Artificial neural network (ANN) is a simulation of biological neural network with some mathematical functions. However, it might be **better if ANN be simulated closer to the real biological structure and mechanism**.

Current ANN designs come with input nodes, middle layers and output layers. Each layer is connected to previous layer and next layer. This is multi-layer ANN. A good suggestion should be using the real meaning of the word network and create a **graph-based ANN instead of layer-based, eg. recurrent neural network is kinda graph than layered**. Graph-based ANN allows **circulation in the network during training**, not only feed-forwarding.

Biological neuron soma is supposed to be much more complex than the mathematical artificial neuron with only regression function and activation function. **Should an artificial neuron soma come with a full complex programme instead of just only 2 functions: regression and activation?**

Biological neuron comes with **multiple axon terminals which carry similar outputs instead of exactly the same output** as current ANN designs.

**Some types of neural networks**:

- Feedforward dense multi-layer neural network (basic and simple neural network with feedforwarding and backpropagation)
- Convolutional neural network (CNN, simulate visual cortex) for image recognition
- Recurrent neural network (RNN), time delay neural network (TDNN) for speed recognition
- Radial basis function network (2 layer neural network)
- Evolutionary neural network (the network which changes neuron connections by itself)
- Modular neural network (just like the brain, each different part of the brain is a neural network by itself)
- Biological simulated neural network (the neural network with features simulating closely to biological neurons and parts of brain)

**More details about types of neural networks**:

https://en.wikipedia.org/wiki/Types_of_artificial_neural_networks

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