While deep neural networks (DNNs) have achieved remarkable performance in tasks such as image recognition, they often struggle with generalization, learning from few examples, and continuous adaptation - abilities inherent in biological neural systems. These challenges arise due to DNNs' failure to emulate the efficient, adaptive learning mechanisms of biological networks. To address these issues, we explore the integration of neurobiologically inspired assumptions in neural network learning. This study introduces a biologically inspired learning rule that naturally integrates neurobiological principles, including sparsity, lognormal weight distributions, and adherence to Dale's law, without requiring explicit enforcement. By aligning with these core neurobiological principles, our model enhances robustness against adversarial attacks and demonstrates superior generalization, particularly in few-shot learning scenarios. Notably, integrating these constraints leads to the emergence of biologically plausible neural representations, underscoring the efficacy of incorporating neurobiological assumptions into neural network design. Preliminary results suggest that this approach could extend from feature-specific to task-specific encoding, potentially offering insights into neural resource allocation for complex tasks.

翻译：尽管深度神经网络（DNNs）在图像识别等任务中取得了显著成就，但其在泛化能力、少样本学习以及持续适应方面仍面临挑战——而这些能力是生物神经系统固有的。这些挑战源于DNNs未能有效模拟生物网络的高效自适应学习机制。为解决这些问题，我们探索了在神经网络学习中整合受神经生物学启发的假设。本研究提出了一种受生物学启发的学习规则，该规则自然地整合了包括稀疏性、对数正态权重分布以及遵循戴尔定律在内的神经生物学原理，无需显式强制约束。通过遵循这些核心神经生物学原理，我们的模型增强了对对抗攻击的鲁棒性，并展现出优异的泛化能力，尤其在少样本学习场景中。值得注意的是，整合这些约束条件促使了生物学合理的神经表征的涌现，这凸显了将神经生物学假设融入神经网络设计的有效性。初步结果表明，该方法可从特征特异性编码扩展到任务特异性编码，有望为复杂任务中的神经资源分配提供新的见解。