  We face up to the challenge of explainability in Multimodal Artificial
Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum
computing, interpretable and transparent spin-geometrical neural architectures
for early fusion of large-scale, heterogeneous, graph-structured data are
envisioned, harnessing recent evidence for relativistic quantum neural coding
of (co-)behavioral states in the self-organizing brain, under competitive,
multidimensional dynamics. The designs draw on a self-dual classical
description - via special Clifford-Lipschitz operations - of spinorial quantum
states within registers of at most 16 qubits for efficient encoding of
exponentially large neural structures. Formally 'trained', Lorentz neural
architectures with precisely one lateral layer of exclusively inhibitory
interneurons accounting for anti-modalities, as well as their co-architectures
with intra-layer connections are highlighted. The approach accommodates the
fusion of up to 16 time-invariant interconnected (anti-)modalities and the
crystallization of latent multidimensional patterns. Comprehensive insights are
expected to be gained through applications to Multimodal Big Data, under
diverse real-world scenarios.
