TY - JOUR N2 - A simple analog circuit is presented which can play a neuron role in static-model-based neural networks implemented in the form of an integrated circuit. Operating in a transresistance mode it is suited to cooperate with transconductance synapses. As a result, its input signal is a current which is a sum of currents coming from the synapses. Summation of the currents is realized in a node at the neuron input. The circuit has two outputs and provides a step function signal at one output and a linear function one at the other. Activation threshold of the step output can be conveniently controlled by means of a voltage. Having two outputs, the neuron is attractive to be used in networks taking advantage of fuzzy logic. It is built of only five MOS transistors, can operate with very low supply voltages, consumes a very low power when processing the input signals, and no power in the absence of input signals. Simulation as well as experimental results are shown to be in a good agreement with theoretical predictions. The presented results concern a 0.35 1m CMOS process and a prototype fabricated in the framework of Europractice. L1 - http://www.journals.pan.pl/Content/111738/PDF-MASTER/(54-4)443.pdf L2 - http://www.journals.pan.pl/Content/111738 PY - 2006 IS - No 4 EP - 448 KW - neural networks KW - learning on silicon KW - hardware intelligence KW - CMOS analog circuits KW - low-power electronics A1 - Wojtyna, R. A1 - Talaśka, T. VL - vol. 54 DA - 2006 T1 - Transresistance CMOS neuron for adaptive neural networks implemented in hardware SP - 443 UR - http://www.journals.pan.pl/dlibra/publication/edition/111738 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -