Research Article A Neuron Model Based Ultralow Current Sensor System for Bioapplications A. K. M. Arifuzzman, Mohammad Shafquatul Islam, and Mohammad Rafiqul Haider Department of Electrical and Computer Engineering, Te University of Alabama at Birmingham, Birmingham, AL 35294, USA Correspondence should be addressed to Mohammad Rafqul Haider; mrhaider@uab.edu Received 30 July 2015; Revised 26 January 2016; Accepted 27 January 2016 Academic Editor: Jian-Nong Cao Copyright © 2016 A. K. M. Arifuzzman et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An ultralow current sensor system based on the Izhikevich neuron model is presented in this paper. Te Izhikevich neuron model has been used for its superior computational efciency and greater biological plausibility over other well-known neuron spiking models. Of the many biological neuron spiking features, regular spiking, chattering, and neostriatal spiny projection spiking have been reproduced by adjusting the parameters associated with the model at hand. Tis paper also presents a modifed interpretation of the regular spiking feature in which the fring pattern is similar to that of the regular spiking but with improved dynamic range ofering. Te sensor current ranges between 2 pA and 8 nA and exhibits linearity in the range of 0.9665 to 0.9989 for diferent spiking features. Te efcacy of the sensor system in detecting low amount of current along with its high linearity attribute makes it very suitable for biomedical applications. 1. Introduction In light of the successful strides achieved in biomedical tech- nology in the last couple of decades, the 21st century is expe- riencing intensifed demands in ultralow current biosensors as it has become increasingly apparent that ultralow current sensors play a critical role in many bioapplications, especially those aimed at biosensing systems. Te ultralow current sensors are frequently used in areas such as clinical diagnosis, genome research, drug development [1], surveying systems, metabolite activity monitoring [2], and bioelectrochemical sensors [3]. State-of-the-art biosensors ofer numerous advantages in the form of high selectivity, high sensitivity, large dynamic range, lower response time, simple calibration techniques, feld reconfgurability, reproducibility, stability, low power consumption, and low manufacturability cost. A neuron’s capability of sensing changes in environment is exception- ally explicit and exquisitely sensitive [4], doing such with fying initial response times, which are usually as short as milliseconds. A lot of efort has already been put into the advancement of spike based neuron sensor applica- tions such as tactile sensing [5], biomolecular detections [6], and capacitive biosensor [7]. Due to the compensa- tions mentioned above an ultralow current detection sensor based on neural spiking model has been introduced in this paper. Te neuron spiking model is based on the Izhikevich neuron model which ofers similar biological plausibility as the Hodgkin-Huxley model but with superior computational efciency as the integrate-and-fre model. Te mathematical model of the Izhikevich neuron model is given in [8]. A crucial advantage of this model is that one can easily generate diferent spiking features of a neuron by varying only a hand- ful number of parameters, as we have produced four diferent spiking features: regular spiking, chattering, neostriatal spiny projection, and modifed regular spiking. Among them the modifed regular spiking is analogous to the regular spiking feature but with improved range of input sensing current. A notable observation here is that the spiking features of this particular neuron model are always triggered at ultralow current, usually in the picoampere (pA) to nanoampere (nA) Hindawi Publishing Corporation Journal of Sensors Volume 2016, Article ID 9437129, 11 pages http://dx.doi.org/10.1155/2016/9437129