Intellicortex
Intellicortex
SPARSITRON™ enforces a fixed routing budget per layer and per timestep. As network activity increases, the per‑node fan‑out decreases automatically. This simple inverse relationship keeps computation predictable and prevents runaway resource consumption. By treating computation as a governed quantity rather than an emergent side‑effect, SPARSITRON™ delivers bounded latency and energy‑proportional scaling.
Each node operates as a local state machine built around integers. Nodes maintain counters that decay over time, compare against thresholds and fire discrete events instead of continuous floating‑point activations. This leads to deterministic behaviour, removes the need for expensive floating‑point arithmetic and enables efficient implementation on a wide variety of hardware.
Stable cognitive structure isn’t stored in large adjacency matrices. Instead SPARSITRON™ uses small connectivity generators. Routing is computed on demand using node identifiers, context salts, routing motifs and compact codebooks. These generators define the structure deterministically, allowing large‑scale connectivity without memory explosion.
The architecture preserves stable cognitive priors through generator‑defined connections. These priors act as long‑term knowledge and are robust to learning.
Parallel to the stable field, a structurally plastic memory allows rapid adaptation. Adaptive connections form and decay based on experience, providing flexibility without overwriting cognition.
SPARSITRON™ learns continually. Adaptive connections appear when needed and disappear when unused. A periodic consolidation process identifies persistent patterns, updates generator parameters and modifies future routing behaviour. This way, experience reshapes cognition without copying individual edges into a static graph.
SPARSITRON™ acts as an intelligence control substrate rather than a monolithic model. Dense neural networks can be gated on and off based on sparse signals, enabling efficient hybrid systems. This integration supports cost‑governed inference and allows existing models to benefit from compute governance and continual learning.