Communication in nano devices: Electronic based biophysical model of a neuron


NANO COMMUNICATION NETWORKS, cilt.19, ss.134-147, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 19
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.nancom.2019.01.006
  • Sayfa Sayıları: ss.134-147


Investigating new strategies and signaling techniques for nano-devices and systems is quite challenging. The communication systems considered to be feasible in nano-devices are inspired from biophysical systems which communicate with electro-chemical signals organized with respect to excitation. While the electrical pulses transmitted along with the cell membrane the chemical signal transmitted in the synaptic cleft. Developing new chemical signal based communication which termed as the molecular communication with minimum error is now the central deal for the researchers. Strategic approaches to the issue in variety of perspective such as systematic, experimental and electronic circuitry viable for chip based robotic and nano-device design are now available in the literature. Biological signaling pathways, in accordance with the action potentials generated in pre-synaptic neuron some chemical substances called neurotransmitters released into the synaptic cleft and hence the post-synaptic neuron is accordingly triggered. In this way the information transmitted from one cell to another by electro chemical signal carriers. About this process some electronic neuron models have also been introduced to simulate dynamic behavior of neuronal cells. In this study, a novel simple electronic integrate and fire model which has been designed previously was further developed and used to simulate and analyze the communication of neurons. The proposed electronic model not only simulates the neuronal cell's behavior and also can transmit the information to the following neuron. The rate of correct transmission depends on the synaptic channel model. The characteristics of the used semiconductor components with overall structure of the proposed electronic model are very close to the biophysical nature of neuron and can be designed on semiconductor chips which is the advantage of the model. (C) 2019 Elsevier B.V. All rights reserved.