Difference between Biological Neurons and Artificial Neurons
Biological Neurons | Artificial Neurons |
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Major components: Axions, Dendrites, Synapse | Major components: Axions, Dendrites, Synapse |
Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the cell where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapse of other neurons. | The arrangements and connections of the neurons made up the network and have three layers. The first layer is called the input layer and is the only layer exposed to external signals. The input layer transmits signals to the neurons in the next layer, which is called a hidden layer. The hidden layer extracts relevant features or patterns from the received signals. Those features or patterns that are considered important are then directed to the output layer, which is the final layer of the network. |
A synapse is able to increase or decrease the strength of the connection. This is where information is stored. | The artificial signals can be changed by weights in a manner similar to the physical changes that occur in the synapses. |
Approx 1011 neurons. | 102– 104 neurons with current technology |
Introduction to Artificial Neural Networks | Set 1
ANN learning is robust to errors in the training data and has been successfully applied for learning real-valued, discrete-valued, and vector-valued functions containing problems such as interpreting visual scenes, speech recognition, and learning robot control strategies. The study of artificial neural networks (ANNs) has been inspired in part by the observation that biological learning systems are built of very complex webs of interconnected neurons in brains. The human brain contains a densely interconnected network of approximately 10^11-10^12 neurons, each connected neuron, on average connected, to l0^4-10^5 other neurons. So on average human brain takes approximately 10^-1 to make surprisingly complex decisions.
ANN systems are motivated to capture this kind of highly parallel computation based on distributed representations. Generally, ANNs are built out of a densely interconnected set of simple units, where each unit takes a number of real-valued inputs and produces a single real-valued output. But ANNs are less motivated by biological neural systems, there are many complexities to biological neural systems that are not modeled by ANNs.