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Data Science Community Knowledge Base

How are artificial and biological neural networks different?

A biological neural network (BNN) is a structure that consists of synapses, dendrites, cell bodies, and axons. In this neural network, the processing is carried out by neurons. Dendrites receive signals from other neurons, Soma sums all the incoming signals and the axon transmits the signals to other cells. BNNs can process highly complex parallel inputs, however, they lack any controlling mechanism and face slow processing.  

Meanwhile, an artificial neural network (ANN) is composed of artificial neurons, or nodes, that include input, output, and hidden layers. Each node connects to others, sending data to the next layer if the output of another node is above its associated weight and threshold. ANNs are trained from scratch usually using a fixed topology, although the topology of ANN can also change depending on the problem being solved. The weights of an ANN are randomly initialized and adjusted via an optimization algorithm. ANNs can learn about multiple types of data (Linear or Nonlinear). ANN is highly volatile and serves best in financial time series forecasting. However, ANN’s architecture makes it difficult to explain output.

While artificial neurons and perceptrons were inspired by biological neural networks, they do differ s in several ways:

Additional Reading About the Foundations of Neural Networks:

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