Artificial Neural Networks & Symbolic Thought
Artificial Neural Networks (ANNs), the foundation of modern AI, were first proposed in the 1940s as mathematical models inspired by the neurons of the human brain. While originally limited in scope, today’s deep learning models (like large language models) have become strikingly capable of processing patterns, generating language, and even simulating reasoning.
At a glance, they seem “intelligent.” But what makes them powerful is not mystical — it’s their similarity to how the human mind encodes and manipulates symbols. This gives us a mirror for understanding how beliefs, thoughts, and perception might operate as an information-processing system within consciousness.
Neural Networks as Mirrors of Mind
Nodes & Neurons
Biological neurons fire based on inputs and connections; ANN nodes do the same.
Both systems “learn” not by storing facts directly, but by strengthening or weakening connections.
Layers & Abstraction
In deep networks, information flows through many layers. Early layers detect raw features (edges in an image, syllables in speech), while deeper layers form abstractions (faces, meanings, concepts).
Similarly, the human mind builds up layers of concepts and beliefs, starting with raw sensations and abstracting into meaning, narrative, and identity.
Embeddings & Belief Systems
Modern AI uses vector embeddings: high-dimensional coordinates representing meaning. Concepts close together in this space are semantically related.
Human thought works similarly: our belief systems create a semantic landscape. “Worthless” might cluster near “shame” and “failure” unless debugged and realigned.
Symbolic Thought: Language as Code
The human mind is unique in its reliance on symbols and language to represent reality.
Language doesn’t just describe; it also filters and shapes perception. Words anchor beliefs, making them feel “real.”
Neural networks show us how this can work mechanically: given enough symbolic input, patterns emerge that simulate reasoning.
Parallels to Spiritual Frameworks
Ego as Default Program: Just as an ANN inherits its training data, the ego inherits conditioning from family, culture, and society.
Belief Debugging: Like retraining a model, spiritual practice involves surfacing and “re-weighting” beliefs until they no longer distort.
Awakening: Recognizing that symbols are not reality itself, but maps. In AI terms, the embedding space is not the world — it’s a compressed model.
Why It Matters
For Psychology: Understanding the mind as a symbolic processor helps explain why beliefs feel so binding: they are patterns reinforced across layers.
For Awakening: Seeing thought as symbolic code makes it easier to step back. Just as AI outputs are simulations of meaning, human thoughts are appearances in consciousness, not absolute truth.
For Spirit Tech: ANNs illustrate how intelligence can emerge from pattern recognition alone — showing both the limits of ego-mind and the possibility of intelligence beyond it.
Key Insight
Neural networks don’t “think” like humans — but their mechanics echo our own symbolic processing. They serve as a reminder: our thoughts and beliefs are not reality itself, but patterns built from conditioning. Awakening begins when we stop mistaking the code for the consciousness that runs it.