AI神经网络能否自主修改/新增参数与超参数？实现方法探究

Absolutely—modern neural networks can modify their own parameters, adjust hyperparameters, and even add new parameters or network components. This falls under the umbrella of neuroevolution and self-adaptive machine learning, which draw inspiration from how biological brains grow and adapt over time.

How It Works: Key Approaches #

1. Self-Modifying Parameters (Weight Adaptation) #

Most standard neural networks adjust weights/biases during training via backpropagation, but that’s guided by a fixed loss function. For autonomous parameter modification, systems use:

• Reinforcement Learning (RL) with Parameter Tuning: The network treats weight adjustments as part of its action space. An RL agent might tweak specific weights to boost its reward signal over time, no external optimizer required.
• Hebbian Learning: Inspired by biological plasticity ("fire together, wire together"), connections strengthen/weaken based on activity correlation. This lets the network adapt dynamically as it processes data, without human-led training loops.

2. Adjusting Hyperparameters #

Hyperparameters (learning rate, layer count, batch size) are often set by humans, but self-adaptive systems can tweak these too:

• Bayesian Self-Tuning: The network uses probabilistic models to predict how hyperparameter changes impact performance, then iteratively adjusts them to maximize accuracy or efficiency.
• Evolutionary Algorithms: Treat hyperparameters as "genes" in a network population. Over generations, top-performing networks are selected, their hyperparameters mutated/crossed over to create new variants—all without human input.

3. Adding New Parameters/Components #

This is where we get closest to the "growing brain" analogy. Systems can expand their structure by adding neurons, layers, or sub-networks:

• Neuroevolution of Augmenting Topologies (NEAT): Starts with a minimal network (like your 3-emotion-parameter example) and incrementally adds nodes/connections. It tracks "innovations" to avoid redundancy, so the network only grows when it identifies a need for more complexity (e.g., realizing it can’t distinguish "frustration" from "anger" with just 3 parameters).
• Dynamic Neural Networks: Can add/prune neurons/layers during training or inference. For example, a network might start with 3 emotion nodes, then spawn new parameters (like "mixed valence intensity") when it encounters ambiguous inputs—guided by a loss signal that flags insufficient performance.

Example: The 3 Emotion Parameters Scenario #

Let’s walk through how your hypothetical emotion network might self-expand:

1. Performance Gap: The network struggles to classify nuanced emotions like "bittersweet" or "irritated"—its 3 core parameters can’t capture those subtle combinations.
2. Growth Trigger: A built-in performance monitor (or evolutionary fitness function) detects consistent high error rates for these cases.
3. Self-Expansion: Using NEAT or dynamic network logic, the network adds new parameters (e.g., "arousal fluctuation rate") and connects them to existing emotion nodes.
4. Validation: The new parameters are tested against the dataset—if they reduce error, they’re retained; if not, they’re pruned or modified. Over time, the network builds a richer set of emotion parameters tailored to its task.

It’s worth noting that while these systems mimic biological brain plasticity, they’re still simplified models. Biological brains have far more complex rewiring mechanisms, but modern self-adaptive networks are a meaningful step toward more autonomous, brain-like AI.

内容的提问来源于stack exchange，提问作者PearsUndGrapes