Mathematics and Simulation of Brain-Inspired Computing: From Dynamic Models to Visual Intelligence
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".
Deadline for manuscript submissions: 1 April 2026 | Viewed by 466
Special Issue Editors
Interests: brain-like neural networks; memristors; image processing; pattern recognition; nonlinear systems
Interests: intelligent traffic flow prediction; vehicle path planning and prediction technology; trajectory data representation learning; social network representation learning and mining; entity recognition
Interests: computational intelligence; memristive system theory and application; chaos theory and application; impulse control theory and application; hybrid system; iterative learning control of time-delay systems
Special Issue Information
Dear Colleagues,
This Special Issue explores mathematical frameworks driving brain-inspired computing systems, focusing on dynamic computational models and their applications in visual intelligence. We seek contributions that rigorously integrate the following:
- Mathematical modeling of neurobiological processes (e.g., coupled differential equations for spiking neurons, fractional calculus for synaptic plasticity).
- Learning algorithms with provable properties (e.g., convergence analysis of memristor-augmented backpropagation, Lyapunov stability in neuromorphic training).
- Optimization algorithms for energy-efficient neuromorphic architectures (e.g., stochastic gradient descent with biological constraints, gene algorithms based on memristor).
- Computational analysis of brain-inspired systems (e.g., complexity theory for event-based vision, entropy-driven information extraction).
- Statistical estimation in brain-like neural network vision tasks (e.g., Bayesian inference for brain-like neural network object detection, topological analysis of feature maps).
- Machine learning theory (e.g., convergence proofs for spiking neural networks, manifold learning for neuromorphic data).
Submissions must integrate formal mathematical analysis (theorems, proofs, or quantitative benchmarks) with computer vision applications, validated through simulations or hardware experiments.
Dr. Ling Chen
Dr. Huaijie Zhu
Prof. Dr. Chuandong Li
Guest Editors
Manuscript Submission Information
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Keywords
- memristor networks
- nonlinear dynamics
- neuromorphic computing
- mathematical modeling
- optimization theory
- object recognition
- stochastic learning
- energy-efficient algorithms
- hardware–software co-design
- computer vision
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