Network Architecture

flie arrangement of neuron form layers and the connection panem formed and between layers is the network architecture.

1. single-layer feed-forwar network

2. multilayer feed-forward network

3. single node with  own feedback

 4. single-layer recurrent network

 5. mulrilayer recurrent network.

1) Single layer feedforward network

• This type of network comprises two layers, namely the input layer and output layer.
• Input layer neurons receive the input signals and output layer neurons receive the output signal.
• The synaptic links carrying the weight connect every input neurons to output neurons but not vice-versa.
• This type of network is also called feedforward in type or acyclic in nature.
• The output layer alone which performs computations so is also called a single-layer network.
• The input layer sends the signals to the output layer thus the name of the feedforward network.

2) Multi-layer feed forward network

• It comprises multiple layers.
• This type of architecture besides processing an input and output layer.
• This second class of feedforward network distinguishes itself by the presence of one more hidden layer, whose computational nodes are corresponding called hidden neurons or hidden units.
• Hidden layer neurons are present between the input layer and the output layer.
• Hidden layer help in performing useful intermediary computations before directing the input to the output layer.
• Multilayer feed-forward network with L input neurons, m1 neurons in the first hidden layer,m2 neurons in the second layer and n output can be written as: L-m1-m2-n
• These networks differ from feedforward architecture in the sense that there is at least one feedback loop.
• There could also be neurons with self-feedback links, that is the output of neurons is feedback into itself as input

3. Single node with its own feedback 
 

Single Node with own Feedback
 

When outputs can be directed back as inputs to the same layer or preceding layer nodes, then it results in feedback networks. Recurrent networks are feedback networks with closed loops. The above figure shows a single recurrent network having a single neuron with feedback to itself.

4. Single-layer recurrent network 

 

The above network is a single-layer network with a feedback connection in which the processing element’s output can be directed back to itself or to another processing element or both. A recurrent neural network is a class of artificial neural networks where connections between nodes form a directed graph along a sequence. This allows it to exhibit dynamic temporal behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

5. Multilayer recurrent network 
 

In this type of network, processing element output can be directed to the processing element in the same layer and in the preceding layer forming a multilayer recurrent network. They perform the same task for every element of a sequence, with the output being dependent on the previous computations. Inputs are not needed at each time step. The main feature of a Recurrent Neural Network is its hidden state, which captures some information about a sequence.