Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCYB.2025.3597576DOI Listing

Publication Analysis

Top Keywords

neural network
20
cmac neural
16
network circuit
12
artificial fish
12
memristor-based cmac
8
circuit artificial
8
fish behavioral
8
behavioral decision
8
fuzzy emotion
8
neural
6

Similar Publications

This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences.

View Article and Find Full Text PDF

Emotional contagion is an important aspect of social interaction. Traditional theories suggest that it relies on mimicry of facial or emotional movements. To address the question of whether there is a distinction between emotional contagion and emotional mimicry, we conducted a meta-analysis using the ALE algorithm to identify brain regions activated by the two tasks.

View Article and Find Full Text PDF

Accelerating Transition State Search and Ligand Screening for Organometallic Catalysis with Reactive Machine Learning Potential.

J Chem Theory Comput

September 2025

State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.

Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations.

View Article and Find Full Text PDF

Predicting complex time series with deep echo state networks.

Chaos

September 2025

School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.

View Article and Find Full Text PDF

Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential.

View Article and Find Full Text PDF