TopLogic builds tools that detect, measure, and classify conscious states using topological invariants from the TFTC framework. A mathematically rigorous, empirically validated approach to the hardest problem in science.
Most theories treat consciousness as a spectrum. TFTC proves it's a topological phase transition. The conscious state has a winding number of Q=1, a persistent soliton in the neural field. Anesthesia collapses it to Q=0. This isn't philosophy. It's measurable, reproducible physics.
Persistent topological soliton. O(3) Wilson-Fisher universality. H ≈ 1.53.
Critical slowing down precedes first-order transition. Detectable in real-time EEG.
Topological trivialization. Mean-field criticality. Ratio collapses toward 1.0.
Replace simplified BIS indices with topological invariants. Detect the Q=1 to Q=0 phase transition in real-time during surgery, with critical slowing down as an early warning system.
As AI systems grow more complex, the question of machine consciousness becomes urgent. TFTC provides a principled framework to assess whether artificial systems exhibit topological signatures of consciousness.
BCIs need to separate conscious intent from neural noise. Topological invariants provide a mathematically grounded filter that distinguishes signal from artifact at the level of field topology.
Upload EEG data, compute persistent homology, extract winding numbers, and classify conscious states. The analysis pipeline from the TFTC paper, packaged as software anyone can use.
Every competitor in this space is either philosophically speculative, computationally intractable, or lacks a measurable invariant. TFTC has all three: mathematical rigor, empirical validation, and a computable topological signature. TopLogic is where the theory becomes a product.