Difference between biological neuron artificial neuron
The biological neuron artificial neuron are compared on the basis of the following criteria:
- Processing: basically the biological neuron can perform massive parallel operations simultaneously. The artificial neuron can also perform several parallel operations simultaneously but in general the artificial neuron network process is faster than that of the brain.
- Control mechanism: an artificial neuron is modeled using a computer whereas there is no such control unit for monitoring the brain. Thus the biological neuron artificial neuron vary in the way that the artificial neuron is very simple as compared to biological neuron.
- Tolerance: the biological neuron possesses fault tolerance capability whereas the artificial neuron has no such fault tolerance. Biological neurons can accept redundancies, which is not possible in artificial neurons.
- Storage capacity (memory): the biological neuron stores the information in its interconnections or in synapse strength but in an artificial neuron it is stored in its contiguous memory locations. In an artificial neuron, the continuous loading of new information may sometimes overload the memory locations. Thus the biological neuron artificial neuron vary in the way that the adaptability is more towards the artificial neuron
- speed: Where speed is concerned the biological neuron artificial neuron vary in the following way. The cycle time of execution in the ANN is of few nanoseconds whereas in the case of biological neurons it is of few milliseconds.
After going through the above comparison, we can say that Artificial neural networks posses the following characteristics
- It is a neutrally implemented mathematical model
- There exist a large number of highly interconnected processing elements called neurons in an ANN
- The interconnections with their weighted linkages hold the the informative knowledge.
- The processing elements of the ANN have the ability to learn, recall, and generalize from the given data by suitable assignment or adjustments of weights.
The above mentioned characteristics make the ANNs as:
Parallel distributed processing models