MOREOVER A single neuron usually has one axon, which expands off from a part of the cell body. This I called the axon hillock(#h k). The axon main purpose is to conduct electrical signals generated at the axon hillock down its length. These signals are called action potentials(动作电位). e The other end of the axon may split into several branches, which end in a pre-synaptic terminal. The electrical signals(action potential) that the neurons use to convey the information of the brain are all identical. The brain can determine which type of information is being received based on the path of the signal e Just similar to this case: I will send the some message to different medias, and the authority of each media will change the weight of what I said from the audience perspective
A single neuron usually has one axon, which expands off from a part of the cell body. This I called the axon hillock(轴丘). The axon main purpose is to conduct electrical signals generated at the axon hillock down its length. These signals are called action potentials(动作电位). The other end of the axon may split into several branches, which end in a pre-synaptic terminal. The electrical signals (action potential) that the neurons use to convey the information of the brain are all identical. The brain can determine which type of information is being received based on the path of the signal. Just similar to this case: I will send the some message to different medias, and the authority of each media will change the weight of what I said from the audience perspective
The Mathematical model Fixed input xo=± Once modeling an artificial functional model from 100—8080= bk(bias the biological neuron, we must take into account three basic components. First of all, the synapses of the biological neuron are modeled as weights. Lets n10-2wkI is the one which interconnects the neural network p remember that the synapse of the biological neuron Activation and gives the strength of the connection. For an Finction artificial neuron, the weight is a number, and 12O—用 k represents the synapse. A negative weight reflects an 卡9( 7k inhibitory connection, while positive values designate excitatory connections. The following components of the model represent the actual activity of the neuron cell. All inputs are summed altogether and modified by the weights. This activity k is referred as a linear combination. Finally, an Input Synaptic activation function controls the amplitude(ii +a )of Weights the output. For example, an acceptable range of output is usually between o and 1, or it could be-1 and l
Once modeling an artificial functional model from the biological neuron, we must take into account three basic components. First of all, the synapses of the biological neuron are modeled as weights. Let’s remember that the synapse of the biological neuron is the one which interconnects the neural network and gives the strength of the connection. For an artificial neuron, the weight is a number, and represents the synapse. A negative weight reflects an inhibitory connection, while positive values designate excitatory connections. The following components of the model represent the actual activity of the neuron cell. All inputs are summed altogether and modified by the weights. This activity is referred as a linear combination. Finally, an activation function controls the amplitude (值幅)of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be -1 and 1
Activation functions(激发函数) As mentioned previously, the activation function acts as a squashing function(压缩函数), such that the output of a neuron in a neural network is between certain values(usually o and 1, or-1 and 1). In general, there are three types of activation functions, denoted byΦ(
As mentioned previously, the activation function acts as a squashing function（压缩函数）, such that the output of a neuron in a neural network is between certain values (usually 0 and 1, or -1 and 1). In general, there are three types of activation functions, denoted by Φ(.)
Threshold function(阈值函数) First there is the Threshold 1in20 Function which takes on a 0if<0 value of o if the summed input is less than a certain threshold value(v), and the value l if the summed input is greater than or equal to the threshold value
First, there is the Threshold Function which takes on a value of 0 if the summed input is less than a certain threshold value (v), and the value 1 if the summed input is greater than or equal to the threshold value