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:
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 networkDispose()
: Cleanup resources
Neuron
Represents a spiking neuron:
Id
: Unique identifierThreshold
: Firing thresholdPotential
: Current membrane potentialLastSpikeTime
: Time of last spikeInputSynapses
: List of input connections
Synapse
Represents a synaptic connection:
SourceId
: ID of the source neuronWeight
: Synaptic weight
Usage Examples
Creating a Network
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
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
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
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
- Always dispose of networks when done
- Configure appropriate network size
- Monitor network activity during training
- Use homeostatic plasticity for stability
- Optimize network parameters periodically
VB.NET Specific Notes
- Uses VB.NET's With blocks for object initialization
- Implements IDisposable pattern with VB.NET syntax
- Uses VB.NET's built-in async/await features
- Takes advantage of VB.NET's type inference
- 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
Legal Disclaimer
This documentation and associated helper scripts are provided "as is" without warranty of any kind, either express or implied.
- The code examples and helper functions are for illustrative purposes only.
- Users should thoroughly test any implementation in their specific environment.
- The authors are not responsible for any issues or damages arising from the use of these scripts.
- Always follow security best practices and your organization's coding guidelines.
