Tham khảo tài liệu 'crc press mechatronics handbook 2002 by laxxuss episode 3 part 12', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Neuron Cell A biological neuron is a complicated structure which receives trains of pulses on hundreds of excitatory and inhibitory inputs. Those incoming pulses are summed with different weights averaged during the time period of latent summation. If the summed value is higher than a threshold then the neuron itself is generating a pulse which is sent to neighboring neurons. Because incoming pulses are summed with time the neuron generates a pulse train with a higher frequency for higher positive excitation. In other words if the value of the summed weighted inputs is higher the neuron generates pulses more frequently. At the same time each neuron is characterized by the nonexcitability for a certain time after the firing pulse. This so-called refractory period can be more accurately described as a phenomenon where after excitation the threshold value increases to a very high value and then decreases gradually with a certain time constant. The refractory period sets soft upper limits on the frequency of the output pulse train. In the biological neuron information is sent in the form of frequency modulated pulse trains. This description of neuron action leads to a very complex neuron model which is not practical. McCulloch and Pitts 1943 show that even with a very simple neuron model it is possible to build logic and memory circuits. Furthermore these simple neurons with thresholds are usually more powerful than typical logic gates used in computers. The McCulloch-Pitts neuron model assumes that incoming and outgoing signals may have only binary values 0 and 1. If incoming signals summed through positive or negative weights have a value larger than threshold then the neuron output is set to 1. Otherwise it is set to 0. if net T if net T where T is the threshold and net value is the weighted sum of all incoming signals n net WiXi i 1 Examples of McCulloch-Pitts neurons realizing OR AND NOT and MEMORY operations are shown in Fig. . Note that the .
