local-pheromone-network

name	local-pheromone-network
category	ai_collection
trigger_words	["local pheromone network","pheromone-weighted learning","synaptic traces","consolidation replay","structural plasticity","partitioned memory","信息素网络","局部学习"]
description	Local Pheromone Network methodology for sparse, local, manually updated neural networks without backpropagation. Uses pheromone-weighted Hebbian updates with short-term/long-term synaptic traces, consolidation, and replay. Achieves partitioned memory preservation, conflict reduction, and structural plasticity through biologically-inspired mechanisms.
arxiv_id	2606.30669
authors	["Xingcheng Fu","Xianjun Chen","Zhihao Li"]
affiliation	Not specified
date	2026-06-22

Local Pheromone Network: Sparse Local Learning with Multi-Scale Synaptic Trails

Overview

Local Pheromone Network is a research prototype for sparse, local, manually updated neural networks that operates without automatic differentiation. Each output unit reads only a fixed local neighborhood of input units subject to geometric distance and molecular-tag compatibility.

The system implements biologically-inspired mechanisms:

• Short-term pheromone traces: Immediate synaptic activity markers
• Long-term pheromone traces: Consolidated synaptic strength indicators
• Consolidation state: Optional mechanism for memory stabilization
• Structural plasticity: Dynamic connectivity changes
• Local replay: Reactivation of recent patterns for conflict resolution

Core Methodology

Local Connectivity Constraints

Each output unit has:

• Fixed local neighborhood: Reads only nearby input units
• Geometric distance constraint: Connection probability decays with distance
• Molecular-tag compatibility: Synapses must match molecular markers (inspired by synaptic tagging)

This contrasts with fully-connected networks where every output can read every input.

Pheromone-Weighted Hebbian Updates

Training does not use backpropagation. Instead, each layer performs:

1. Pheromone-weighted update: Synaptic changes weighted by pheromone trace strength
2. Budgeted subset selection: Only a subset of local synapses updated per timestep
3. Local error + co-activity: Selection based on local error signal and pre/post co-activation

The update budget adapts online:

• Shrinks when loss improves: Focus on currently successful synapses
• Expands when loss worsens: Explore new synaptic neighborhoods

Multi-Scale Trace System

Each synapse stores:

• Weight: Current synaptic strength
• Short-term pheromone trace: Recent activity (fast decay)
• Long-term pheromone trace: Consolidated strength (slow decay)
• Consolidation state: Optional flag for stabilized memories

The dual-trace system separates:

• : Short-term trace mediates rapid changes