Enterprise AI Analysis

A Miniature Brain Transformer: Thalamic Gating, Hippocampal Lateralization, Amygdaloid Salience, and Prefrontal Working Memory in Attention-Coupled Latent Memory

We present a miniature brain transformer architecture that extends the attention-coupled latent memory framework of Jeong [12] with four additional brain-region analogues: a thalamic relay, an amygdaloid salience module, a prefrontal working-memory (PFC) buffer, and a cerebellar fast-path, all coupled by inhibitory callosal cross-talk between lateralized hippocampal banks. We evaluate on a two-domain benchmark—MQAR (Multi-Query Associative Recall [1]; episodic domain) and modular arithmetic (+1 mod 10; rule-based domain)—using a seven-variant additive ablation. The central empirical finding is a surprise: inhibitory callosal coupling alone never lateralizes the banks (variants 1–5 maintain Dsep~0.25 and Pct≈0.25 for all 30 epochs). Functional lateralization requires the synergy of PFC and inhibition: only when the PFC buffer is added (variant 6) does a sharp, discontinuous phase transition fire—at epoch 11 for the PFC-only variant and epoch 10 for the full model—collapsing Pct from 0.25 to ≈ 0.002 and more than doubling Dsep from 0.251 to 0.501 in a single gradient step. The PFC buffer acts as a symmetry-breaker: its slowly drifting domain context creates the initial asymmetry that the inhibitory feedback loop then amplifies irreversibly. The cerebellar fast-path accelerates the transition by one epoch (epoch 10 vs. epoch 11) with no asymptotic change, confirming its convergence-acceleration role. The result constitutes a novel, falsifiable prediction—no lateralization without working memory context—and a principled, neurobiologically motivated blueprint for hierarchical persistent memory in sequence models.