The significant difference between artificial neural network and biological neural network is that in an artificial neural network the unique functioning memory of the system is placed separately with the processors. On the other hand, in the biological neural network the distributed memory is located inside the neural inter-links.
Neural computers are founded on the biological processes of the human neural system (brain). Neural computing involves a extensive parallel processing and self-learning machines just like a brain, which made possible by the neural network present in the brain. A neural network is nothing but a collection of processing elements interconnected with each other resembling a web, which can produce some results after receiving an input.
Furthermore, the biological neural system has the inbuilt feedback system which is not present in the artificial neural network. Traditional computing system focuses on imitating the human thought process instead of modelling how it is achieved by the human brain. A neural computer takes an alternative method in which the biological structure and mechanism of information processing of the human brain are directly modelled.
Content: Artificial Neural Network Vs Biological Neural Network
|Basis for comparison||Artificial Neural Network||Biological Neural Network|
|Processing||Sequential and centralised||Parallel and distributed|
|Rate||Artificial neural networks process information in a faster pace.||Biological neurons are slow in processing information.|
|Complexity||Incapable to perform complex pattern recognition.||The enormous size and complexity of the connections provide brain a capability of the performing complex tasks.|
|Fault tolerance||Intolerant to the failure.||Implicitly fault tolerant.|
|Control mechanism||Control unit monitors all computing-related activities.||All the processing is centrally controlled.|
Definition of Artificial Neural Network
Artificial neural network is the mathematical model, essentially inspired by the biological neuron system in the human brain. The neural network is built from the several numbers of processing elements interlinked by weighted pathways to form networks. The result of each element is computed by using a non-linear function of its weighted inputs. When these processing elements are merged into networks can employ arbitrarily complex non-linear functions such as problems regarding classification, prediction or optimization.
Similar to human brain these artificial neural networks learn by experiences, generalise by examples and can retrieve essential data from the noisy one. These can work parallelly, at a higher speed and are fault tolerant.
Definition of Biological Neural Network
The biological neural network is also made up of multiple processing elements known as neurons, which are interconnected by synapses. These neurons either accepts the external input or the outcome of the other neurons. The generated output from the various neurons propagates their effect on the whole network to the final layer where the results can be shown to the real world.
Each synapse has a processing value and weight, which is recognized at the time of the training of the network. The network’s performance and potency completely depend on the number of neurons in the network, how these are connected with each other (i.e. topology) and the value of weights assigned to each synapse.
Key Differences Between Artificial Neural Network and Biological Neural Network
- Initially, the processing in an artificial neural network was sequential and centralised. As against, in biological neural network handles information parallelly and distributively.
- The speed of processing in the artificial neural network is in the range of nanoseconds which is more than the biological neural network where the cycle time related to neural event impelled by an external stimulus occurs in few milliseconds.
- The artificial neural network is small in scale compared to the biological neural network.
- Biological neural networks can perform more complex problems relative to the artificial neural network.
- The biological neural network is fault tolerant while the artificial neural network is not.
Characteristics of the Neural Network
- A neural network can easily map the input patterns to their corresponding output patterns.
- It follows a heuristic approach of learning and learns by examples. So, these neural architectures can be trained with the help of known examples before testing on the unknown set of problems.
- It has the capability to generalize the problems, which can be used to predict the new results from past experiences.
- These systems are robust and fault tolerant and can be beneficial in recognising the absolute pattern or result from the partial or noisy patterns.
The artificial neural network is the outcome of the implementation of the biological neural network approach. The purpose behind the development of the artificial neural network is to build an expert system based on artificial intelligence.