There are several frameworks available in C# that allow you to build neural networks. Some popular frameworks include:

1. Accord.NET: Accord.NET is a comprehensive machine learning framework that includes support for neural networks. It provides various types of neural networks, including feedforward, convolutional, and recurrent networks. Accord.NET also has support for deep learning, allowing you to train models on GPUs using TensorFlow or CNTK.

2. TensorFlow.NET: TensorFlow.NET is a .NET binding to the popular TensorFlow library. TensorFlow is one of the most widely used deep learning libraries, and TensorFlow.NET allows you to use it in C#. You can build various types of neural networks using TensorFlow.NET and train them using GPUs.

3. CNTK: The Microsoft Cognitive Toolkit (CNTK) is another deep learning framework that can be used with C#. It provides support for building, training, and evaluating deep neural networks. CNTK supports distributed training on multiple GPUs and machines, making it a good choice for large-scale deep learning projects.

4. ML.NET: ML.NET is a machine learning framework created by Microsoft. It provides a high-level API for building machine learning models, including neural networks. ML.NET is designed to be easy to use and suitable for developers who don’t have a strong background in machine learning.

These frameworks provide various levels of abstraction and are suitable for different types of projects. Accord.NET and TensorFlow.NET offer more flexibility and control over the neural network models, while ML.NET provides a simpler and more streamlined API. CNTK is a good choice for large-scale projects that require distributed training.