How Do Activation Functions in Neural Networks Relate to How Humans Think?

Activation functions in neural networks are important parts that help decide how information is processed. They are somewhat similar to how humans think, but this comparison also shows some problems that can make these AI systems less effective.

1. The Complexity of Human Thinking:

• Human thinking is very complicated, and we do not fully understand it. This makes it hard to create exact models in artificial networks.
• Our brains can handle a lot of information at once. We mix together what we see, feel, and understand in ways that current neural networks have trouble copying.

2. Oversimplification:

• Activation functions like sigmoid, ReLU (Rectified Linear Unit), and tanh are basic math operations. They don't capture the detailed ways humans learn.
• For instance, the ReLU function adds some flexibility but misses important parts of thinking, like how we pay attention, remember things, or make decisions. Human thought works in a connected and changing way.

3. Limited Flexibility:

• Activation functions can introduce some changes, but they are fixed and set ahead of time. Human thinking, on the other hand, changes based on the situation, past experiences, and what’s happening around us.
• This inflexibility means that a neural network might not work well on different tasks, unlike humans who use what they already know in new situations.

4. Difficulty in Understanding:

• Neural networks often act like "black boxes." This means they produce results that are hard to trace back to what went in. While sometimes human thinking can also be unclear, we can think about and explain our thought processes to some extent.
• This issue of understanding is a big problem in important areas like healthcare. It's crucial to know why a decision was made, especially when it affects people's lives.

Possible Solutions:

• Creating models that blend connectionist theories with logic-based reasoning could improve how these neural networks learn.
• Using techniques like attention mechanisms can help networks focus on the key parts of the data, similar to how humans pay attention, which can make them better at certain tasks.
• Adding insights from biology into how neural networks are made and how they work might help create better activation functions that more closely match human cognitive processes.

Conclusion: While there are some similarities between activation functions in neural networks and human thinking, the big differences in complexity, flexibility, and understanding make current models less effective. Solving these problems is important for improving neural networks and how we apply them in learning. Exploring mixed models and studying biological principles could lead to new discoveries that help us understand both artificial intelligence and human learning better.