hybrid-ann-snn-local-plasticity

name	hybrid-ann-snn-local-plasticity
description	Hybrid ANN-SNN pipeline with local plasticity: couples pretrained ANN encoders with spiking classifiers using biologically-inspired local learning rules, bypassing end-to-backprop. Use when building energy-efficient spiking neural networks from pretrained models, implementing local Hebbian/plasticity rules, or converting ANN features to spike trains.
metadata	{"arxiv_id":"2606.20151","published":"2026-06-18","authors":"","tags":["spiking-neural-network","local-plasticity","ann-snn-conversion","neuromorphic","rate-coding"]}

Hybrid ANN-SNN Pipeline with Local Plasticity

Core Idea

Combine a pretrained ANN encoder (e.g., EfficientNet) with a spiking classifier trained using local, biologically-plausible learning rules. This bypasses end-to-end backpropagation while achieving performance comparable to deep networks (99.09% on 64-class ImageNet).

Architecture

Input → [Pretrained ANN Encoder] → Rate Coding → [Spiking Classifier] → Output
                              (activations → spike trains)   (local learning rules)

Key Components

1. ANN Encoder Selection

• Use any pretrained feature extractor (EfficientNet, ResNet, etc.)
• Extract intermediate layer activations as feature embeddings
• No fine-tuning needed — encoder stays frozen

2. Rate Coding Conversion

• Convert continuous ANN activations to spike trains
• Higher activation → higher firing rate
• Temporal window determines spike count resolution
• Trade-off: longer windows = more accurate but higher latency

3. Spiking Classifier (CoLaNET)

• Spiking neural network with local learning rules
• Receives spike trains from rate-coded ANN features
• Trained independently — no gradient flow back to encoder

4. Local Learning Rules

• Hebbian plasticity: strengthen co-active connections
• Spike-timing dependent plasticity (STDP)
• Local error signals without global backpropagation
• Biologically plausible — no weight transport problem

Workflow

1. Select pretrained encoder: Choose based on task domain and feature requirements
2. Extract features: Run input through encoder, capture activations
3. Configure rate coder: Set temporal window and firing rate mapping
4. Build spiking classifier: Design SNN architecture for the task
5. Train with local rules: Apply Hebbian/STDP learning to SNN weights
6. Evaluate: Measure accuracy vs energy efficiency trade-offs

Advantages

• Biological plausibility: No end-to-end backpropagation
• Energy efficiency: SNN inference is sparse and event-driven
• Leverages pretrained models: Benefits from large-scale pretraining
• Modular: Encoder and classifier can be developed independently
• Scalable: Local rules parallelize naturally

Pitfalls

• Information loss in rate coding: Continuous values discretized to spikes
• Temporal latency: Rate coding requires time windows for accuracy
• Local rule limitations: May not capture complex feature interactions
• Hyperparameter sensitivity: Learning rates, thresholds, time constants need careful tuning
• Task dependency: Works best when pretrained features are already discriminative

Activation Keywords

• hybrid ann snn
• local plasticity
• spiking neural network conversion
• rate coding
• biologically plausible learning
• CoLaNET
• hebbian spiking
• snn classifier
• neuromorphic pipeline