sarn 0.1.1

SARN - Self-Adaptive Rewiring Neural Network

SARN: Self-Adaptive Rewiring Neural Network

SARN is a modular, brain-inspired neural network engine that supports sparse top-k activations, neurogenesis, self-rewiring, and biological activation functions. Designed to simulate real neuron behavior, it's ideal for building your own evolving neural systems with full control over nodes, weights, connections, and more.

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🌟 Features

• SARN Layers — Top-k activated sparse neurons with rewiring
• Neuron Growth — Dynamically adds neurons when novelty is detected
• Hebbian Plasticity — Rewires weights based on usage strength
• Fully Modular — Build networks with any size, connection config, and activation

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Getting Started

🔧 Install via PyPI

pip install sarn-ai

$ Clone this repository

git clone https://github.com/yourusername/sarn-ai.git
cd sarn-ai
pip install -e .

☁ Usage

Example

from SarnAi import SARNLayer, SARNNetwork

# Define a custom SARN-based network
input_layer = SARNLayer(input_size=4, output_size=8, k=3)
hidden_layer = SARNLayer(input_size=8, output_size=6, k=2)
output_layer = SARNLayer(input_size=6, output_size=2, k=1)

model = SARNNetwork([input_layer, hidden_layer, output_layer])

# Forward pass
output = model.forward([0.3, 0.7, 0.1, 0.9])
print("Output:", output)

# Self-adapt via rewiring and neuron growth
model.adapt()

Activation Functions

from SarnAi.functions import neuro_spike, adaptive_pulse, neuro_softmax

Function	Uses
neuro_spike	Peak around x=1 like a firing neuron
adaptive_pulse	Smooth pulse that adapts with input
neuro_softmax	Confidence-weighted class activation

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⚙️ API Overeview

Class

Class	Description
SARNLayer	Customizable sparse neural layer with weights and top-k fire
SARNNetwork	Connect multiple layers and manage memory + neurogenesis

Functions

Function	Purpose
model.forward(input)	Runs input through all SARN layers
model.adapt()	Rewires weights and grows neurons if needed
layer.set_weights()	Manually set layer weights
layer.add_neuron()	Dynamically add a neuron to a layer

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Contributing

Pull requests are welcome! If you want to propose improvements or report issues, feel free to fork the repo and open a PR or GitHub Issue.

!Note: This project is in active development and experimental.

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📈 Star History

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