Feedforward Learning Local

Feedforward Learning Local

Category: Phase 3 Core - Alternative Learning Paradigms Status: Skeleton Implementation Dependencies: None (standalone learning framework)

Overview

Implements forward-forward (FF) learning algorithm and variants that eliminate backpropagation through local, layer-wise contrastive objectives. Each layer learns to distinguish positive from negative data independently.

Capabilities

• Forward-Forward Algorithm: Hinton's layer-local learning
• Contrastive Objectives: Positive/negative data discrimination
• No Backprop: Purely feedforward gradient computation
• Statistical Communication: Inter-layer coordination via activity statistics

Core Components

1. FF Layer (ff_layer.jl)

   • Local goodness function per layer
   • Positive/negative data generation
   • Layer-wise gradient updates

2. Contrastive Learning (contrastive_learning.jl)

   • Contrastive divergence variants
   • Energy-based formulations
   • Hybrid supervised/unsupervised objectives

3. Statistical Coordination (statistical_coordination.jl)

   • Activity normalization between layers
   • Whitening and decorrelation
   • Predictive coding integration

4. FF Network (ff_network.jl)

   • Multi-layer FF architecture
   • Inference and training loops
   • Comparison with backprop baselines

Integration Points

• Input from: Raw data (no dependencies on other skills)
• Output to: emergent-role-assignment (decentralized learning signals)
• Coordinates with: categorical-composition (compositional learning)

Usage

using FeedforwardLearningLocal

# Create FF network
network = FFNetwork([
    FFLayer(input_dim=784, hidden_dim=500, threshold=2.0),
    FFLayer(input_dim=500, hidden_dim=500, threshold=2.0),
    FFLayer(input_dim=500, hidden_dim=10, threshold=1.0)
])

# Train on MNIST
for (x_pos, y) in train_data
    # Generate negative data by corrupting label
    x_neg = overlay_wrong_label(x_pos, y)

    # Local learning at each layer
    train_step!(network, x_pos, x_neg)
end

# Inference
predictions = predict(network, test_data)

References

• Hinton "The Forward-Forward Algorithm" (2022)
• LeCun et al. "A Tutorial on Energy-Based Learning" (2006)
• Nokland & Eidnes "Training Neural Networks with Local Error Signals" (ICML 2019)

Implementation Status

• Basic FF layer implementation
• Positive/negative data generation
• Multiple variants (supervised, unsupervised)
• Benchmark against backprop
• Integration with predictive coding

Para(Optic) atlas

Part of: para-mensch-commons.