Spiking Neural Network with Pattern Recognition

This project implements a Spiking Neural Network (SNN) in C++, integrates it with Python using pybind11, and provides a Python interface for dynamic pattern recognition and real-time data processing.

Table of Contents

• Installation
• Usage
• Details
  • SNN Model
  • Python Interface
• Example

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Installation

Prerequisites

• C++ compiler
• Python 3
• pybind11
• numpy
• scikit-learn

Build the C++ Library

1. Clone the repository:

   git clone https://github.com/HermiTech-LLC/2create.git
   cd 2create

2. Navigate into it build directory:

   cd build

3. Configure and build the project using CMake:

   cmake ..
   make

This will compile the C++ code and create a shared library that can be used in Python.

Usage

Python Interface

1. Ensure that the compiled shared library is in your Python path.
2. Import the necessary modules and use the CognitiveModel class as shown in the example below.

Example

import snn
import numpy as np
from Python.pattern_recon import CognitiveModel

num_neurons = 10
cognitive_model = CognitiveModel(num_neurons)

# Initial stimuli
initial_stimuli = np.random.rand(num_neurons).tolist()
print("Initial Stimuli:", initial_stimuli)

output = cognitive_model.stimulate(initial_stimuli)
print("Output after initial stimuli:", output)

# New data
new_data = np.random.rand(num_neurons).tolist()
print("New Data:", new_data)

updated_output = cognitive_model.stimulate(new_data)
print("Output after new data:", updated_output)

# Training example (placeholder data)
training_data = [np.random.rand(num_neurons).tolist() for _ in range(100)]
cognitive_model.train(training_data)

# Recognize pattern in new input
pattern = cognitive_model.recognize_pattern(new_data)
print("Recognized pattern:", pattern)

# Reconfigure with new insights
new_insights = [np.random.rand(num_neurons).tolist() for _ in range(50)]
cognitive_model.reconfigure(new_insights)

Details

SNN Model

The SNN model is implemented in C++ with three main classes:

• Neuron: Represents a neuron in the network.
• Synapse: Represents a synapse between two neurons.
• SNNModel: Manages a network of neurons and synapses, and handles stimulation, updating, and training.

The model includes basic spiking behavior and a simple form of Spike-Timing-Dependent Plasticity (STDP).

Python Interface

The Python interface uses pybind11 to expose the C++ classes and methods to Python. The CognitiveModel class in pattern_recognition.py integrates the SNN model with machine learning for pattern recognition and reconfiguration.

Example

An example of using the CognitiveModel class is provided in the Usage section above. This example demonstrates how to stimulate the network, train it with data, recognize patterns, and reconfigure the network with new insights.