Adaptive Neural Networks with Stochastic Synapses

Introduction

In recent years, neural networks have revolutionized the fields of machine learning and artificial intelligence. These powerful models are inspired by the human brain, using layers of artificial neurons to process complex data patterns.

In this blog, I will walk you through my novel neural network design: the Adaptive Neural Network with Stochastic Synapses. This architecture introduces innovative concepts, including synaptic variability and adaptive neuron thresholds, to enhance learning capabilities and adaptability.

Understanding the Neural Network Architecture

The Adaptive Neural Network with Stochastic Synapses (ANNS) introduces two features:

1. Stochastic Synapses: This involves adding a degree of randomness to the synaptic weights during each training iteration. By introducing variability, the network explores a broader solution space and avoids overfitting, improving robustness and generalization.
2. Adaptive Neuron Thresholds: Neurons adjust their activation thresholds based on input data, allowing them to self-tune and respond dynamically to different input scales. This feature enhances the network’s ability to learn from diverse patterns and scales of data.