An Intrinsically Knowledge-Transferring Developmental Spiking Neural Network for Tactile Classification

Gradient descent, computed through backpropagation (BP), has been widely used to train spiking neural networks (SNNs). However, the approach has several limitations. It requires manual intervention to tune the network architecture, is prone to catastrophic forgetting of previously learned information when exposed to data containing new information, and is computationally demanding. To address these issues, we propose brain-mimetic developmental spiking neural networks (BDNNs), which emulate the postnatal development of biological neural circuits. We evaluated BDNNs using a neuromorphic tactile system with the task of classifying objects through grasping. Our findings show that BDNNs grow dynamically in response to input data by incrementally recruiting hidden neurons, leading to steadily increasing classification accuracy without the need for manual architecture tuning. The growth process adapts autonomously to the complexity of incoming data. BDNNs also exhibit strong knowledge transfer capabilities, which effectively leverage previously learned knowledge about grasping objects to incrementally learn about new objects. Furthermore, in comparative experiments using the same dataset and hardware, BDNNs achieved classification performance comparable to the standard BP-based method and its variants, while learning one to three orders of magnitude faster. Furthermore, the BDNN outperforms existing continual learning algorithms in the performance and speed. These results highlight BDNNs as a promising approach for continual learning and real-time edge computing applications. The source code of our work is publicly available at https://github.com/1jiaqixing/BDNNversion1.