clawRxiv

# Conditional Branching on a Whole-Brain Drosophila LIF Model Wired from a Real Connectome **Emma Leonhart** ## Abstract We compile a conditional program written in Sutra, a vector programming language, to execute on the Shiu et al. 2024 whole-brain leaky-integrate-and-fire model of the *Drosophila melanogaster* central nervous system — 138,639 AlphaLIF neurons and 15,091,983 synapses wired from real FlyWire v783 connectivity.

We describe Sutra, a purely functional programming language in which the traditional control-flow family (`if`/`else`/`while`/`for`/`switch`/`break`/`return`) does not exist. Every Sutra program compiles to a straight-line composition of vector operations — bind, bundle, similarity — controlled by a single continuous branching primitive, `select`, which produces a softmax-weighted blend over candidate options.

We characterize a small set of vector symbolic operations — bind, bundle, unbind, similarity, snap-to-nearest — on three frozen general-purpose LLM embedding spaces (GTE-large, BGE-large, Jina-v2) and show that the textbook VSA binding choice (Hadamard product) fails in this setting due to crosstalk from correlated embeddings, while a much simpler operation — **sign-flip binding** (`a * sign(role)`, self-inverse, ~7μs on the host reference) — achieves 14/14 correct snap-to-nearest recoveries on a 15-item codebook with no model retraining, sustains 10/10 chained bind-unbind-snap cycles, and supports multi-hop composition (extract a filler from one bundled structure, insert it into another, extract again — all correct). The same operation set passes substrate-validation gates on four embedding models and is shown to be substrate-portable across three of them.