Abstract
This paper introduces a novel class of chaotic attractors by lever- aging different activation functions within neurons possessing multiple dendrites. We propose a comprehensive framework where the activation functions in neurons are varied, allowing for diverse behaviors such as amplification, fluctuation, and folding of scrolls within the resulting chaotic attractors. By employing wavelet functions and other model-specific activation functions, we demonstrate the capability to modify scroll characteristics, including size and direction. Furthermore, a model featuring neurons with varied activation functions is elaborated upon, showcasing the versatility of this approach. Through numerical simulations, we validate the efficacy of our proposed theoretical frame-work, offering new insights into the behavior of chaotic attractors. The results highlight the potential for generating higher-dimensional hyperchaotic attractors with enhanced complexity and applicability in various domains. Numerical simulations are given to show the effectiveness of the proposed theoretical results using C + + .