scholarly journals The Role of Geterogeneity in Synchronization of Spiking Neural Networks

Author(s):  
I.E. Mysin ◽  
A.V. Chizhov

The diversity and heterogeneity of neurons and synapses is an important factor in the functioning of the brain. In our work, we investigated the role of heterogeneity of neural populations in the occurrence of synchronous modes in a network connected by exciting links in the presence of an external exciting input. Using Monte-Carlo modeling and the semi-analytical modeling the distribution of the refractory density of neuron integrators and Hodgkin – Huxley neurons, we showed that there is a range of parameters for the stimulating current and the strength of connections in the population where the effects of neuronal heterogeneity on the threshold or on the stimulating current are opposite. For large values ​​of synaptic weights and subthreshold values ​​of the exciting current, heterogeneity contributes to the emergence of a synchronous mode in the neural network, while at the same time reducing the coupling strength and increasing the exciting current. The heterogeneity reduces the tendency of the neural network to synchronize. The results obtained make it possible to reconcile the known data on the effects of heterogeneity in the regulation of the synchronous regimes arising in the neural ensembles of the brain.

2005 ◽  
Vol 17 (8) ◽  
pp. 1739-1775 ◽  
Author(s):  
Osamu Hoshino

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief γ-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.


2019 ◽  
pp. S453-S458
Author(s):  
R. Krupička ◽  
S. Mareček ◽  
C. Malá ◽  
M. Lang ◽  
O. Klempíř ◽  
...  

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson’s Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.


2020 ◽  
Author(s):  
Gang Liu

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.


Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.


2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Chen ◽  
Chunyu Li ◽  
Yafei Shi ◽  
Yujun Zhang ◽  
Dujia Jin ◽  
...  

Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.


We know that the brain is the seat of the mind. Constructing the reductive model of the conscious mind requires an indication of the laws according to which the mind emerges from biophysical processes occurring in natural brains. Because in Part I, the authors presented the theoretical model referring to the ideal structures of the imagined neural network, we now have easier task, because we need to indicate in the brains of the living beings those processes that functionally correspond to our postulates. Such suitability is not guaranteed by known processes occurring in specialized parts of the brain. The role of the primary sensory areas is a detailed analysis of sensory stimuli with specific modality. They result in analysis of the meaning of all useful stimuli and their interpretation used in various parts of the cortex. The high specialization of individual cortex areas is striking and are the result of evolutionary development of the brain. New brain structures, such as the new cortex, were added on the outskirts of existing structures, improving their performance in the ever more demanding environments, where other intelligent beings ravened. But even as we know the brain organization, we struggle to understand how it works. How neurons that make the brain work together to create the conscious mind. To discover functionally effective processes in the brain, one need to reach for the biophysical properties of the astrocyt-neural network. In this chapter, the authors suggest that some concepts of neuro-electro-dynamics and the phenomena of neuro- and synapto-genesis as well as synaptic couplings may explain the processes of categorization, generalization and association leading to the formation of extensive, semihierarchical brain structures constituting neural representations of perceptions, objects and phenomena. Natural brains meet the embodiment condition. They are products of evolution, so they have intentionality, their own goals and needs. So they can naturally show emotions, drives and instincts that motivate to act. This determines the nature of constructed mental representations. They are the subject of psychological research, which shows the motivation of pain and pleasure in the field of intelligent activities, as well as the motivation of curiosity and the need for understanding in the domain of propositional and phenomenal consciousness. They describe the way pain is felt in organisms as basic quale. The role of other qualia for “how-it-is-like to feel something” and their subjective character was explained, as well as their interspecies specificity was characterized. In this chapter, the authors present an elementary biophysical phenomenon, that is a flash of consciousness. This phenomenon is synaptic coupling formed in the course of learning. They justify that the stream of such phenomena is the foundation of consciousness. They also point out that the astrocytic-neural network meets all the conditions required to generate conscious sensations.


2015 ◽  
Vol 114 (5) ◽  
pp. 2555-2557
Author(s):  
Clarisa A. Martinez ◽  
Chunji Wang

Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues ( Nature 512: 423–426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.


2019 ◽  
Vol 24 (2) ◽  
pp. 46
Author(s):  
B. R. R. Boaretto ◽  
R. C. Budzinski ◽  
T. L. Prado ◽  
S. R. Lopes

The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson’s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary research field where there are still questions to be answered. Here, through mathematical modeling and numerical approach, we simulated a neural network composed of 5000 bursting neurons in a scale-free connection scheme where non-trivial synchronization phenomenon is observed. We proposed two different protocols to the suppression of phase synchronization, which is related to deep brain stimulation and delayed feedback control. Through an optimization process, it is possible to suppression the abnormal synchronization in the neural network.


1992 ◽  
Vol 40 (4) ◽  
pp. 1139-1159 ◽  
Author(s):  
Stanley R. Palombo

Converging developments in the cognitive- and neurosciences have brought Freud's hope of a bridge between psychoanalysis and psychophysiology nearer to hand. This paper concerns the relation between dream construction and memory in terms of these new developments. The neural network architecture of memory structures in the brain is described and illustrated with simple examples. We see how a network is connected and how connection weights vary with experience. The distributed representation stored by the network and its crucial properties for mental functioning are discussed. These concepts are used to explain how particular memories of past events are selected for inclusion in the dream. The properties of the neural network suggest that images of distinct past events are conflated at times during the selection process. The appearance of these conflated images may complicate the matching of day residues with representations of past events in the dream itself. Some likely implications for psychoanalytic theory are explored.


1997 ◽  
Vol 07 (08) ◽  
pp. 1887-1895 ◽  
Author(s):  
Vladimir E. Bondarenko

Self-organization processes in an analog asymmetric neural network with time delay and under an external sinusoidal force are considered. Quantitative characteristics of the neuron outputs (spectrum, correlation dimension, largest Lyapunov exponent, Shannon entropy, normalized and renormalized Shannon entropies) are studied in dependence on the frequency and amplitude of the external force. It is shown that the external sinusoidal force allows the control of the degree of chaos and produces transitions "order–chaos", "chaos–order" and "chaos–chaos" with different quantitative characteristics. Information processing both by the individual neurons and by the neural network as a system is discussed. Chaotic neural network under an external force is considered as a qualitative model of the infra-frequencies action on the brain.


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