Local Pheromone Network: Sparse Local Learning with Multi-Scale Synaptic Trails, Consolidation, and Replay

Backpropagation-trained dense neural networks are powerful function approximators, but they couple learning across many parameters and can overwrite previous associations when tasks conflict.

This paper describes Local Pheromone Network, a small research prototype for sparse, local, manually updated neural networks.

In Local Pheromone Network, each output unit reads only a fixed local neighborhood of input units subject to geometric distance and molecular-tag compatibility.

Each synapse stores a weight, a short-term pheromone trace, a long-term pheromone trace, and an optional consolidation state.

Training does not call automatic differentiation.

Instead, every layer performs a pheromone-weighted Hebbian-style update on a budgeted subset of local synapses selected from local error and co-activity.

The update budget adapts online: it shrinks when loss improves and expands toward recently active neighborhoods when loss worsens.

Optional mechanisms add structural plasticity, local replay, output masks for partitioned learning, and a target-free local contrastive step.

We present the implementation, learning rule, and preliminary experiments on synthetic regression, partitioned memory, conflicting memory, consolidated conflict, structural plasticity, replay, and a synthetic long-context hybrid memory task.

The prototype learns local linear rules, preserves partitioned memories through tags and masks, reduces forgetting under consolidation, and uses replay under conflict.