The Olivera Bias Metric: A Synaptic Input-Output Framework Revealing Bias Patterns in <em>Drosophila melanogaster </em>Optic Lobe

Excitatory and inhibitory (E/I) interactions are central to neural computation, but most studies of E/I "balance" have focused on functional measurements. Far less is known about how balance is constrained by the anatomical distribution of excitatory and inhibitory synapses. This work introduces an approach that quantifies how synaptic anatomy itself shapes the template for this balance. Here, I introduce the Olivera Bias Metric (OBM), a two-dimensional framework that quantifies synaptic bias at the level of individual neurons using a large-scale connectomic dataset. OBM defines input bias as OBMinput = (Ein - Iin) / (Ein + Iin) and output bias as OBMoutput = (Iout - Eout) / (Eout + Iout) , with values normalized between [-1 and 1]. Applied to the Drosophila melanogaster optic lobe connectome (optic-lobe:v1.1), OBM was computed for 53,979 neurons with defined excitatory and inhibitory synaptic weight counts. The analysis revealed structured, transmitter-specific quadrant motifs, cholinergic neurons were broadly distributed, GABAergic and glutamatergic neurons clustered toward excitatory-biased quadrants, and histaminergic neurons displayed polarized bimodal distributions. The neuromodulatory transmitters: dopamine and octopamine also showed distinct and non-random patterns despite their smaller populations. These results indicate that synaptic bias reflects circuit-specific organization rather than stochastic variation. OBM thus provides a compact and interpretable framework for mapping excitatory-inhibitory balance in large connectomic data. While demonstrated here in the Drosophila melanogaster optic lobe, the metric is general and can be adapted to other brain regions and species as reliable neurotransmitter annotations become available. By revealing structured bias landscapes, OBM offers a foundation for possible hypothesis-driven investigations into how excitatory and inhibitory biases shape neural circuits.