Given infinite compute resources; can a traditional feed-forward neural network model a human brain?
The human brain is a highly complex and sophisticated organ that scientists are still trying to fully understand. While feed-forward neural networks have proven to be highly effective in many domains, it is unlikely that a traditional feed-forward neural network, even with infinite compute resources, would be able to fully model the complexity of the human brain.

One reason for this is that the human brain consists of many different types of neurons and other specialized cells, which interact with each other in highly complex ways. In contrast, traditional feed-forward neural networks typically consist of relatively simple artificial neurons that communicate with each other in a much more limited way.

Additionally, the human brain also has many other structures and features that are not present in traditional feed-forward neural networks, such as the intricate network of white matter that connects different regions of the brain, the various neurotransmitters and other chemicals that are used for communication within the brain, and the various feedback and regulatory mechanisms that help to maintain the brain's homeostasis.

Therefore, while it is possible that a feed-forward neural network with infinite compute resources might be able to approximate some aspects of the human brain's behavior, it is unlikely to be able to fully capture the complexity and sophistication of the human brain.
Are there any different types of neutral network's that can model a human brain?
There are various types of neural networks that have been proposed to model different aspects of the human brain. For example, recurrent neural networks (RNNs) can capture the temporal dynamics of sequences of inputs, and have been used to model language processing and speech recognition. Convolutional neural networks (CNNs) have been used to model the visual cortex and image recognition, while hierarchical temporal memory (HTM) networks have been proposed as a biologically inspired model of cortical columns.

However, it's worth noting that while these models may capture some aspects of brain function, they are still limited in their ability to fully capture the complexity and diversity of the human brain. Much more research is needed to better understand the brain and develop more sophisticated models that can fully capture its behavior.