Fully Connected Networks, also known as Dense Networks or Multi-Layer Perceptrons (MLPs), consist of layers where each neuron is connected to every neuron in the subsequent layer. The output of a neuron is typically passed through an activation function. While fundamental, their inability to capture spatial or temporal dependencies makes them less suitable for raw neural signals like EEG or fMRI, though they can be used on extracted features.

Recurrent Neural Networks (RNNs) are characterized by their feedback loops, allowing information to persist. At each time step, an RNN takes an input and its previous hidden state to produce an output and a new hidden state. This 'memory' mechanism is crucial for understanding patterns that unfold over time in neural signals. However, standard RNNs can struggle with long-term dependencies due to the vanishing gradient problem.

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are specialized types of RNNs designed to overcome the limitations of basic RNNs. They employ 'gates' (input, forget, output gates in LSTMs; update and reset gates in GRUs) that selectively allow information to pass through or be forgotten. This sophisticated control over the cell state enables them to learn and remember information over much longer sequences, which is vital for analyzing complex, extended neural activity patterns.

Convolutional Neural Networks (CNNs) are built around the concept of convolution, where small filters are applied across the input data. These filters learn to detect specific features, such as edges or textures in images, or local correlations in spatial neural activity. Pooling layers (like max-pooling or average-pooling) are often used after convolutional layers to downsample the feature maps, reducing computational complexity and providing a degree of translation invariance. This hierarchical processing allows CNNs to build representations from simple local features to complex global structures, mirroring how sensory information is processed in the brain.

Graph Neural Networks (GNNs) are designed to operate directly on graph-structured data. In neuroscience, this can represent the connectivity between brain regions (connectomics), the relationships between different types of neurons, or even the structure of protein-protein interaction networks relevant to neurological function. GNNs learn by aggregating information from a node's neighbors, allowing them to capture complex relational dependencies that are not easily represented by sequential or grid-like architectures.

Transformer networks, initially developed for natural language processing, have shown remarkable success in various sequence-to-sequence tasks, including those in neuroscience. Their core innovation is the self-attention mechanism, which allows the model to dynamically focus on different parts of the input sequence when processing each element. This capability is highly beneficial for analyzing long and complex neural time series, where important events might be separated by significant temporal distances. They can also be adapted for spatial data.