Feedforward Learning Local Skill

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

FF Layer (ff_layer.jl)
- Local goodness function per layer
- Positive/negative data generation
- Layer-wise gradient updates
Contrastive Learning (contrastive_learning.jl)
- Contrastive divergence variants
- Energy-based formulations
- Hybrid supervised/unsupervised objectives
Statistical Coordination (statistical_coordination.jl)
- Activity normalization between layers
- Whitening and decorrelation
- Predictive coding integration
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

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

Agent Skills: Feedforward Learning Local

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