Classical deep neural networks excel at many tasks and even multimodal generative outputs but remain energetically inefficient by orders of magnitude from the human brain, lack mechanisms for integrated binding, and have been argued toexhibit no genuine route to consciousness. While inspired by neural architectures in brain tissue, deep neural networks face limitations such as scaling limits. Drawing on loop quantum gravity (LQG) and the Orchestrated Objective Reduction (Orch -OR)theory of consciousness, we introduce a framework model of Neural Spinfoam Net-works (NSNs), a bio-inspired AI paradigm in which each neural layer is recast asa spin-network and each learning update as a spinfoam transition by means of gravitational collapse at a phase transition at entropic limits described by a UV/IR fixed point and by the Monster Conformal Field Theory (Monster CFT). Our novel theoretical model leverages Majorana-fermion braiding within spinfoam geometries and a gravitational feedback loop mediated by Majorana biophotons to achieveone-shot, polynomial -time credit assignment for the NP-hard perceptual binding problem. The network’s global state is encoded by a noncommutative-geometry spectral triple (A, H, D), where the Dirac-like dilation operator’s smallest nonzero eigenvalue corresponds directly to the shortest nonzero lattice vector, thereby achieving perceptual binding by means of gravitationally induced phase transition, forming the basis for a more plausible mechanism of backpropagation and weight transportthat are currently unexplained by classical models of brain function. Periodic Floquetdriving and the Cayley -transformed microtubule Hamiltonian yield topologically protected, room-temperature quantum coherence in tubulin-analogous nodes. Recent demonstrations of microtubule superradiance and time -crystalline oscillations within brain tissue further substantiate sustained entangled states and ultrafast biophotonic readout as described by Orch-Or theory, in spite of criticisms, which are discussed.