Demonstration of Synaptic Connections with Unipolar Junction Transistor based Neuron Emulators

Document Type: Research Note


1 Department of Biomedical Engineering, Tekirdag Namik Kemal University, Tekirdag, Turkey

2 Department of Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey

3 Electromagnetic Research Center, Sakarya University, Sakarya, Turkey

4 Department of Electronics and Telecommunication Engineering, Tekirdag Namik Kemal University, Tekirdag, Turkey


Neuron emulator circuits can be used for teaching and proving concepts.  Such emulators should be made with cheap and off-the-shelf components.  There are bipolar and MOSFET transistor-based neuron emulator circuits heavily used in literature. Opamp-based neuron emulators are also commonly used. Such circuits provide simple and cheap solution instead of using microcontroller-based neuron emulators if many neurons are to be used in the studies such as showing circadian resonance. Unipolar junction transistor (UJT) is commonly used in industrial electronics applications. It provides a cheap timing circuit. Although there are a few UJT-based artificial neuron patents, we were unable to find research articles on UJT-based artificial neurons. In this study, we examined a simple network of UJT-based artificial neurons and show their spiking and bursting behavior with synaptic connections between neurons. It is shown that the firing rate of a UJT-neuron can be increased by utilizing spikes generated by another one with simulations. This behavior represents excitatory connectivity between two neurons.


1.     Hodgkin, A.L. and Huxley, A.F., "A quantitative description of membrane current and its application to conduction and excitation in nerve", The Journal of Physiology,  Vol. 117, No. 4, (1952), 500. doi: 10.1113/jphysiol.1952.sp004764
2.     FitzHugh, R., "Mathematical models of threshold phenomena in the nerve membrane", The Bulletin of Mathematical Biophysics,  Vol. 17, No. 4, (1955), 257-278.
3.     Izhikevich, E.M., "Which model to use for cortical spiking neurons?", IEEE Transactions on Neural Networks,  Vol. 15, No. 5, (2004), 1063-1070. doi: 10.1109/TNN.2004.832719
4.     Izhikevich, E.M. and Hoppensteadt, F., "Classification of bursting mappings", International Journal of Bifurcation and Chaos,  Vol. 14, No. 11, (2004), 3847-3854. doi: 10.1142/S0218127404011739
5.     Nagumo, J., Arimoto, S. and Yoshizawa, S., "An active pulse transmission line simulating nerve axon", Proceedings of the IRE,  Vol. 50, No. 10, (1962), 2061-2070. doi: 10.1109/JRPROC.1962.288235
6.     FitzHugh, R., "Impulses and physiological states in theoretical models of nerve membrane", Biophysical Journal,  Vol. 1, No. 6, (1961), 445. doi: 10.1016/s0006-3495(61)86902-6
7.     Gonzales, O.A., Han, G., De Gyvez, J.P. and Sánchez-Sinencio, E., "Lorenz-based chaotic cryptosystem: A monolithic implementation", IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications,  Vol. 47, No. 8, (2000), 1243-1247. doi: 10.1109/81.873879
8.     Linares-Barranco, B., Sánchez-Sinencio, E., Rodríguez-Vázquez, A. and Huertas, J.L., "A cmos implementation of fitzhugh-nagumo neuron model", IEEE Journal of Solid-State Circuits,  Vol. 26, No. 7, (1991), 956-965. doi: 10.1109/4.92015
9.     Rajasekar, S., Murali, K. and Lakshmanan, M., "Control of chaos by nonfeedback methods in a simple electronic circuit system and the fitzhugh-nagumo equation", Chaos, Solitons & Fractals,  Vol. 8, No. 9, (1997), 1545-1558. doi: 10.1016/S0960-0779(96)00154-3
10.   Tamaševičiūtė, E. and Mykolaitis, G., "Analogue modelling an array of the fitzhugh–nagumo oscillators", Nonlinear Analysis: Modelling and Control,  Vol. 17, No. 1, (2012), 118-125. doi: 10.15388/NA.17.1.14082
11.   Zhao, J. and Kim, Y.-B., "Circuit implementation of fitzhugh-nagumo neuron model using field programmable analog arrays", in 2007 50th Midwest Symposium on Circuits and Systems, IEEE. (2007), 772-775. doi: 10.1109/MWSCAS.2007.4488691
12.   Petrovas, A., Lisauskas, S. and Slepikas, A., "Electronic model of fitzhugh-nagumo neuron", Elektronika Ir Elektrotechnika,  Vol. 122, No. 6, (2012), 117-120. doi: 10.5755/j01.eee.122.6.1835
13.   Tamaševičius, A., Bumelienė, S., Mykolaitis, G., Tamaševičiūtė, E. and Kirvaitis, R., "Desynchronization of mean–field coupled oscillators by remote virtual grounding", in 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems (NDES’2010).–Dresden, Germany. (2010), 30-33.
14.   Putzrath, F.L., Processing apparatus utilizing simulated neurons. 1965, Google Patents.
15.   Askew, W.J., Unijunction transistor artificial neuron. 1972, Google Patents.
16.   Tağluk, M.E., "A new dynamic electronic model of neuron’s membrane", Anatolian Science-Bilgisayar Bilimleri Dergisi,  Vol. 3, No. 1, (2018), 1-6.
17.   Tagluk, M.E. and Isik, I., "Communication in nano devices: Electronic based biophysical model of a neuron", Nano Communication Networks,  Vol. 19, (2019), 134-147. doi: 10.1016/j.nancom.2019.01.006
18.   Chaturvedi, D., Malik, O. and Kalra, P., "Studies with a generalized neuron based pss on a multi-machine power system",  (2004).
19.   Boylestad, R.L. and Nashelsky, L., "Electronic devices and circuit theory, Prentice Hall,  (2012).
20.   Chitode, J., "Power electronics, Technical publications,  (2009).