🎯 Neural Network Training

Perfect Mode: Target accuracy 99.5-100%, rapid convergence with minimal loss fluctuations.
Epoch: 0/80
Loss: 2.300
Accuracy: 0.0%
Loss (lower is better)
Accuracy (higher is better)

Spiking Neural Networks (SNNs)

Spiking neural networks process information through discrete spike events, mimicking biological neurons more closely than artificial neural networks.

Key Characteristics:

  • Event-Driven: Neurons communicate via discrete spikes
  • Temporal Coding: Information encoded in spike timing
  • Energy Efficient: Only compute when spikes occur
  • Biological Plausibility: Closer to real neural processes
  • Neuromorphic Hardware: Native support on Intel Loihi, IBM TrueNorth

Neuron Models:

  • Integrate-and-Fire (IF): Simple, accumulates input until threshold
  • Leaky Integrate-and-Fire (LIF): Adds membrane potential decay
  • Hodgkin-Huxley: Biologically detailed ionic channel model
  • Izhikevich: Balance between biological detail and computational efficiency

Applications:

  • Neuromorphic Computing
  • Pattern Recognition
  • Real-time Signal Processing
  • Brain-Computer Interfaces
  • Autonomous Robotics

πŸ“Š Standard ANN vs Spiking Networks Comparison

Feature Artificial Neural Networks (ANN) Spiking Neural Networks (SNN)
Information Type Continuous values Discrete spike events
Computation Model Synchronous, feedforward Asynchronous, event-driven
Energy Consumption High (continuous computation) Low (sparse events)
Temporal Dynamics Temporal pooling via RNN/LSTM Native temporal processing
Biological Plausibility Limited similarity High biological realism
Training Algorithm Backpropagation STDP, Surrogate Gradients
Hardware Support GPUs, TPUs widely available Neuromorphic chips (Loihi, TrueNorth)
Latency Feed-forward in few ms Requires time window (temporal integration)

πŸ“ˆ Advanced Analytics

Model Performance Metrics

Training Efficiency
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Convergence Speed
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Final Accuracy
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Energy Score
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Analysis Details

Training Efficiency: Ratio of accuracy gained per epoch. Higher is better.

Convergence Speed: Epochs required to reach 95% of target accuracy.

Energy Score: Estimated energy efficiency based on mode and architecture.

Mode-Specific Insights

Select a training mode and run simulation to see detailed insights.

πŸ“– Training Modes

🌟 Perfect Mode (99.5–100% accuracy)

  • Final Loss: 0.001 – 0.01
  • Final Accuracy: 99.5 – 100%
  • Characteristics: Rapid convergence with minimal fluctuations
  • Use Case: Showcase optimal training performance

πŸ“ˆ Normal Mode (94–98% accuracy)

  • Final Loss: 0.01 – 0.05
  • Final Accuracy: 94 – 98%
  • Characteristics: Realistic training with natural oscillations
  • Use Case: Most common real-world scenario

πŸ”΄ Hard Mode (70–85% accuracy)

  • Final Loss: 0.20 – 0.60
  • Final Accuracy: 70 – 85%
  • Characteristics: Slow convergence with significant noise
  • Use Case: Demonstrate learning challenges

⚠️ Overfit Mode (poor generalization)

  • Final Loss: 0.01 – 0.05 (memorizes training data)
  • Final Accuracy: 60 – 80% (fails on unseen data)
  • Characteristics: Loss decreases but accuracy stagnates
  • Use Case: Illustrate the overfitting problem

πŸ”¬ Technical Details

Learning Rate Schedule:

  1. Accuracy < 50%: 2.0Γ— multiplier (rapid initial learning)
  2. Accuracy 50–80%: 1.0Γ— multiplier (steady progress)
  3. Accuracy 80–95%: 0.4Γ— multiplier (diminishing returns)
  4. Accuracy 95%+: 0.1Γ— multiplier (fine-tuning only)

Loss Decay: Exponential: loss = loss - decay Γ— (1 - epoch/80)

Noise Injection: Micro-oscillations Β±0.005 to Β±0.020 for realism