Model Of Biological Neural Networks Research Articles

Deep Learning-Driven Optimization Strategies for Teaching Decisions in Smart Classrooms

With the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans.

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices. In this paper, a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation. The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs, respectively, and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface. The dynamic process of material vibration screening was simulated using discrete element method (DEM). By comparing the similarity between the material distribution established using biological neural network (BNN) and that obtained using DEM simulation, the optimum coefficients of BNN model under a certain screening parameter were determined, that is, one relationship between the BNN model coefficients and the screening operation parameters was established. Different screening parameters were randomly selected, and the corresponding relationships were established as a database. Then, with straw/grain ratio, aperture diameter, inclination angle, vibration strength in normal and tangential directions as inputs, five independent adaptive neuro-fuzzy inference systems (ANFIS) were established to predict the optimum BNN model coefficients, respectively. The training results indicated that ANFIS models had good stability and accuracy. The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

How children learn to understand language meanings: a neural model of adult-child multimodal interactions in real-time.

This article describes a biological neural network model that can be used to explain how children learn to understand language meanings about the perceptual and affective events that they consciously experience. This kind of learning often occurs when a child interacts with an adult teacher to learn language meanings about events that they experience together. Multiple types of self-organizing brain processes are involved in learning language meanings, including processes that control conscious visual perception, joint attention, object learning and conscious recognition, cognitive working memory, cognitive planning, emotion, cognitive-emotional interactions, volition, and goal-oriented actions. The article shows how all of these brain processes interact to enable the learning of language meanings to occur. The article also contrasts these human capabilities with AI models such as ChatGPT. The current model is called the ChatSOME model, where SOME abbreviates Self-Organizing MEaning.

Time-Reduced Model for Multilayer Spiking Neural Networks

Spiking neural networks (SNNs) is a type of biological neural network model, which is more biologically plausible and computationally powerful than traditional artificial neural networks (ANNs). SNNs can achieve the same goals as ANNs, and can build a large-scale network structure (i.e. deep spiking neural network) to accomplish complex tasks. However, training deep spiking neural network is difficult due to the non-differentiable nature of spike events, and it requires much computation time during the training period. In this paper, a time-reduced model adopting two methods is presented for reducing the computation time of a deep spiking neural network (i.e. approximating the spike response function by the piecewise linear method, and choosing the suitable number of sub-synapses). The experimental results show that the methods of piecewise linear approximation and choosing the suitable number of sub-synapses is effective. This method can not only reduce the training time but also simplify the network structure. With the piecewise linear approximation method, the half of computation time of the original model can be reduced by at least. With the rule of choosing the number of sub-synapses, the computation time of less than one-tenth of the original model can be reduced for XOR and Iris tasks.

Persistence in a large network of sparsely interacting neurons.

This article presents a biological neural network model driven by inhomogeneous Poisson processes accounting for the intrinsic randomness of synapses. The main novelty is the introduction of sparse interactions: each firing neuron triggers an instantaneous increase in electric potential to a fixed number of randomly chosen neurons. We prove that, as the number of neurons approaches infinity, the finite network converges to a nonlinear mean-field process characterised by a jump-type stochastic differential equation. We show that this process displays a phase transition: the activity of a typical neuron in the infinite network either rapidly dies out, or persists forever, depending on the global parameters describing the intensity of interconnection. This provides a way to understand the emergence of persistent activity triggered by weak input signals in large neural networks.

Retracted: Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Retracted: Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.

Biological neural network model based on a non-linear stochastic system

As a contribution to recent activities in the field of modelling electro-biological phenomena, we would like to present an investigation on the electrical properties of a network of neuron and propose a simplified model for brain activity, based on a biological neural network represented by a non-linear system with stochastic components. The network’s fundamental element is represented by Izhikevich model, and including several types of responses, grouped into excitatory and inhibitory response. The network’s electrical activity is compared to a biological signal obtained from an electroencephalogram recording, and it is noted that the correlation between the biological and the simulated signal increases with the number of neurons in the network.

A hybrid biological neural network model for solving problems in cognitive planning

A variety of behaviors, like spatial navigation or bodily motion, can be formulated as graph traversal problems through cognitive maps. We present a neural network model which can solve such tasks and is compatible with a broad range of empirical findings about the mammalian neocortex and hippocampus. The neurons and synaptic connections in the model represent structures that can result from self-organization into a cognitive map via Hebbian learning, i.e. into a graph in which each neuron represents a point of some abstract task-relevant manifold and the recurrent connections encode a distance metric on the manifold. Graph traversal problems are solved by wave-like activation patterns which travel through the recurrent network and guide a localized peak of activity onto a path from some starting position to a target state.

Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

A novel neural network approach to modeling particles distribution on vibrating screen

Screening is the most important operation for the separation of solid particles. The distribution of particles on the screen surface is an important factor affecting the screening performance. In this paper, a biological neural network (BNN) approach is proposed for modeling the distribution of particles on a vibrating screen surface. The dynamics of each neuron is characterized by the shunting equation, and the neural connection weights are properly defined according to the movement of particles under different vibration and structural parameters. Neural activities can propagate from the particles input neurons to the whole network through adjacent neural connections. When the iterative calculation is stable, the generated neural activity landscape is used to describe the particles distribution state.Discrete element method (DEM) simulations are carried out to obtain the particles screening processes and the corresponding distribution states. Then, the particles distribution models established using BNN are compared with that obtained by DEM simulations, and the similarities between them are improved by optimizing the BNN model coefficients. Similarity analysis results under different screening conditions show that the general correlation coefficient is higher than 0.9, which verifies the feasibility of the proposed BNN approach. Compared with the traditional kinetic and probability models, the BNN approach has obvious advantages in solving the modeling problem when the particles are fed to screen with multiple areas and non-uniform rate.

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static synapses to the case of dynamic synapses exhibiting short-term plasticity (STP). The extended theory offers an approximate mean-field dynamics for the synaptic input currents arising from populations of spiking neurons and synapses undergoing Tsodyks–Markram STP. The approximate mean-field dynamics accounts for both finite number of synapses and correlation between the two synaptic variables of the model (utilization and available resources) and its numerical implementation is simple. Comparisons with Monte Carlo simulations of the microscopic model show that in both feedforward and recurrent networks, the mesoscopic mean-field model accurately reproduces the first- and second-order statistics of the total synaptic input into a postsynaptic neuron and accounts for stochastic switches between Up and Down states and for population spikes. The extended mesoscopic population theory of spiking neural networks with STP may be useful for a systematic reduction of detailed biophysical models of cortical microcircuits to numerically efficient and mathematically tractable mean-field models.

Artificial intelligence based neurofeedback

A new approach to the design of neurofeedback systems based on using Artificial Intelligence (AI) tools is proposed. The concept of control models of biological neural networks, and the set-up including equipment and software tools developed in IPME RAS in order to implement the proposed concept is described. as well as the AI methods and programs proposed for use.

Regularization theory in the study of generalization ability of a biological neural network model

This paper focuses on the generalization ability of a dendritic neuron model (a model of a simple neural network). The considered model is an extension of the Hodgkin-Huxley model. The Markov kinetic schemes have been used in the mathematical description of the model, while the Lagrange multipliers method has been applied to train the model. The generalization ability of the model is studied using a method known from the regularization theory, in which a regularizer is added to the neural network error function. The regularizers in the form of the sum of squared weights of the model (the penalty function), a linear differential operator related to the input-output mapping (the Tikhonov functional), and the square norm of the network curvature are applied in the study. The influence of the regularizers on the training process and its results are illustrated with the problem of noise reduction in images of electronic components. Several metrics are used to compare results obtained for different regularizers.

Generative models for network neuroscience: prospects and promise.

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience.

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Neurorobotics: an experimental science of embodiment

At the interface of neuroscience and robotics, neurorobotics is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms (e.g. connectionist networks, artificial spiking neural nets), computational models of biological neural networks (e.g. large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). Such neural systems can be embodied in machines with mechanic, pneumatic, electromagnetic or any other forms of physical or virtual actuation. This includes robots, prosthetic, wearable systems, virtual reality environments but at also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures. Neurorobotics is at the confluence of different research trends that emancipated from their original disciplines in the 1980s and 1990s: artificial neural network and computational neuroscience models that are neural systems but not embodied, embodied developmental robotic systems that are embodied and autonomous but not brain-like, and research studies in hybrid biological – artificial systems that are embodied neural systems but with very little autonomy. In each of these fields great progresses have been recently made. Most advanced computing machineries permit now to simulate huge realistic neural network. Promising experiments in epigenetic robotics move from traditional learning of isolated tasks to open-ended developmental trajectories. Pioneering research in hybrid systems permit to investigate symbiotic biological-artificial systems that were previously only in the realm of science fiction. Neurorobotics is the natural meeting point of these converging trends. The grand challenge of neurorobotics is to build a well-founded experimental science of embodiment. Considered in isolation, neural systems tend to be intrinsically generic, plastic and versatile. However, once embodied and coupled with a given internal or external environment, they become specific and adapted. Neurorobotics offers a novel methodology to understand this process by considering both neural systems and embodiment as experimental variables. Since the 1950s, robots were essentially seen as fixed bodies on which different programs could be plugged in, like the software and hardware parts of a computer. This dualism has led to a prejudicial divergence between artificial intelligence researchers building intelligent programs and roboticists building sophisticated bodies. In the last 1980s, a handful of researchers tried to escape from what appeared to be a technological dead-end and pushed forward a reunited view of intelligence, under the name of embodied artificial intelligence or new A.I. They argued that physical bodies and control systems should be intrinsically linked, like two sides of the same coin. The return to the design of complete agents undoubtedly led to some successes, notably for locomotion, sensorimotor learning and navigation in unknown complex environments. However, if such kind of holistic approaches proved to be efficient for designing complex adapted behavior, they were not sufficient to articulate a clear view of developmental processes. For instance, in just a few months, children learn to crawl, stand, walk, jump, hop, run, etc. As they learn these new skills in a continuous incremental manner, their sensorimotor space changes permitting them to investigate novel domains of exploration. This is even clearer with the use of tools or the acquisition of communication skills. This has lead researchers in developmental and epigenetic robotics to present models in which an agent is essentially constituted on the one hand, of a kernel, a set of stable processes that drive developmental dynamics and, on the other hand, of variable body envelopes that change over time. This novel view reverses the classic notion of a fixed body on which different software can be applied to consider a fixed software that can be applied to different kinds of embodiment, potentially changing over time. Experiments in neurorobotics permit to progress in understanding how the interplay between neural learning dynamics, physical embodiment and environment factors shape developmental trajectories in specific ways, leading, in some circumstances, to open-ended acquisition of new competences but also, in other cases, to abnormal trajectories. In addition, neurorobotics is expected to provide the technological basis for self-developing devices of a new kind, capable of acquiring new know-how in a continuous and open-ended manner. Understanding how to design such devices and the impact they can have in our daily activities is ofa major importance for the coming years.

Energy Function and Energy Evolution on Neuronal Populations

Based on the principle of energy coding, an energy function of a variety of electric potentials of a neural population in cerebral cortex is formulated. The energy function is used to describe the energy evolution of the neuronal population with time and the coupled relationship between neurons at the subthreshold and the suprathreshold states. The Hamiltonian motion equation with the membrane potential is obtained from the neuroelectrophysiological data contaminated by Gaussian white noise. The results of this research show that the mean membrane potential is the exact solution of the motion equation of the membrane potential developed in a previously published paper. It also shows that the Hamiltonian energy function derived in this brief is not only correct but also effective. Particularly, based on the principle of energy coding, an interesting finding is that in some subsets of neurons, firing action potentials at the suprathreshold and some others simultaneously perform activities at the subthreshold level in neural ensembles. Notably, this kind of coupling has not been found in other models of biological neural networks.