Yuki Todo

Kanazawa University | Kindai · Faculty of Electrical and Computer Engineering

Some fundamental visual features have been found to be fully extracted before reaching the cerebral cortex. We focus on direction-selective ganglion cells (DSGCs), which exist at the terminal end of the retinal pathway, at the forefront of the visual system. By utilizing a layered pathway composed of various relevant cells in the early stage of the...

Orientation detection is an essential function of the visual system. In our previous works, we have proposed a new orientation detection mechanism based on local orientation-selective neurons. We assume that there are neurons solely responsible for orientation detection, with each neuron dedicated to detecting a specific local orientation. The glob...

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers fro...

Manta ray foraging optimization (MRFO) tends to get trapped in local optima as it relies on the direction provided by the previous individual and the best individual as guidance to search for the optimal solution. As enriching population diversity can effectively solve this problem, in this paper, we introduce a hierarchical structure and weighted...

Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond...

The visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, including the Hassenstein–Reichardt model (HRC model)...

Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Eac...

The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in the long term. At the same time, the dendritic neuron model (DNM) has been shown to be effective in prediction problems, and it accounts for the nonlinear information-processing capacity of synapses and dendrites. Furthermore, since the classical error back-p...

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC)....

As the most studied sensory system, the visual system plays an important role in our understanding of brain functions. Biological researchers have divided the nerve cells in the retina into dozens of visual channels carrying various characteristics based on visual features. Although orientation-selective cells have been identified in the retinas of...

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch–Pitts neuron unit. A widely recog...

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers fro...

For mammals, enormous amounts of visual information are processed by neurons of the visual nervous system. The research of the direction selectivity is of great significance and local direction-selective ganglion neurons have been discovered. However, research is still at the one dimensional level and concentrated on a single cell. It remains chall...

Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original...

In this paper, we propose a mechanism of orientation detection system based on edge-orientation selective neurons. We assume that there are neurons in the V1 that can generate response to object’s edge, and each neuron has the optimal response to specific orientation in a local receptive field. The global orientation is inferred from the aggregatio...

The visual system plays a vital role in the daily life of humans, as more than 90 percent of the external information received by the human brain throughout the day comes from the visual system. However, how the human brain processes the received visual information remains a mystery. The information received from the external through the visual sys...

How specific features of the environment are represented in the mammalian brain is an important unexplained mystery in neuroscience. Visual information is considered to be captured most preferentially by the brain. As one of the visual information elements, motion direction in the receptive field is thought to be collected already at the retinal di...

This paper discusses the visual mechanism of global orientation detection and the realization of a mechanism-based artificial visual system for two-dimensional orientation detection tasks. For interpretation and practicability, we introduce the visual mechanism into the design of a detection system. We first propose an orientation detection mechani...

The perception of motion direction is essential for the survival of visual animals. Despite various theoretical and biophysical investigations that have been conducted to elucidate directional selectivity at the neural level, the systemic mechanism of motion direction detection remains elusive. Here, we develop an artificial visual system (AVS) bas...

The Hubel–Wiesel (HW) model is a classical neurobiological model for explaining the orientation selectivity of cortical cells. However, the HW model still has not been fully proved physiologically, and there are few concise but efficient systems to quantify and simulate the HW model and can be used for object orientation detection applications. To...

Research on dendrites has been conducted for decades, providing valuable information for the development of dendritic computation. Creating an ideal neuron model is crucial for computer science and may also provide robust guidance for understanding our brain’s underlying mechanisms and principles. This paper aims to review the related studies regar...

The human visual system is one of the most important components of the nervous system, responsible for visual perception. The research on orientation detection, in which neurons of the visual cortex respond only to a line stimulus in a particular orientation, is an important driving force of computer vision and biological vision. However, the princ...

The visual system plays a vital role when the brain receives and processes information. Approximately ninety percent of the information received by the brain comes from the visual system, and motion detection is a crucial part of processing visual information. To further understand the generation of direction selectivity, we propose a novel apparen...

Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively bette...

As an important part of the nervous system, the human visual system can provide visual perception for humans. The research on it is of great significance to improve our understanding of biological vision and the human brain. Orientation detection, in which visual cortex neurons respond only to linear stimuli in specific orientations, is an importan...

Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e., how to select the most informative genes from thousands of genes, remains challenging. This problem is a specific feature selection problem with high-dimensional features and small sample s...

With the rapid development of the global economy, air pollution, which restricts sustainable development and threatens human health, has become an important focus of environmental governance worldwide. The modeling and reliable prediction of air quality remain substantial challenges because uncertainties residing in emissions data are unknown and t...

The algorithm wingsuit flying search (WFS) mimics the procedure of landing the vehicle. The outstanding feature of WFS is parameterless and of rapid convergence. However, WFS also has its shortcomings, sometimes it will inevitably be trapped into local optima, thereby yield inferior solutions owing to its relatively weak exploration ability. Spheri...

In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhan...

The scale-free network is an important type of complex network. The node degrees in a scale-free network follow the power-law distribution. In the skeleton of a scale-free network, there exists a few nodes which own huge neighborhood size and play an important role in information transmission of the entire network, while most of the network nodes h...

Dendritic neuron model (DNM), which is a single neuron model with a plastic structure, has been applied to resolve various complicated problems. However, its main learning algorithm, namely the back-propagation (BP) algorithm, suffers from several shortages, such as slow convergence rate, being easy to fall into local minimum and over-fitting probl...

A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal p...

Nature-inspired metaheuristic algorithms are often based on the first-order difference hypercube search style to search for optimum solutions. In contrast, the spherical evolution algorithm (SE) employs a spherical search style. SE is very effective; however, there is still room for improvement. In this study, we added a chaotic local search (CLS)...

The protein-ligand docking problem plays a crucial role in the drug discovery process and remains challenging in bioinformatics. A successful protein-ligand docking approach depends on two key factors: an efficient search strategy and an effective scoring function. In this study, we attempt to use an adaptive differential evolution (DE) algorithm a...

Previous studies have reported that directionally selective ganglion cells respond strongly in their preferred direction, but are only weakly excited by stimuli moving in the opposite null direction. Various studies have attempted to elucidate the mechanisms underlying direction selectivity with cellular basis. However, these studies have not eluci...

Recent neurological studies have shown the importance of dendrites in neural computation. In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification. This model consists of four layers: a synaptic layer, a dendritic layer, a membrane layer, and a soma body. After training,...

Wingsuit flying search (WFS) is a novel metaheuristic search algorithm that mimics the process of a flier to land on the earth’s surface within their range, i.e., a global minimum of the search space. The main advantage of this algorithm is parameter-free except for the population size and the maximal number of iterations. Spherical evolution (SE)...

In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical ev...

Air pollution in cities has a massive impact on human health, and an increase in fine particulate matter (PM2.5) concentrations is the main reason for air pollution. Due to the chaotic and intrinsic complexities of PM2.5 concentration time series, it is difficult to utilize traditional approaches to extract useful information from these data. There...

An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendritic structure for a particular task. The simplified structure of ALNM can be substituted by a logic c...

In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) trained by the backpropagation (BP) algorithm, the proposed EDNM is trained by a metaheuristic cuckoo...

The primary motivation of this paper is twofold: first, to employ a heuristic optimization algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual classifier for computer-aided diagnosis that can be easily implemented on a hardware system. Considering that the backpropagation (BP) algorithm is sensitive to the ini...

Despite the rapid development of computer techniques and the unremitting efforts of researchers, the protein structure prediction (PSP) problem remains challenging in computational biology and bioinformatics. In this study, we model the PSP problem as a multiobjective optimization problem and propose a free modeling approach called MODE-K to solve...

With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification pr...

Because of the intrinsic complexity and chaotic nature of wind speed time series, an appropriate model for accurately forecasting the moving tendency is required. In this paper, we propose an evolutionary dendritic neuron model (EDNM) to carry out wind speed forecasting. The model is trained via adaptive differential evolution with the linear popul...

Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective...