Maximum Number Of Neurons Research Articles

Asynchronous Numerical Spiking Neural Membrane Systems with Local Synchronization.

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems.

Sphenopalatine Ganglion Block ‘Miracle Block’- A Review Article

The sphenopalatine ganglion has maximum number of neurons in the calvarium that are not situated within the brain. In the sympathetic, parasympathetic and sensory nervous system it’s the largest and most superior ganglion. SPG block in conjunction with topical anaesthetic and radiofrequency ablation is currently advised for the treatment of trigeminal neuralgia, cluster headaches, migraines, and persistent idiopathic facial discomfort. The block of SPG is also known as “the miracle block”. The sphenopalatine ganglion block is a simple and safe procedure which can be used for eliminating acute or chronic pain and reduces the episodic recurrence of the pain.

Age- and sex-related dynamics of structural and functional motor behavior interactions in striatum neurons in rats

Aim. To study the age-related dynamics of structural and functional interactions of striatal neurons in the implementation of acts of motor behaviour in rats of both sexes.Materials and methods. The study was carried out on 36 Wistar rats of both sexes aged 2, 7 and 16 months (n = 6 per group). In animals of all groups, locomotor activity was determined using a Laboras device (Metris, the Netherlands) for15 minutes, after which the brain was sampled to determine the number and size of neurons in the striatum. The median and interquartile range of the index of motor activity and the number of neurons were determined, and to study the relationship between these indicators, a correlation and regression analysis was performed with the construction of linear and polynomial trends, and the coefficient of determination R2 was calculated.Results. The size of neurons did not change significantly with age in the rats of both sexes. The number of neurons differed statistically in the rats of different sexes in all age groups. In male rats, the maximum number of neurons was noted at the age of 7 months with a decrease to 16 months. In female rats, the maximum number of neurons was recorded at the age of 2 months with a further decrease to 7 and 16 months. According to the regression analysis, a linear strong relationship (R2 =0.80 for males, R2 = 0.79 for females) was established between the number of neurons in the striatum and motor activity in 2-month-old animals. At the age of 7 and 16 months the relationship is non-linear.Conclusion. The number of neurons in the striatum is subject to sex and age dynamics, while their size remains unchanged from 2 to 16 months. For animals of both sexes, a decrease in the role of the striatum in providing motor activity in the process of growing up was noted. This relationship reaches its maximum in 2-month-old rats and then decreases.

Common Carotid Artery Occlusion and Double-Nucleated Cellular Structures In The Rat Sensorimotor Cerebral Cortex

The aim of the study. To study the double-nucleated cellular structures of the brain sensorimotor cortex (SMC) of sexually mature white rats after a 40-minute occlusion of the common carotid arteries.Methods. Acute ischemia was simulated in white Wistar rats by 40-minute occlusion of the common carotid arteries (OCCA). We performed comparative morphometric evaluation of cyto-, dendro-, synapto-, and glioar-chitectonics of the neocortex in intact animals (n=5), and 1 (n=5), 3 (n=5), and 7 days (n=5) after OCCA. We used Nissl, hematoxylin and eosin staining, and immunohistochemical reactions for NSE, MAP-2, HSP-70, p38, caspase-3, GFAP, AIF1, and Ki-67. Numerical density of pyramidal neurons, oligodendrocytes (ODCs), mi-croglyocytes (MGCs), presence of dystrophic and necrobiotic neurons with one or more nucleoli, hetero- and dikaryons were assessed. Statistical hypotheses were tested using Statistica 8.0 software.Results. The percentage of dystrophic and necrobiotic neurons, nerve cells with two nuclei or two or more nucleoli, the total number (proliferation) and percentage of hypertrophic astrocytes, ODCs and MGCs increased significantly after OCCA. The total numerical density of SMC neurons decreased by 26.4% (P=0.001) in layer III and by 18.5% in layer V (Mann-Whitney U Test; P=0.01) after OCCA throughout the observation period. Pathological and compensatory changes were diffusely focal and more pronounced in layer III of the neocortex. The density of bi-nucleated heterokaryons and dikaryons remained unchanged on days 1 and 3 after OCCA vs control and was 3.5 (1.5-4.0)/mm2, and increased to 6.5 (5.0-8.5)/mm2 on day 7 (Mann-Whitney U Test; P=0.002). This increase occurred along with a higher density of ODCs and MGCs than in the control. The maximum number of neurons with two or more nucleoli was also noted in layer III and V during this period.Conclusion. After 40-minute OCCA in SMC, parallel to the dystrophic and necrobiotic changes of pyramidal neurons and activation of neuroglial cells, there was an increase in the formation of heterokaryons and neurons with amplified nucleolus. These changes were considered as a variant of neuronal response to ischemic damage.

Changes in the Cellular Composition of the Cerebral Cortex in Rats with Different Levels of Cognitive Functions Under Cerebral Hypoperfusion

The aim of study was to investigate characteristics of neurodystrophic changes in neurons and glia of the motor cortex in rats with different levels of cognitive functions under bilateral ligation of the common carotid arteries. Material and methods . The study included 136 Wistar rats. All animals were divided into two subgroups based on the results of the Morris water maze test: with high and low level of ability to spatial learning. Animals of the experimental group were removed from the experiment on the 1st, 6th, 14th, 21st, 35th, 60th and 90th days after bilateral ligation of both carotid arteries. Histological sections of the motor cortex were studied using Nissl and hematoxylin-eosin staining. Results . In 1, 6 and 8 days after ischemia, the number of neurons with irreversible changes and dead cells reached their maximum for the entire time of observation. On the 8th day of study, compact groups of glial cells appeared near the vessels. Heterochrony was noted: in animals with high level of cognitive ability, an increase in the number of neurons with irreversible changes followed the peak of cell death; in animals with low level, on the contrary, the maximum number of dead cells was observed on the 6th day, and the maximum number of neurons with irreversible changes – on the 1st. On the 14th, 21st, and 28th days there was observed a gradual stabilization of indicators characterizing damage to the cerebral cortex. Average values of the perikarya area and neurons nuclei increased, the nuclear-cytoplasmic ratio decreased, and intracellular swelling was noted. After 35 days, areas of the cortex depleted in bodies of neurons and glia appeared; the number of neurons with irreversible changes increased, to a greater extent, in animals with high level of cognitive abilities. Typical trends of the first month of study are as follows: a decrease in the nuclear-cytoplasmic ratio and a number of neurons without irreversible changes, and an increase in the neuroglial index continued to progress on the 60th and 90th days of study. The apical dendrites of the pyramidal neurons obtained a corkscrew course. Compact groups of glial cells disappeared. Conclusion . During the first week of cerebral hypoperfusion, irreversible changes in neurons predominated, in the second and third weeks, the morphological criteria of their functional activity decreased. In the fourth and fifth weeks incomplete adaptation developed in the form of an increased number of neurons near the vessels of the hemocirculatory bed and an increased number of satellite gliocytes by immersing them in the cytoplasm of neurons. In 2-3 months after cerebral hypoperfusion, signs of acute hypoxia reappeared. Animals with high level of cognitive ability were characterized by large damage to the structures of the neuroglial ensemble.

Maximum Individual Complexity is Indefinitely Scalable in Geb.

Geb was the first artificial life system to be classified as exhibiting open-ended evolutionary dynamics according to Bedau and Packard's evolutionary activity measures and is the only one to have been classified as such according to the enhanced version of that classification scheme. Its evolution is driven by biotic selection, that is (approximately), by natural selection rather than artificial selection. Whether or not Geb can generate an indefinite increase in maximum individual complexity is evaluated here by scaling two parameters: world length (which bounds population size) and the maximum number of neurons per individual. Maximum individual complexity is found to be asymptotically bounded when scaling either parameter alone. However, maximum individual complexity is found to be indefinitely scalable, to the extent evaluated so far (with run times in years and billions of reproductions per run), when scaling both world length and the maximum number of neurons per individual together. Further, maximum individual complexity is shown to scale logarithmically with (the lower of) maximum population size and maximum number of neurons per individual. This raises interesting questions and lines of thought about the feasibility of achieving complex results within open-ended evolutionary systems and how to improve on this order of complexity growth.

Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.

The influence of computational traits on the natural selection of the nervous system

This article addresses why the neural network model has been selected by nature against other computational models to generate behavior in complex multicellular clades. This question, which has not yet been addressed in research, should not be ignored because understanding this issue is necessary to have a complete picture of the evolutionary process of the nervous system. The starting point to discuss the issue is a proposal made 30 years ago: the free-moving hypothesis. This proposal establishes prediction as the main function of the brain and that all multicellular organisms that move require a brain in order to make predictions. This article contains a review contrasting this hypothesis with the discoveries made in the last 30 years within different biological kingdoms. Although none of these discoveries contradict the free-moving hypothesis, it still does not answer the main question. Alternative hypotheses about the origin of the nervous system are discussed in this paper, but they also are not able to answer the question. Six hypotheses are proposed as possible answers, and each of them is discussed by comparing neural processing systems with three other alternative processing systems. The result is that the neural processing system is selected against other kinds of processing systems because it has computational robustness to damage, allowing its function of prediction to be more durable. While this result, called the first neural processing principle, answers the initial question and permits a finished proof of the free-moving hypothesis, it gives rise to the question of how computationally robust a system must be to be selected by nature. This paper claims that the system selected must be computationally robust enough to have enough offspring to allow variation. This answer, named the second neural principle, determines the minimum amount of neurons that a neural processing system must have, but not the maximum. To address this issue, the third and fourth neural processing principles are stated, which determine that the maximum number of neurons is limited by energetic restrictions and body size, respectively. The results presented in this paper show that computational robustness is an important parameter to understand the evolution of nervous system.

Implementing radial basis function neural networks for prediction of saturation pressure of crude oils

ABSTRACTThis study highlights the application of radial basis function (RBF) neural networks for perdition of saturation pressure of gas condensates and oils. The experimental data were collected from literature and cover a vast geographic distribution. Genetic algorithm (GA) was used to determine the optimum values of spread and maximum number of neurons for developed RBF model. The input parameters of the model were the C1 through C7+ fraction of gas condensates, crude oil, nonhydrocarbon fraction of crude oil (nitrogen [N2], carbon dioxide [CO2], and hydrogen sulfide [H2S]), specific gravity and molecular weight of C7+ (SGC7+, MWC7+) and temperature. The output of model was the saturation pressure of crude oil. Different statistical and graphical methods were utilized to examine the accuracy of implemented GA-RBF model. Results of modeling study showed that the GA-RBF model is effective and robust in reproducing the whole data points with an acceptable accuracy.

GPGPU implementation of a synaptically optimized, anatomically accurate spiking network simulator

Simulation of biological spiking networks is becoming more relevant in understanding neuronal processes. An increasing proportion of these simulations focuses on large scale modeling efforts. Unfortunately the size of large networks is often limited by both computational power and memory. Computational power constrains both the maximum number of differential equations and the maximum number of spikes that can be processed per unit time. Memory size limits the maximum number of neurons and synapses that can be simulated. To solve for the computational bottleneck, a neuronal simulator is implemented on a CUDA-based General Purpose Graphic Processing Unit (GPGPU). CUDA provides a C-like environment to harness the computational power of specialized video cards from NVIDIA (these cards provide a computational peak power on single precision floats of 1TFLOPS, at least an order of magnitude higher than the fastest CPU). To solve for the memory bottleneck, a just-in-time synapse storing algorithm is implemented requiring only 4 bytes per synapse. Only the synaptic weight is stored, while both post-synaptic contact and delay are recomputed at run-time. This allows a resource shift from memory to computation which fits with the peculiar GPGPU architecture, where an abundance of compute nodes access the memory via a bandwidth-limited bus. Neurons are represented by a single compartment whose activity is modeled by the Izhikevich formalism. Excitatory synapses are plastic and follow both a spike-timing-dependent plasticity rule and a short term potentiation/depression rule. We are able to simulate networks with up to a million neurons and up to 100 million synapses on a single GPGPU card. Networks of this size cannot be simulated on desktop computers. For smaller networks the speedup obtained is at least of an order of magnitude compared to traditional CPU platform. As an example of possible use of the present work we present preliminary results on the simulation of early stage of the visual pathway. In the retina, Retinal Ganglion Cells (RGC) project to the Lateral Geniculate Nucleus (LGN). LGN projects to the cortical area V1. V1 projects back to LGN. The network is represented by 100,000 neurons, 10 million synapses, and 32 different morphological classes with >350 topological projections. Input to the network is provided by current injection in the RGC layer. The RGC layer models midget and parasol ganglion cells (representing 80% of the RGC in primates). Each ganglion type is then subdivided into on- and off-center cells for a total of 4 different types of RGC. Training is performed via natural stimuli while testing is done with vertical and horizontal bars. The network average spiking frequency is within biological limits. Testing performed with both vertical and horizontal bars shows each pattern propagating along the network’s anatomical projections. At each stage the pattern is progressively elaborated and modified. In conclusion we present a novel simulator that is fast, synaptically optimized and anatomically accurate. At an additional cost to an available desktop PC of few hundred dollars we think the GPGPU is an ideal platform to simulate large spiking networks.

Neuronal overload in the developing anuran lateral motor column in response to limb removal and thyroid hormone.

The lateral motor column (LMC) in the anuran spinal cord normally undergoes a dramatic reduction in motor neuron number during development. At least two factors influence this process: the limb target which is required for the progression of cell loss, and thyroid hormone, a requisite for metamorphosis. This study has examined the relative and combined effects of limb amputation and exogenous thyroxine, initiated at the onset of normal rapid cell loss in Rana pipiens tadpoles, in regulating neuron number in the lumbosacral LMC. Thyroxine treatment or unilateral limb amputation temporarily resulted in significantly more LMC neurons than in untreated controls. Extraordinary numbers of motor neurons persisted through metamorphic climax when both treatments were combined. Population sizes frequently exceeded the maximum number of neurons observed prior to the onset of natural cell loss. Moreover, thyroxine-treated tadpoles contained increased numbers of mitotic figures in the ventricular zone of the spinal cord and significantly more newly generated cells in the LMC, as revealed by 3H-thymidine autoradiography. These findings suggest that thyroxine-potentiated mitogenesis promotes greater numbers of new motor neurons to the LMC while, simultaneously, target removal delays the loss of extant cells. It is proposed that this interaction effectively maintains an immature state in the LMC so that neuronal "decisions" for survival and the consequent loss of target-deprived neurons are postponed far longer than previously reported.