Overfitting and underfitting are two critical problems that can occur when training neural networks, leading to poor predictive performance. Understanding these concepts is vital for anyone working with machine learning or deep learning models.

Overfitting occurs when a model learns the detail and noise in the training data to an extent where it negatively impacts the model’s ability to perform on new, unseen data. It essentially means that the model has become too complex, capturing random fluctuations in the training data rather than reflecting underlying trends. The result is a highly accurate model on training data but one that performs poorly on test datasets or new real-world data.

Underfitting, on the other hand, happens when a model cannot capture relevant patterns in the dataset. This usually arises from oversimplified models that lack complexity needed to learn from data effectively. An underfitted model will perform poorly not only on unseen data but also on its training dataset because it fails even to learn adequately from this information.

The balance between overfitting and underfitting is crucial in neural network for texts is too simple (underfitted), it might not be able to solve complex tasks or make accurate predictions. Conversely, if it’s overly complex (overfitted), it may fail at generalizing what it learned from its training set to new inputs.

Several techniques can help manage overfitting and underfitting issues in neural networks. Regularization methods like L1 or L2 regularization add a penalty term to discourage complexity in models thus preventing overfitting while boosting accuracy by promoting simpler models with fewer parameters.

Another common strategy against overfitting involves using dropout layers within neural networks which randomly ignore certain units during training making our network less sensitive to specific weights hence improving generalization error.

Early stopping is another technique used where you stop your training process once your performance starts deteriorating after reaching optimal performance thereby saving resources and avoiding overtraining of your network which leads towards overfitting.

On the other hand, to prevent underfitting, one might need to increase the complexity of the model either by adding more layers or units in a layer, using more advanced types of layers like convolutional or recurrent ones, or training for a longer period.

In conclusion, striking a balance between overfitting and underfitting is crucial when working with neural networks. It’s about finding that sweet spot where your model is complex enough to learn effectively from your data but not so much that it becomes overly specialized on its training set. By understanding these concepts and knowing how to apply various techniques correctly, you can significantly enhance your models’ performance and accuracy on unseen data.