According to foreign media reports, scientists have long dreamed of creating computers like the brain. The brain is more energy-efficient than a computer, and it also learns independently, without any programming. Dr. Andy Thomas, a senior lecturer from the Department of Physics at the University of Bielefeld, is making efforts in this regard. His research material is a memristor, a microelectronic component that can mimic nerve functions. Thomas and his colleagues produced a memristor with learning ability a year ago. Now, Andy Thomas uses this memristor as a key component of the artificial brain, and his research results will be published in the Journal of Physical Sciences D: Applied Physics recently.
Memristors are made of extremely thin nano films and can be used to connect electronic circuits. For many years, memristors have been regarded as electronic versions of synapses. Synapses are bridges between neurons. The more neurons are active, the more synaptic connections there are. Usually, one neuron communicates with other neurons through thousands of synapses. The memristor can receive pulses from the electronic circuit, and after turning off the power, it can still "memorize" the amount of charge previously passed. Therefore, the amount of current allowed by the memristor depends on the intensity of the current and the time it passed.
Andy Thomas explained that because of the similarity between the memristor and the synapse, it makes it an excellent material for making artificial brains-and thus a new generation of computers-"It allows us to build extremely energy-efficient , Durable, and self-learning processor. "Thomas ’s article summarizes his experimental results and draws on the results of other biological and physics studies. For the first time, he explains how this neural-like computer can transform natural phenomena into technology. The system, and several principles that should be followed. These principles include that the memristor should “pay attention†to the previous electronic pulse like a synapse; and only when the stimulation pulse exceeds a certain amount, the neuron will respond, as will the memristor.
It is precisely because of this characteristic that nerve synapses can play a key role in the learning process of the brain. Andy Thomas took Pavlov's classic physiology experiment as an example to illustrate the learning process. If the dog sees food, a natural reaction of drooling will occur; if the dog hears a ringtone every time it sees food, then the "neutral" stimulus of the ringtone will be linked to the stimulus that causes the reflex reaction. Therefore, even if the dog does not see the food at the end, he will drool when he hears the bell. The physiological reason for this conditioned reflex is that there are more synaptic connections between the neurons that make reflex reactions and the neurons that trigger these reactions.
If a neutral bell stimulus and food stimulus appear at the same time, the dog's brain will begin to learn, and the neurons responsible for these two stimuli will establish more connections. "You can also use a memristor to build a similar electronic circuit, which is the first step to a neuromimetic processor." Andy Thomas said, "This is entirely possible, because compared to the original based on The bit computer processor, memristor is more accurate for storing information. "Both the memristor and the bit use electronic pulses to work, but the latter cannot make any minor adjustments, but can only be turned on and" on. " turn off". In contrast, memristors can continue to increase or decrease resistance. Thomas explained: "This is also the basis of how the memristor works in the process of learning and forgetting by the artificial brain."
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