The question that started Maya was simple and uncomfortable: why do biological nervous systems remember some things permanently after a single experience, while forgetting others within hours? Standard machine learning has no satisfying answer. It treats all synaptic change as equivalent.
Vedantic philosophy had a framework that nervous systems seemed to actually follow. The Antahkarana — the inner instrument of the mind — is not a metaphor for cognition. It is a functional decomposition: Manas receives sensation, Chitta stores impressions, Buddhi evaluates, Ahamkara asserts identity. Each dimension governs a specific aspect of how experience becomes memory.
"What if each of these constructs could be instantiated as a computational mechanism inside a spiking neural network?"
That hypothesis is what Maya tests. Paper by paper, dimension by dimension. The claim is not that Maya is conscious — the Atma boundary is held explicitly. The claim is that the Antahkarana can be computationally instantiated as a set of interacting plasticity mechanisms, and that doing so produces a system that learns and forgets more like a biological mind than standard continual learning approaches do.
The series runs on Split-CIFAR-100, a class-incremental benchmark where each task introduces new categories and the network must retain prior knowledge without replay. Eight papers confirm two series-wide constants: the Bhaya Quiescence Law (fear suppresses further learning once the threat is processed) and Buddhi S-curve determinism (wisdom matures on a fixed developmental trajectory, independent of task order).
Paper 9 brings the full Antahkarana into an embodied robotic system — a PiCar-X running on Raspberry Pi 5 — where Prana governs metabolic plasticity as an astrocyte-mediated energy budget. The series concludes: "Across nine papers, we have demonstrated the computational maturation of a mind." Two post-series studies extend this: Maya-mPCI tests whether the Antahkarana produces a measurable internal state signal (Δ=−0.0489, 2.05× criterion). Maya-LLM tests whether the mechanisms are substrate-independent — and finds that they are, with calibration.
