Neural networks (NNs) are known to have diverse defects such as adversarial examples, backdoor and discrimination, raising great concerns about their reliability. While NN testing can effectively expose these defects to a significant degree, understanding their root causes within the network requires further examination. In this work, inspired by the idea of debugging in traditional software for failure isolation, we propose a novel unified neuron-isolation-based framework for debugging neural networks, shortly IDNN. Given a buggy NN that exhibits certain undesired properties (e.g., discrimination), the goal of IDNN is to identify the most critical and minimal set of neurons that are responsible for exhibiting these properties. Notably, such isolation is conducted with the objective that by simply ‘freezing’ these neurons, the model’s undesired properties can be eliminated, resulting in a much more efficient model repair compared to computationally expensive retraining or weight optimization as in existing literature. We conduct extensive experiments to evaluate IDNN across a diverse set of NN structures on five benchmark datasets, for solving three debugging tasks, including backdoor, unfairness, and weak class. As a lightweight framework, IDNN outperforms state-of-the-art baselines by successfully identifying and isolating a very small set of responsible neurons, demonstrating superior generalization performance across all tasks.