Connectionist Learning Biological Neurons and Perceptrons

Lecture 37 of CS607 Artificial Intelligence transitions from symbolic AI to Connectionist Learning (Artificial Neural Networks). The lecture explores the biological inspiration: the human brain contains approximately 100 billion biological neurons interconnected by 100 trillion synapses. Students study biological neuron architecture—Dendrites (which receive chemical signals from other neurons), the Soma or cell body (which integrates electrical inputs), the Axon (which transmits action potentials), and Synapses (connecting junctions whose variable conductance represents learned knowledge). The lecture introduces Frank Rosenblatt's 1958 Artificial Perceptron model: input signals x_i are multiplied by synaptic connection weights w_i, summed with a bias term (net = sum(w_i * x_i) + b), and passed through an Activation Function (such as a hard threshold step function or signum) to produce binary output. The physical analogy between synaptic plasticity and weight updates is established. By engaging with solved past papers by Moaaz and Waqar Siddhu, learners gain actionable insights into recurrent university exam questions. Interactive quiz assessments on VU SIGMA facilitate rapid knowledge verification and retention of critical artificial intelligence terminology. Comprehensive coverage of IEEE and AAAI standards ensures learners communicate with technical precision across global computational intelligence organizations. Detailed analysis of semantic networks and frame systems illustrates how hierarchical inheritance models capture structured real-world domain knowledge. The strategic role of expert system shells is evaluated as an instrument for decoupling domain knowledge bases from generic inference engines. Certainty factor calculi and probabilistic reasoning provide systematic mechanisms for managing incomplete and noisy real-world evidentiary data. The culminating synthesis highlights emerging frontiers in autonomous systems, deep learning architectures, and ethical artificial intelligence governance. In this lecture, students examine fundamental artificial intelligence concepts, rational agent architectures, and computational problem-solving paradigms. Through detailed state-space graphs and algorithmic walkthroughs, the lesson explores search completeness, time complexity, and heuristic evaluation metrics. Special emphasis is placed on heuristic design, pruning unpromising branches, and guaranteeing admissibility in optimal pathfinding algorithms. Mastering these foundational intelligence frameworks empowers computer science scholars to design autonomous agents capable of complex decision-making in dynamic environments.