A neuronal network unit, comprising: an inhibitory read control transistor (T21) and an excitatory read control transistor (T22) which are provided in parallel, and a write control transistor (T1) provided to be opposite the two transistors (T21, T22) in a stacked manner longitudinally; the source electrode of the write control transistor (T1) and the source electrodes of the read control transistors (T21, T22) are arranged facing away from each other; the drain electrode of the write control transistor (T1) is electrically connected to a first end of a magnetic tunnel junction (MTJ); the drain electrode of the inhibitory read control transistor (T21) is electrically connected to a second end of the magnetic tunnel junction (MTJ) by means of a weight signal line (W); and the drain electrode of the excitatory read control transistor (T22) is electrically connected to the second end of the magnetic tunnel junction (MTJ) by means of the weight signal line (W). By arranging the transistors of the neuronal network unit in a vertically stacked manner, the total area of a chip is reduced, and the two transistors share a magnetic tunnel junction (MTJ), so that in cases where an excitation signal and a suppression signal are opposite, the levels are completely consistent and are counteracted, improving the accuracy of a deep learning algorithm.
NEURONAL NETWORK UNIT