Skip to main content

Artificial neuron


An artificial neuron is a connection point in an artificial neural network. Artificial neural networks, like the human body's biological neural network, have a layered architecture and each network node (connection point) has the capability to process input and forward output to other nodes in the network. In both artificial and biological architectures, the nodes are called neurons and the connections are characterized by synaptic weights, which represent the significance of the connection. As new data is received and processed, the synaptic weights change and this is how machine learning occurs.

Artificial neurons are modeled after the hierarchical arrangement of neurons in biological sensory systems. In the visual system, for example, light input passes through neurons in successive layers of the retina before being passed to neurons in the thalamus of the brain and then on to neurons in the brain's visual cortex. As the neurons pass signals through an increasing number of layers, the brain progressively extracts more information until it is confident it can identify what the person is seeing. In artificial intelligence, this fine tuning process is known as deep learning.

In both artificial and biological networks, when neurons process the input they receive, they decide whether the output should be passed on to the next layer as input. The decision of whether or not to send information on is called bias and it's determined by an activation function built into the system. For example, an artificial neuron may only pass an output signal on to the next layer if its inputs (which are actually voltages) sum to a value above some particular threshold value. Because activation functions can either be linear or non-linear, neurons will often have a wide range of convergence and divergence. Divergence is the ability for one neuron to communicate with many other neurons in the network and convergence is the ability for one neuron to receive input from many other neurons in the network.

Comments

Post a Comment

Popular posts from this blog

A Graphics Processing Unit (GPU)

A graphics processing unit (GPU) is a computer chip that performs rapid mathematical calculations, primarily for the purpose of rendering images. A GPU may be found integrated with a central processing unit (CPU) on the same circuit, on a graphics card or in the motherboard of a personal computer or server. In the early days of computing, the CPU performed these calculations. As more graphics-intensive applications such as AutoCAD were developed; however, their demands put strain on the CPU and degraded performance. GPUs came about as a way to offload those tasks from CPUs, freeing up their processing power. NVIDIA, AMD, Intel and ARM are some of the major players in the GPU market. GPU vs. CPU A graphics processing unit is able to render images more quickly than a central processing unit because of its parallel processing architecture, which allows it to perform multiple calculations at the same time. A single CPU does not have this capability, although multi...
Post a Comment
Read more

What's the difference between two-step verification and 2FA?

  The two terms, two-step verification, and two-factor authentication are synonymous, though the former is now being used more widely by the likes of Google, Microsoft, and Apple as it better conveys how the actual authentication process works. In the past, two-step verification was used to describe processes that used the same authentication factors, while two-factor authentication described processes that involved different factors, such as entering a password on a website and receiving a numerical code on a mobile device. Today, the two terms are both used to describe authentication that involves a secondary factor that is different from the first. Authentication is a vital element of access control and data security because users can be assigned access rights and be authorized to perform certain actions only after successful authentication is performed. The ways in which someone can be authenticated fall into three categories based on what is known ...
Post a Comment
Read more

Black Swan Event

A black swan event is an incident that occurs randomly and unexpectedly and has wide-spread ramifications. The event is usually followed with reflection and a flawed rationalization that it was inevitable. The phrase illustrates the frailty of inductive reasoning and the danger of making sweeping generalizations from limited observations. The term came from the idea that if a man saw a thousand swans and they were all white, he might logically conclude that all swans are white. The flaw in his logic is that even when the premises are true, the conclusion can still be false. In other words, just because the man has never seen a black swan, it does not mean they do not exist. As Dutch explorers discovered in 1697, black swans are simply outliers -- rare birds, unknown to Europeans until Willem de Vlamingh and his crew visited Australia. Statistician Nassim Nicholas Taleb uses the phrase black swan as a metaphor for how humans deal with unpredictable events in his 2007...
Post a Comment
Read more