NeuromorphicComputingHelper Documentation (C#)

Overview

The NeuromorphicComputingHelper is a theoretical implementation of a spiking neural network system that simulates brain-like neural processing. It provides functionality for creating, training, and optimizing neuromorphic networks with features like spike-timing-dependent plasticity (STDP) and homeostatic plasticity.

Key Features

• Spiking neural network creation and management
• Spike-timing-dependent plasticity (STDP)
• Homeostatic plasticity
• Network optimization
• Configurable neuron and synapse parameters
• Asynchronous processing

Configuration

The helper can be configured through the NeuromorphicConfig class:

            
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var config = new NeuromorphicConfig
{
    MaxNeurons = 1000,            // Maximum number of neurons
    MaxSynapses = 10000,          // Maximum number of synapses
    DefaultThreshold = 1.0,       // Default neuron firing threshold
    LeakRate = 0.1,              // Membrane potential leak rate
    LearningRate = 0.01,         // STDP learning rate
    SimulationTimeStep = TimeSpan.FromMilliseconds(1),
    UseHomeostasis = true        // Enable homeostatic plasticity
};
        

Core Components

SpikeNeuralNetwork

Represents a spiking neural network:

• Neurons: List of neurons in the network
• Dispose(): Cleanup resources

Neuron

Represents a spiking neuron:

• Id: Unique identifier
• Threshold: Firing threshold
• Potential: Current membrane potential
• LastSpikeTime: Time of last spike
• InputSynapses: List of input connections

Synapse

Represents a synaptic connection:

• SourceId: ID of the source neuron
• Weight: Synaptic weight

Usage Examples

Creating a Network

            
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var helper = new NeuromorphicComputingHelper(logger, config);
var topology = new NetworkTopology
{
    NeuronCount = 100,
    LayerSizes = new[] { 10, 50, 40 },
    ConnectionDensity = 0.1
};
var networkId = await helper.CreateNetworkAsync(topology);
        

Processing Spikes

            
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var input = new SpikeInput
{
    Spikes = new List(),
    TimeSteps = new List()
};
var result = await helper.ProcessSpikesAsync(networkId, input);
        

Training the Network

            
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var trainingData = new TrainingData
{
    Samples = new List(),
    Epochs = 100,
    TargetError = 0.01
};
var result = await helper.TrainNetworkAsync(networkId, trainingData);
        

Optimizing Network Parameters

            
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var parameters = new OptimizationParameters
{
    MinActivity = 0.001,
    MaxActivity = 0.1,
    MinWeight = 0.01,
    MaxWeight = 1.0,
    MinNeurons = 50
};
var result = await helper.OptimizeNetworkAsync(networkId, parameters);
        

Error Handling

• All operations are wrapped in try-catch blocks
• Detailed error messages are logged via ILogger
• Network validation is performed at each step
• Resource cleanup through IDisposable pattern

Performance Considerations

• Asynchronous operations for better responsiveness
• Efficient spike processing and weight updates
• Optimized network topology through pruning
• Configurable simulation time steps

Limitations

• This is a theoretical implementation
• Simplified neuron and synapse models
• Basic STDP implementation
• Limited network architectures
• Approximate homeostatic mechanisms

Dependencies

• System.Collections.Generic
• System.Threading.Tasks
• System.Linq
• Microsoft.Extensions.Logging

Best Practices

1. Always dispose of networks when done
2. Configure appropriate network size
3. Monitor network activity during training
4. Use homeostatic plasticity for stability
5. Optimize network parameters periodically

Future Enhancements

• Advanced neuron models
• More sophisticated STDP rules
• Additional plasticity mechanisms
• Better optimization algorithms
• Real-time visualization
• Hardware acceleration support