (BOLD signals, local field potentials, functional connectivities), and it has been successfully used to address similar problems in other fields of neuroscience. Based on our previous neural network models, we investigated the energy coding patterns of different neuronal types with or without MDD.
How the brain encodes spatial information is an important topic. Experimental and theoretical progresses achieved in this area mainly focused on the neuronal response in the lower-dimensional space ...such as a linear track or a horizontal flat arena. How the real three-dimensional (3-D) space is represented in the brain is unknown. Grid cells in the medial entorhinal cortex and the place cells in the hippocampus are the principal spatial neurons, and the grid cells provide important inputs to the place cells. In order to simulate the place cell activity in higher dimension, we proposed a rotating-integration model to generate the place field on non-horizontal surfaces for crawling animal in 3-D space. By referring to the gravity signal as an anchor, preferred directions of the grid cell will be rotated with the animal’s body plane during navigating on the surfaces. Then, multiple grid cell patterns with distributed orientations and wavelengths are integrated to form the firing field(s) of a place cell. The results can not only account for the known experimental recordings but also predict a segment planar encoding property of place cell on novel complex surfaces. It suggests that the spatial cognition for crawling animal is achieved by a mosaic of lower-dimensional codes rather than the full volumetric perception. This work can help us understand how the spatial information provided by the external physical world is represented and processed by the neuronal systems.
The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for ...explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, and only a small fraction of visual information reaches the primary visual cortex (V1). This, nevertheless, does not affect our visual perception. Furthermore, how neurons in the secondary visual cortex (V2) encode such a small amount of visual information has yet to be addressed. To this end, the current paper established a visual network model for retina-lateral geniculate nucleus (LGN)-V1–V2 and quantitatively accounted for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrated that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode “corner” information, due to the effects of synaptic plasticity, but the similar function does not exist in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the “contour” feature (edge and corner) is achieved in the pathway of retina-LGN-V1–V2.
Among the theories of neural information coding, the neural energy coding is more accessible to global coding features than traditional neural encoding. According to the shortcomings existing in the ...neuronal energy model, that is, the non-smooth nature of the energy curve, we proposed an improved neuronal energy model in this paper. The modified energy model is a good choice for establishment of the global model of brain function. And it is also the basis of energy calculation for functional cognitive neural networks in the future.
This study surveys the interaction between working memory and long-term memory using the neural energy coding method based on a working memory model with
Ca
2
+
subsystem-induced bi-stability. We ...apply theta-burst stimulation (TBS) and high-frequency stimulation (HFS) to the bi-stable dynamical model to induce long-term memory. During studying the formation of long-term memory, we develop methods to measure the changes in energy input of these two stimuli and the corresponding energy consumption of the memory system. We investigate the minimum energy cost of these two stimuli to induce long-term memory and define an energy ratio to quantitatively describe the energy efficiency of the stimulus. We found that both stimuli induce the similar long-term effects by the dynamical model, but TBS is more energy efficient than HFS. The results provide a more comprehensive understanding in the transformation from working memory, by an energy coding approach, to long-term memory in response to the two types of long-term potentiation-induced protocols, which reflect the physiological mechanisms and neurodynamics of long-term memory generation, and also reveal the energy-efficient principle of the neural system.
The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machine collaborative systems. Recent literature has ...shown that the change of discrete CTL level during human-machine system operations can be objectively recognized using neurophysiological data and supervised learning technique. The objective of this work is to design subject-specific multi-class CTL classifier to reveal the complex unknown relationship between the operator's task performance and neurophysiological features by combining target class labeling, physiological feature reduction and selection, and ensemble classification techniques. The psychophysiological data acquisition experiments were performed under multiple human-machine process control tasks. Four or five target classes of CTL were determined by using a Gaussian mixture model and three human performance variables. By using Laplacian eigenmap, a few salient EEG features were extracted, and heart rates were used as the input features of the CTL classifier. Then, multiple support vector machines were aggregated via majority voting to create an ensemble classifier for recognizing the CTL classes. Finally, the obtained CTL classification results were compared with those of several existing methods. The results showed that the proposed methods are capable of deriving a reasonable number of target classes and low-dimensional optimal EEG features for individual human operator subjects.
This paper designs a pattern classifier based on a Nonlinear AutoRegressive model with eXogenous inputs (NARX) to reveal intricate nonlinear dynamical correlation between mental workload (MWL) of a ...human operator and psychophysiological features. The salient electroencephalogram and electrocardiogram features were selected as inputs to the NARX model, whose continuous output was discretized in terms of five MWL classes at each time instant. The orders of the NARX model were determined using an objective function to achieve a good tradeoff between model accuracy and complexity via a least-squares support vector machine. The physiological features from different measurement channels (electrodes) and frequency bands were compared in terms of multiclass MWL classification performance. The classification results showed that the locality projection preservation technique can maintain sufficiently high MWL classification accuracy (with the highest five-class correct classification rate of 88%) with a significantly reduced computational complexity. The comparative results of classification performance also demonstrated the superiority of the proposed dynamic model to a widely-used static model.
Landslide-induced impulse waves in alpine valleys are a significant risk to large-scale dam and reservoir engineering projects in the surrounding area. In this study, a 1 : 200-scale physical model ...of landslide-induced impulse waves in a V-shaped river channel was established, and 18 groups of tests were conducted to evaluate the influence of different parameters, such as the volume and shape of the landslide body, water entry velocity, and water depth of the reservoir. Based on the test results, a dimensionless formula was established for the first wave height of impulse waves caused by a deep-water landslide in a V-shaped channel. An energy conversion law was determined for the impact of landslide-induced impulse waves on the reservoir bank. Finally, a distribution law was obtained for the initial maximum pressure caused by landslide-induced impulse waves along the water depth on the opposite bank. The theoretical predictions of the dimensionless formula showed good agreement with the experimental results, and the energy conversion rate of the landslide-induced impulse waves initially increased and then decreased with an increasing Froude number. The maximum dynamic water pressure showed a triangular distribution with increasing water depth below the surface of the still water body. The impact pressure of the impulse waves on the slope on the opposite bank increased with the water entry velocity. This study provides a scientific basis for the risk prevention and control of landslide-induced impulse waves in river channels feeding into reservoirs.
PDF
