In this part 1 video of the Cognitive Architecture in Neural Networks, I present my research into the Syntrons architecture—a novel computational paradigm for neural networks that moves beyond the limitations of static weights. We explore how to achieve a form of network cognition by replacing fixed parameters with dynamic, probabilistic models of computation.

This is a deep dive into an architecture that learns not just what to compute, but how to formulate its own operational plan for every forward pass. We will cover the core concepts from the ground up, and discuss the implications for building more adaptive, robust, and interpretable AI systems.

Source + Code: abdelrrahim.com/syntrons

TIMESTAMPS:
0:00 - Introduction
0:22 - The Limitations of the Static Weight Paradigm
1:02 - The Primitive Basis: A Shared, Universal Computational Vocabulary
1:26 - The Mixture Distribution: A Learned Policy of Possibilities
1:50 - The Emergent Weight: Computation as Expectation
2:12 - Cognitive Mechanism 1: Working Memory as a Temporal Trace
2:25 - Cognitive Mechanism 2: Dynamic Structural Plasticity (The Birth of Primitives)
3:30 - Source + Code

// KEY CONCEPTS DISCUSSED
Neural Network Architecture
Artificial General Intelligence (AGI) / Artificial Superintelligence (ASI)
Dynamic Structural Plasticity
Probabilistic Neural Networks
Emergent Computation
Meta-Learning & Self-Modifying Architectures
Cognitive Science & AI
ASI, AGI, AI

Thank you for joining me on this deep dive. If you are a researcher, engineer, or enthusiast in this field, I would be very interested to read your thoughts and feedback in the comments below.

#ai #deeplearning #neuralnetworks #artificialintelligence #cognitivescience #pytorch #machinelearning #research