Meta-learning in spiking neural networks with reward-modulated STDP

The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neural networks perform poorly when there is limited data or when they need to adapt quickly to new unseen tasks. Meta-learning models are proposed to facilitate quick learning in low-data regimes by employing absorbed information from the past. Although some models have recently been introduced that reached high-performance levels, they are not biologically plausible. In our research, we have proposed a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex using spiking neural networks with a reward-based learning system. The major contribution of our work lies in the design of a bio-plausible meta-learning framework that incorporates learning rules such as SpikeTiming-Dependent Plasticity (STDP) and Reward-Modulated STDP (R-STDP). This framework not only reflects biological learning mechanisms more accurately but also attains competitive results comparable to those achieved by traditional gradient descent-based approaches in meta-learning. Our proposed model includes a memory designed to prevent catastrophic forgetting, a phenomenon that occurs when meta-learning models forget what they have learned so far as learning the new task begins. Furthermore, our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware. The implications and predictions of various models for solving few-shot classification tasks are extensively analyzed. Base on the results, our model has demonstrated the ability to compete with the existing state-of-the-art metalearning techniques, representing a significant step towards creating AI systems that emulate the human brain's ability to learn quickly and efficiently from limited data.