Adaptive Synaptic Plasticity Algorithms (ASPA) or Dynamic Adaptive Synaptic Learning (DASL)

ASPA or DASL both names are good for our title of patent or thesis or research. Moreover, we are leaning toward the name of DASL. It will be used as an abbreviation in further explanation.

What Are We Doing?

We are developing Dynamic Adaptive Synaptic Learning (DASL)—a framework that enables neural networks to dynamically evolve their synaptic connections based on real-time environmental feedback. Inspired by biological processes, our approach replaces static synaptic rules (like Hebbian learning or traditional STDP) with a multi-layered, context-sensitive mechanism. This allows the network to continually adapt and learn from new experiences without requiring complete retraining.

What is our main goal and why?

Goal: Our main goal is to create a neural model that exhibits continual, context-aware learning by dynamically updating its synaptic plasticity rules based on both local and global feedback.

Why:

• Overcome Limitations: Traditional deep learning models rely on fixed learning rules and often require extensive retraining when facing new tasks or environments.
• Biological Inspiration: Biological neural networks adapt in real-time through processes like neuromodulation and homeostatic regulation. Emulating this can lead to more robust and flexible AI.
• Enhanced Adaptability: With dynamic adaptation, our model can handle changing environments more efficiently, making it ideal for real-world applications.

What will we achieve?

• Robust Continual Learning: A system capable of learning new tasks on the fly without catastrophic forgetting.
• High Adaptability: Enhanced responsiveness to real-time environmental changes via multi-layered feedback mechanisms.
• Increased Efficiency: More efficient learning through self-adjusting synaptic rules, reducing the need for manual intervention or retraining.
• Biologically Plausible AI: A model that closely mimics human brain dynamics, paving the way for more natural and intelligent behavior in AI systems.

NOTE: Biologically plausible AI refers to artificial intelligence systems and models that are inspired by, and aim to mimic, the structure, dynamics, and learning processes observed in biological neural systems primarily the human brain.