Computational Neuroscience and Machine Intelligence: Exploring Brain-Inspired Models for Adaptive Systems

Yuichi Katori

Computational Neuroscience and Machine Intelligence: Exploring Brain-Inspired Models for Adaptive Systems

Author(s)	Yuichi Katori
Country	Japan
Abstract	Artificial intelligence systems have achieved substantial performance in perception, prediction, language processing, and decision support. However, many remain dependent on extensive labelled data, computationally expensive retraining, centralized processing, and relatively stable task environments. Biological nervous systems exhibit a different form of intelligence. They learn continuously, integrate information across multiple temporal scales, allocate attention selectively, operate with limited energy, adapt to changing conditions, and preserve important knowledge while acquiring new experience. Computational neuroscience provides mathematical and algorithmic tools for examining these capabilities and translating selected neural principles into machine intelligence. This simulation-based study develops a comparative framework for evaluating conventional artificial neural networks, spiking neural models, and hybrid neuroadaptive architectures under sequential learning conditions. The proposed hybrid model integrates predictive coding, synaptic plasticity, reinforcement learning, memory replay, recurrent state representation, and uncertainty-sensitive adaptation. Ten synthetic learning episodes were constructed, including a simulated environmental shift after the sixth episode. Performance was evaluated through an illustrative adaptive-performance index combining task competence, recovery after change, retention of earlier knowledge, learning efficiency, and response stability.
Keywords	computational neuroscience, machine intelligence, brain-inspired computing, adaptive systems, spiking neural networks, predictive coding, neuromorphic computing, continual learning
Field	Engineering
Published In	Volume 2, Issue 9, September 2026
Published On	2026-09-04

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Computational Neuroscience and Machine Intelligence: Exploring Brain-Inspired Models for Adaptive Systems

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