Neuroscience-Inspired Artificial Intelligence: Exploring Brain-Based Principles for More Adaptive Intelligent Systems

Elena Marielle Kovacs

Neuroscience-Inspired Artificial Intelligence: Exploring Brain-Based Principles for More Adaptive Intelligent Systems

Author(s)	Dr. Elena Marielle Kovacs
Country	United States
Abstract	Artificial intelligence has achieved strong performance through increasing computational scale, optimization capabilities, and access to extensive data. Nevertheless, many intelligent systems remain vulnerable when operating conditions change. They may forget previously acquired knowledge during sequential learning, require substantial energy, demonstrate poor transfer across unfamiliar environments, and struggle to integrate perception, memory, reasoning, and action. Neuroscience-inspired artificial intelligence, commonly described as NeuroAI, treats the biological brain not as a structure that should be reproduced literally but as a source of computational principles that may inform the development of more adaptive intelligent systems. This conceptual study develops an integrative framework connecting sparse and distributed representation, predictive processing, recurrent dynamics, complementary memory systems, experience replay, neuromodulated plasticity, attention, embodiment, and event-driven computation with the engineering requirements of adaptive artificial intelligence. A structured conceptual synthesis and a transparent simulation-based scenario analysis are employed. The study does not report newly collected biological observations, experimental participant data, or measured deployment outcomes. Instead, an illustrative comparison is used to demonstrate how progressively integrating brain-derived principles could improve the balance between rapid adaptation and knowledge retention. The analysis suggests that predictive processing and sparse representation may strengthen selective responses to unfamiliar situations, whereas replay and regulated plasticity may be particularly important for limiting catastrophic forgetting. An integrated NeuroAI architecture may produce a more balanced adaptability–retention profile, although it may also introduce greater training, verification, and hardware complexity. The paper argues that neuroscience-inspired systems should be evaluated simultaneously for task competence, adaptation speed, retention, uncertainty calibration, energy efficiency, behavioral safety, and recovery from harmful updates. Brain-based inspiration becomes academically meaningful when each selected principle is translated into a testable computational mechanism rather than employed as a general biological metaphor.
Keywords	Neuroscience-inspired artificial intelligence; NeuroAI; continual learning; predictive processing; memory replay; neuromorphic computing; adaptive systems; embodied intelligence.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-31

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American Journal of Advanced Multidisciplinary Research and Innovation

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Neuroscience-Inspired Artificial Intelligence: Exploring Brain-Based Principles for More Adaptive Intelligent Systems

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