Field Of Neural Networks Research Articles

Tutorial: Artificial neural networks to analyze single-case experimental designs.

Since the start of the 21st century, few advances have had as far-reaching impact in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and psychology. In research methods, one promising area for the adoption of artificial neural networks involves the analysis of single-case experimental designs. Given that these types of networks are not generally part of training in the psychological sciences, the purpose of our article is to provide a step-by-step introduction to using artificial neural networks to analyze single-case designs. To this end, we trained a new model using data from a Monte Carlo simulation to analyze multiple baseline graphs and compared its outcomes with traditional methods of analysis. In addition to showing that artificial neural networks may produce less error than other methods, this tutorial provides information to facilitate the replication and extension of this line of work to other designs and datasets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The preview control of a corticothalamic model with disturbance

<abstract><p>Based on a neural field network model with impulsive and random disturbances, a preview control method that makes full use of known future information is proposed to reduce the static error of the target signal and the transient oscillatory behavior of the controlled system when it receives random disturbance inputs. The preview controller for epileptic seizures is constructed, and the feasibility and effectiveness of clinical single-target and multi-target stimulation in epilepsy regulation are explored from a computational perspective. In addition, a performance index function is proposed to evaluate the energy consumption of controller with and without preview under different input (target) strategies. Suggestions for different strategies are given in terms of the individualized disease environment of patients. From the perspective of seizure control effectiveness and performance consumption, the results show that the preview controller has a greater advantage. The theory of preview control is applied to the control of epileptic seizures for the first time, and the conclusions of the multifaceted study provide some references for clinical trials and controller applications.</p></abstract>

THE USE OF ARTIFICIAL NEURAL NETWORKS ON RUSSIAN RAILWAYS TO CONTROL CURRENT COLLECTORS OF TRAINS

Background. This article presents the results of the analysis of the mechanism of artificial neural networks on Russian railways for the control of current collectors of trains. A critical assessment of the situation was carried out and a forecast of the further development of neural network technologies in railway transport was given.
 Materials and/or research methods. Methods of mathematical modeling, system analysis, data analysis, comparison, systems theory, as well as architecture and mathematical model of neural network and information standards were used.
 Results. A model of the information system for monitoring the characteristics of current collectors has been developed, the algorithm of its operation and the functionality of the software package of the model of the system for monitoring the characteristics of current collectors of rolling stock have been described. The algorithm of neural network operation for determining the characteristics of electric rolling stock current collectors is presented. The analysis of the prospects for the use of artificial neural networks in the field of railway transport has been carried out, and a simulation model of an information system for monitoring the characteristics of current collectors of electric rolling stock of railway transport has been created.
 Conclusion. The article examines the positive experience of JSC “Russian Railways” on the introduction of neural network technologies in technological processes and further prospects for the use of neural network technologies on Russian railways. The necessity of intensifying the development and implementation of neurotechnologies to solve a wide range of tasks, including tasks to optimize the organization of maintenance and repair of rolling stock, is noted. The effectiveness and relevance of the use of artificial neural networks in various fields of activity, especially on Russian railways, is shown, and a model of an information system in the field of monitoring the characteristics of current collectors is proposed.

A Model of Motion Processing in the Visual Cortex Using Neural Field With Asymmetric Hebbian Learning

Neurons in the dorsal pathway of the visual cortex are thought to be involved in motion processing. The first site of motion processing is the primary visual cortex (V1), encoding the direction of motion in local receptive fields, with higher order motion processing happening in the middle temporal area (MT). Complex motion properties like optic flow are processed in higher cortical areas of the Medial Superior Temporal area (MST). In this study, a hierarchical neural field network model of motion processing is presented. The model architecture has an input layer followed by either one or cascade of two neural fields (NF): the first of these, NF1, represents V1, while the second, NF2, represents MT. A special feature of the model is that lateral connections used in the neural fields are trained by asymmetric Hebbian learning, imparting to the neural field the ability to process sequential information in motion stimuli. The model was trained using various traditional moving patterns such as bars, squares, gratings, plaids, and random dot stimulus. In the case of bar stimuli, the model had only a single NF, the neurons of which developed a direction map of the moving bar stimuli. Training a network with two NFs on moving square and moving plaids stimuli, we show that, while the neurons in NF1 respond to the direction of the component (such as gratings and edges) motion, the neurons in NF2 (analogous to MT) responding to the direction of the pattern (plaids, square object) motion. In the third study, a network with 2 NFs was simulated using random dot stimuli (RDS) with translational motion, and show that the NF2 neurons can encode the direction of the concurrent dot motion (also called translational flow motion), independent of the dot configuration. This translational RDS flow motion is decoded by a simple perceptron network (a layer above NF2) with an accuracy of 100% on train set and 90% on the test set, thereby demonstrating that the proposed network can generalize to new dot configurations. Also, the response properties of the model on different input stimuli closely resembled many of the known features of the neurons found in electrophysiological studies.

Reconstruction of a scalar voltage-based neural field network from observed time series

We present a general method for the reconstruction of a network of nonlinearly coupled neural fields from observations. A prominent example of such a system is a dynamical random neural network model studied by Sompolinsky et al. (Phys. Rev. Lett., 61 (1988) 259). We develop a technique for inferring the properties of the system from the observations of the chaotic voltages. Only the structure of the model is assumed to be known, while the nonlinear gain functions of the interactions, the matrix of the coupling constants, and the time constants of the local dynamics are reconstructed from the time series.

Disinhibition-Induced Delayed Onset of Epileptic Spike-Wave Discharges in a Five Variable Model of Cortex and Thalamus.

Based on a modified neural field network model composed of cortex and thalamus, we here propose a computational framework to investigate the onset control of absence seizure, which is characterized by the spike-wave discharges. Firstly, we briefly demonstrate the existence of various transition types in Taylor's model by increasing the thalamic input. Furthermore, after the disinhibitory function is reasonably introduced into the Taylor's model, we can observe the occurrence of various transition states of firing patterns with different dominant frequencies as the thalamic input is varied under different disinhibitory effects onto the pyramidal neural population. Interestingly, it is found that the onset of spike-wave discharges can be delayed as the disinhibitory input is considered. More importantly, we explore bifurcation mechanism of firing transitions as some key parameters are changed. And also, we observe other dynamical states, such as simple oscillations and saturated discharges with different spatial scales, which are consistent with previous theoretical or experimental findings.

An analytical method for computing Hopf bifurcation curves in neural field networks with space-dependent delays

We give an analytical parametrization of the curves of purely imaginary eigenvalues in the delay-parameter plane of the linearized neural field network equations with space-dependent delays. In order to determine if the rightmost eigenvalue is purely imaginary, we have to compute a finite number of such curves; the number of curves is bounded by a constant for which we give an expression. The Hopf bifurcation curve lies on these curves.

Instar and outstar learning with memristive nanodevices

The instar and outstar synaptic models are among the oldest and most useful in the field ofneural networks. In this paper we show how to approximate the behavior of instar andoutstar synapses in neuromorphic electronic systems using memristive nanodevicesand spiking neurons. Memristive nanodevices are especially attractive for thisapplication since such devices are tiny, can be densely packed in crossbar-likestructures and possess the long time constants, or memory, needed by the synapticmodels.