Local Credit Assignment under Dale Constraints with Paired Non-Negative Channels

Biologically plausible credit-assignment models seek to explain how neural circuits can learn effectively under the constraints of real neurons. Despite significant progress, existing biologically motivated credit-assignment models generally do not jointly accommodate non-negative neuronal activity, fixed excitatory/inhibitory identity, and local synaptic learning. Under these constraints, neurons cannot directly represent negative activity or change the sign of their outgoing influence, making the propagation of signed learning signals particularly challenging. We introduce a biologically motivated architecture that represents signed quantities through paired non-negative channels, with sign encoded by channel identity rather than negative neuronal activity. A simple excitatory-inhibitory on-off motif implements this representation and is repeated throughout bottom-up and top-down pathways. Combined with a local activity-based learning rule, paired top-down channels provide opposing contributions to synaptic updates, yielding an effective signed credit-assignment signal using only local interactions and fixed-sign circuitry. We show theoretically that, when bottom-up and top-down connectivity are aligned, the resulting updates exactly recover backpropagation despite propagating only non-negative neural signals. Empirically, the model learns effectively across image-classification benchmarks, remains effective with non-aligned bottom-up and top-down pathways, and scales to Tiny ImageNet, showing that the proposed biological constraints need not come at the cost of effective representation or learning. These results provide a concrete mechanism for local credit assignment under non-negative activity and Dale-constrained connectivity.