Several other neuroscience principles and biological mechanisms could inspire novel parameter-efficient fine-tuning (PEFT) approaches:
Homeostatic Plasticity: This mechanism ensures that neurons maintain stable activity levels despite changes in synaptic strength. Implementing a homeostatic regulation mechanism in neural networks could help maintain balance in parameter adjustments, preventing extreme changes that could lead to instability or overfitting.
Neurogenesis: The process of generating new neurons in the brain could inspire methods for dynamically adding new parameters or layers to a neural network during training. This could allow models to adaptively grow in complexity based on the task requirements, potentially leading to more efficient learning.
Synaptic Scaling: This process involves the uniform adjustment of synaptic strengths across a neuron to stabilize its overall activity. A similar approach could be applied in neural networks, where scaling factors are adjusted uniformly across layers or parameters to maintain stability while allowing for fine-tuning.
Reinforcement Learning Mechanisms: Biological systems often learn through reinforcement, adjusting behaviors based on rewards. Incorporating reinforcement learning principles into PEFT could lead to adaptive tuning strategies that optimize model parameters based on performance feedback.
Distributed Representation: The brain often encodes information in a distributed manner across multiple neurons. This principle could inspire methods that distribute parameter adjustments across multiple layers or components of a neural network, enhancing robustness and reducing the risk of overfitting.