Similarities Between Brain Neurons Training and Deep Artificial Neural Network Training in AI

Both biological neuron and deep neural network (DNN) training involve learning from experience by adjusting connection strengths (synaptic plasticity vs. weight updates) to improve performance on a task, utilizing feedback mechanisms to guide adjustments, and aiming to build robust models of the environment. Key similarities include the network structure of interconnected nodes, the use of hierarchical layers, and the fundamental concept of learning and adaptation through iterative processing of information.
 

Structural Similarities 

• Interconnected Nodes:

  Both systems consist of numerous interconnected nodes (neurons) that process and transmit information.

• Hierarchical Layers:

  Both biological and artificial neural networks are organized into layers of neurons, forming a complex, hierarchical structure for information processing.

• Input/Output Structure:

  Information flows into the network through input layers and is processed through hidden layers before producing an output.

Functional Similarities

• Learning from Experience:

  Both types of networks learn and adapt by modifying the strengths of their connections based on the data they are exposed to. 

• Synaptic Plasticity/Weight Updates:

  In the brain, this is known as synaptic plasticity, where the strength of connections between neurons changes. In DNNs, this is analogous to weight adjustments during training, often using algorithms like gradient descent to minimize errors. 

• Feedback Loops:

  Both systems use feedback mechanisms to regulate activity and refine processing. In the brain, this involves neural feedback loops, while in DNNs, it's the process of feeding outputs back to be used as inputs for further learning. 

• Building Mental Models:

  Both biological and artificial systems create and update mental models to interpret and predict environmental signals and patterns. 

Training & Adaptation 

• Iterative Improvement:

  Learning in both systems is an iterative process, where initial performance is refined through repeated exposure to data and subsequent adjustments. 

• Feature Extraction:

  Both systems learn to extract relevant features from complex data. The brain strengthens pathways for visual recognition, while DNNs adjust weights to detect similar features in images or other data. 

• Supervised Learning Analogies:

  Aspects of both systems can be described using concepts from supervised learning, where a system is taught to associate certain characteristics with specific classes (e.g., a dog's features) through examples and practice.