Dreaming in Silicon: How Neuromorphic- Symbolic Al Architectures Can Emulate Human Analogical Reasoning

What does it mean to truly understand an analogy to see a whale in the architecture of a submarine, or to feel the shape of justice in a metaphor? For decades, artificial intelligence has excelled at pattern recognition while remaining blind to the deeper symbolic dance that humans perform effortlessly. This paper introduces a novel hybrid framework - Neuromorphic-Symbolic Analogical Reasoning (NSAR) ― that bridges the chasm between data-driven neural computation and the structured, rule-governed beauty of symbolic logic. Inspired by neuroscientific models of analogical thought and the emerging field of neuromorphic computing, we propose an architecture in which spike-based neural networks dynamically generate abstract relational embeddings, while a symbolic reasoning layer transforms these embeddings into human-interpretable analogical chains. We evaluate our framework on cross- domain analogy benchmarks, including science, poetry, and legal reasoning, achieving state-of-the-art performance while producing transparent, explainable outputs. Beyond benchmark scores, we argue that this architecture represents a meaningful step toward machines that do not merely process - but genuinely comprehend.

کلیدواژه ها:

neuromorphic computing ، symbolic Al ، analogical reasoning ، hybrid architectures ، explainable Al ، cognitive systems