Distance-Dependent Connectivity Shapes Continual Learning by Synaptic-Resource-Delimited Separation of Neural Dynamics

Biological circuits learn without catastrophic forgetting, but the structural basis of this ability remains unclear. We investigate whether distance-dependent connectivity (DDC), spatial recurrency found in the mammalian cortex, contributes to continual learning by embedding neurons of a biologically plausible recurrent spiking neural network in a 3D Euclidean substrate, using pairwise distance to set recurrent connection probability, and ablating that distance dependence in silico. We show that DDC shapes a synaptic resource geography: it controls the spatial breadth of the candidate synaptic pool for learning to occur, plasticity further compresses that pool, and the resulting substrate governs how subsequent inputs compete for the same synapses. On challenging 10-way class-incremental MNIST classification, this produces a non-monotonic accuracy curve, with an intermediate DDC range achieving the best performance (71.4%) and outperforming both highly local circuits that collapse into a synaptic-resource bottleneck and random connectivity that produces weakly guided, broader synaptic contention. This pattern was better explained by synaptic competition, resulting in separable neural dynamics, than by neuron assembly separation. These results suggest that appropriate cortical DDC may prevent diffuse random contention. For continual learning problems, this finding highlights the conceptual importance of circuit dynamics more than active neuron ensembles and engrams.