NN vs biological neurons
Types of NN

Marie-Hélène Burle

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In biological networks, the information consists of action potentials (neuron membrane rapid depolarizations) propagating through the network. In artificial ones, the information consists of tensors (multidimensional arrays) of weights and biases: each unit passes a weighted sum of an input tensor with an additional—possibly weighted—bias through an activation function before passing on the output tensor to the next layer of units.

Artificial neural networks are a series of layered units mimicking the concept of biological neurons: inputs are received by every unit of a layer, computed, then transmitted to units of the next layer. In the process of learning, experience strengthens some connections between units and weakens others.


Schematic of a biological neuron:

Schematic of an artificial neuron:

noshadow

Modified from O.C. Akgun & J. Mei 2019

While biological neurons are connected in extremely intricate patterns, artificial ones follow a layered structure. Another difference in complexity is in the number of units: the human brain has 65–90 billion neurons. ANN have much fewer units.


Neurons in mouse cortex: