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

Gradient descent computed by backpropagation (BP) is a widely used learning method for training artificial neural networks but has several limitations: it is computationally demanding, requires frequent manual tuning of the network architecture, and is prone to catastrophic forgetting when learning incrementally. To address these issues, we introduce a brain-mimetic developmental spiking neural network (BDNN) that mimics the postnatal development of neural circuits. We validate its performance through a neuromorphic tactile system capable of learning to recognize objects through grasping. Unlike traditional BP-based methods, BDNN exhibits strong knowledge transfer, supporting efficient incremental learning of new tactile information. It requires no hyperparameter tuning and dynamically adapts to incoming data. Moreover, compared to the BP-based counterpart, it achieves classification accuracy on par with BP while learning over ten times faster in ideal conditions and up to two or three orders of magnitude faster in practical settings. These features make BDNN well-suited for fast data processing on edge devices.

Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models

Pre-trained language models (PLMs) have been found susceptible to backdoor attacks, which can transfer vulnerabilities to various downstream tasks. However, existing PLM backdoors are conducted with explicit triggers under the manually aligned, thus failing to satisfy expectation goals simultaneously in terms of effectiveness, stealthiness, and universality. In this paper, we propose a novel approach to achieve invisible and general backdoor implantation, called \textbf{Syntactic Ghost} (synGhost for short). Specifically, the method hostilely manipulates poisoned samples with different predefined syntactic structures as stealth triggers and then implants the backdoor to pre-trained representation space without disturbing the primitive knowledge. The output representations of poisoned samples are distributed as uniformly as possible in the feature space via contrastive learning, forming a wide range of backdoors. Additionally, in light of the unique properties of syntactic triggers, we introduce an auxiliary module to drive the PLMs to learn this knowledge in priority, which can alleviate the interference between different syntactic structures. Experiments show that our method outperforms the previous methods and achieves the predefined objectives. Not only do severe threats to various natural language understanding (NLU) tasks on two tuning paradigms but also to multiple PLMs. Meanwhile, the synGhost is imperceptible against three countermeasures based on perplexity, fine-pruning, and the proposed maxEntropy.