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Unfortunately, input signal conditioning and encoding are usually not supported by such circuits, so a separate module consisting of an analog-to-digital converter, encoder, and transmitter is required. The aim of this article is to propose a sensor architecture, the output signal of which can be directly connected to the input of a spiking neural network. We demonstrate that the output signal is a valid spike source for the Izhikevich model neurons, ensuring the proper operation of a number of neurocomputational features. The advantages are clear: much lower power consumption, smaller area, and a less complex electronic circuit. The main disadvantage is that sensor characteristics somehow limit the parameters of applicable spiking neurons. The proposed architecture is illustrated by a case study involving a capacitive pressure sensor circuit, which is compatible with most of the neurocomputational properties of the Izhikevich neuron model. The sensor itself is characterized by very low power consumption: it draws only 3.49 \u03bcA at 3.3 V.<\/jats:p>","DOI":"10.1162\/neco_a_01706","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T15:14:00Z","timestamp":1724426040000},"page":"2299-2321","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Spiking Neural Network Pressure Sensor"],"prefix":"10.1162","volume":"36","author":[{"given":"Micha\u0142","family":"Markiewicz","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Jagiellonian University, 30-348 Krakow, Poland"},{"name":"Atner Sp. z o.o., 30-394 Krakow, Poland markiewicz@ii.uj.edu.pl"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ireneusz","family":"Brzozowski","sequence":"additional","affiliation":[{"name":"Institute of Electronics, AGH University of Krakow, 30-059 Krakow, Poland 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