does multisim 12 handle transistors and mosfets

In biological systems, however, neurons do not use continuous signals but rather pulses to process information. In a traditional artificial neuron, information is transmitted in the form of continuous signals.

To reproduce brain behaviors, artificial neural networks (ANNs) have been proposed and used in many ways, e.g., to identify failures in transmission lines and for diagnostic imaging in the biomedical field. Moreover, the human brain is a parallelized system with a certain amount of fault tolerance capability. Nearly all human activities are controlled by the brain, which is a low-power-consumption system (roughly 10 W ) offering high-speed processing of large amounts of data. Finally, to test the SNN constructed with SETs and memristor synapses, an associative memory learning process, including memory construction, loss, reconstruction, and change, is implemented in the circuit using the PSPICE simulator. In addition, we modify the memristor synapse to achieve better weight control. Here, a novel window function is used in the memristor model to improve the linearity of the memristor and solve the boundary and terminal lock problems. A prominent feature of SETs is Coulomb oscillation, which has characteristics similar to the pulses produced by spiking neurons. In this regard, the use of SSN circuits based on single-electron transistors (SETs) and modified memristor synapses is proposed herein. Due to its complex structure, the simplification of SNN circuits requires serious consideration, along with their power consumption and space occupation. In recent decades, with the rapid development of artificial intelligence technologies and bionic engineering, the spiking neural network (SNN), inspired by biological neural systems, has become one of the most promising research topics, enjoying numerous applications in various fields.

Does multisim 12 handle transistors and mosfets