NeuromorphicComputingHelper Documentation (VB.NET)

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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Dim config = New NeuromorphicConfig With {
    .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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Dim helper = New NeuromorphicComputingHelper(logger, config)
Dim topology = New NetworkTopology With {
    .NeuronCount = 100,
    .LayerSizes = New Integer() { 10, 50, 40 },
    .ConnectionDensity = 0.1
}
Dim networkId = Await helper.CreateNetworkAsync(topology)
        

Processing Spikes

            
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Dim input = New SpikeInput With {
    .Spikes = New List(Of Spike)(),
    .TimeSteps = New List(Of Double)()
}
Dim result = Await helper.ProcessSpikesAsync(networkId, input)
        

Training the Network

            
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Dim trainingData = New TrainingData With {
    .Samples = New List(Of TrainingSample)(),
    .Epochs = 100,
    .TargetError = 0.01
}
Dim result = Await helper.TrainNetworkAsync(networkId, trainingData)
        

Optimizing Network Parameters

            
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Dim parameters = New OptimizationParameters With {
    .MinActivity = 0.001,
    .MaxActivity = 0.1,
    .MinWeight = 0.01,
    .MaxWeight = 1.0,
    .MinNeurons = 50
}
Dim 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

VB.NET Specific Notes

1. Uses VB.NET's With blocks for object initialization
2. Implements IDisposable pattern with VB.NET syntax
3. Uses VB.NET's built-in async/await features
4. Takes advantage of VB.NET's type inference
5. Uses VB.NET-style property and method declarations

Future Enhancements

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