RESEARCH ARTICLE. Parameter Estimation of Neuroachfet Circuit using Advanced Algorithm for Application in Neurology

Transcription

1 ISSN Journal of Global Pharma Technology Available Online at RESEARCH ARTICLE Parameter Estimation of Neuroachfet Circuit using Advanced Algorithm for Application in Neurology Rashmi Deka*, Kuntala Boruah, Jiten Ch. Dutta Department of Electronics and Communication, Tezpur University,Napaam, India. *Corresponding Author: Rashmi Deka Abstract In 1952, Hodgkin-Huxley have developed an electronic circuit describing the biophysical nature of a neuron. Acetylcholine field effect transistor (AchFET) has been developed in this paper for detection of Acetylcholine (neurotransmitter) and then the AchFET is used in an electronic circuit to reproduce neuronal signals. AchFET is an enzyme field effect transistor (ENFET) fabricated by immobilizing acetylcholine in the gate terminal for proper detection of Acetylcholine. The neuron signals obtained from the circuit using AchFET is named as NEUROAchFET (Neuro Acetylcholine field effect transistor). In the next step, estimation of parameters is done using Firefly Algorithm for signals obtained from the NEURO AchFET. There are various parameters related to neuron signals described in this paper and estimation are done for NEUROAchFET circuit to validate the circuit. Firefly algorithm is used since it is an advanced algorithm and proved to be better than other metaheuristic algorithms. Keywords: Neuron, Acetlycholine, Hodgkin-Huxley, NeuroAchFET, Firefly Algorithm. Introduction The study of neuron has been an interesting topic since many years. Hodgkin Huxley have developed a neuron model descrbing its biophysical nature [1]. They have described the related parameters of a neuron and evaluated the values of the parameters using Voltage Clamp method [2-4]. Through this method, one parameter can be evaluated at one time. In this method, an intracellular electrode is inserted inside the neuron for ionic current and voltage measurement [5]. The voltage clamp method is not easy to use and therefore metaheuristic methods such as Firefly algorithm (FA), Genetic Algorithm (GA), Paricle Swarm Optimization (PSO) method is used nowadays [6-7]. These method searches for optimum solution and parameters can be estimated simultaneously [8-18]. Literature shows FA is the most powerful method for optimization purpose [12-20]. Action potential is propagated from one neuron to another via synapse. At the resting stage, sodium ions remain outside the cell and potassium remains mostly inside the cell. When disturbance is felt, sodium ions penetrate inside the cell causing a hump in the signal. This stage is called depolarization. Potassium ions go outside the cell in order to maintain cell neutrality. In exchange of three sodium ions, two potassium ions are exchanged. After this repolarization occurs where the cell goes to resting state again. In the process of action potential, ionic current also flows. The sodium and potassium current will be studied in this paper. In this paper, NEUROAchFET circuit is developed for reproduction of neuron signals. In NEUROAchFET circuit, AchFET (Acetylcholine field effect transistor) is used along with other electronic devices. AchFET is an enzyme field effect transistor (ENFET) which can detect acetylcholine and thus can help in propagation of action potential , JGPT. All Rights Reserved 6

2 AchFET is biocompatible and can be used in many biomedical applications. The neuron signals obtained from the NEUROAchFET are validated by estimating the parameters using FA. Model Hodgkin-Huxley (H-H) has developed an electronic circuit describing the ionic flow of sodium, potassium and leakage ion. The electronic circuit is shown in Figure 1 and the ionic flow is shown. H-H have described the model mathematically also and the ionic current is given in equation (1).The total ionic current consists of membrane capacitive current, sodium ions, potassium ions and leakage ions [4-5]. INa=gNa(V-VNa) (2) IK=gK(V-VK) (3) Il=gl(V-Vl) (4) gna= m 3 h g Na (5) gk=g n 4 K (6) The total ionic current can be represented by equation (7)[4]: I C M dv 4 g K n ( V VK ) g dt Na 3 m h( V V Na ) gl( V V ) l (7) The curve fitting equations for potassium (gk) and sodium conductance(gna) is given in equation (8) and (9) g K= {(gk ) t [g K ) t g K0 ) t exp ( t m )}4 (8) g K is the final value of potassium and gk0 is the initial value of potassium at t=0. g Na = g t Na [1 exp ( )] 3 exp ( t ) (9) m h Figure.1: Hodgkin-Huxley circuit I = C M dv dt + I ion (1) CM is the capacitive current, V is the membrane potential, I is the total ionic current and Iion consists of ionic current of potassium, sodium and leakage ions. The ionic current of each ion is shown in equation (2) to (4). CM represents the membrane capacitance of a neuron.each ionic current is represented by the following equation(2) to (4).gNa, gk and gl are the sodium, potassium and leakage conductance, VNa, VK and Vl are the equilibrium potential of sodium, potassium and leakage ions. n is the activation particles of potassium ions. m and h is the activation and inactivation particles of sodium ions and shown in equation (5) and (6). g Na is the sodium conductance which it finally attains. m and h are time constants. Neuroachfet Circuit In NEUROAchFET circuit, AchFET is used and AchFET is fabricated using enzyme sensitive field effect transistor (ENFET). In ENFET, gate terminal is modified using Acetylcholine. If acetylcholine increases, drain current increases. Acetylcholine is a neurotransmitter which helps to propagate action potential. Acetylcholine is immobilized in the gate terminal and it binds with the gate terminal using site binding theory. If voltage is applied across gate terminal, ID increases. Figure 2 shows the circuit of NEUROAchFET. Each AchFET is used for sodium conductance and potassium conductance. The first part of the circuit is for sodium and the last part is for potassium. Leakage conductance (200k) and membrane capacitance (0.0047uF) is connected in parallel to produce action potential. When VDS is increased, ID increases. The drain current is similar to currents of potassium, sodium ions in the neuron model. The conductance variance in each AchFET is also similar with neuron model of H-H. Since , JGPT. All Rights Reserved 7

3 potassium current has delayed rise, a diode is connected to form the exact curve for potassium current. This circuit when simulated in PSpice yields many important signals generated in neuron. The output of each AchFET produces conductance of each ion and when sodium, potassium and leakage conductance is combined, produces action potential. Individual current can be obtained from each NEUROAchFET similarly. Figure 2: The NEUROAchFET circui Materials and Method The parameters described in the equations (1) to (9) in the section above is estimated using Firefly algorithm[20]. The flow chart of FA is shown in Figure. 3. Figure.3: Firefly Algorithm The algorithm was applied in MATLAB to estimate the parameters related to the neuron signals [12-17].Equation (7) is taken as the fitness function. Results and Discussion MATLAB 2010a has been used to simulate the equations given in (1) to (7) to obtain action potential and ionic current. The measured signal taken is the action potential obtained by NEUROAchFET circuit. Figure 4, shows the graph to estimate values of the parameters related to action potential for the signal generated from NEUROAchFET using FA. Figure 5 and Figure.6 shows the estimation of sodium and potassium current by FA. It is been observed that FA produces results which is closer to the value of the H-H. Table 1 shows the values obtained by FA and the values given by Hodgkin-Huxley. Analyzing the graphs and Table 1, it can be observed that FA yield nearly same value as by H-H , JGPT. All Rights Reserved 8

4 Figure.4: Estimation of parameters of action potential using FA (Estimated signal). Measured signal is the obtained signal from NEUROAchFET Figure 5: Parameter extraction of sodium current using FA Figure.6: Parameter extraction of potassium current , JGPT. All Rights Reserved 9

5 Table 1: Values of the parameters obtained from FA and H-H mode Parameters Reference (H-H model) Estimated by FA for NEUROAchFET gna (m.mho/cm 2 ) gk(m.mho/cm 2 ) gl(m.mho/cm 2 ) CM(µF) 1 1 VNa(mV) VK(mV) Vl(mV) Conclusion NeuroAchFET circuit can reproduce action potential same as H-H model. The values of the parameter are estimated using FA and found to be similar with H-H model. So, the NEUROAchFET circuit can be said to be verified. FA is the most sought after method for estimation purpose. The estimation method can be used in various medical applications such as in identifying the parameter causing dysfunction in neurological signal. The relation between parameter can be identified. This circuit can be used for study and research purpose. AchFET can be used to detect Acetylcholine for neurological patients who have problem in propagation of action potential. Acknowledgment The authors like to extend their gratitude to the Tezpur University for providing facilities in the Laboratory. References 1. Lapicque L. Recherches quantitatives sur l excitation électrique des nerfs traitée comme une polarisation. J. Physiol. Pathol. Gen 1907 Jan 9;9(1): Cole KS, Hodgkin AL (1939) Membrane and protoplasm resistance in the squid giant axon. The Journal of general physiology, 22(5): Lewis ER (1966) Neuroelectric potentials derived from an extended version of the Hodgkin-Huxley model. Journal of theoretical biology, 1;10(1):125IN IN Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4): Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biological cybernetics, 1;52(3): Vavoulis DV, Straub VA, Aston JA, Feng J (2012) A self-organizing state-space-model approach for parameter estimation in Hodgkin- Huxley-type models of single neurons. PLoS Comput Biol 1;8(3):e parameter estimation of GnRH neurons. Biosystems, 100(3): Buhry L, Pace M, Saïghi S (2012) Global parameter estimation of an Hodgkin Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits. Neurocomputing, 1;81: Cedersund G, Samuelsson O, Ball G, Tegnér J, Gomez-Cabrero D (2016) Optimization in biology parameter estimation and the associated optimization problem. InUncertainty in Biology, Springer International Publishing. 10. Buhry L, Saighi S, Giremus A, Grivel E, Renaud S (2009) Automated tuning of analog neuromimetic integrated circuits. In2009 IEEE Biomedical Circuits and Systems Conference, IEEE. 11. Buhry L, Saighi S, Giremus A, Grivel E, Renaud S (2008) Parameter estimation of the Hodgkin-Huxley model using metaheuristics: application to neuromimetic analog integrated circuits. In2008 IEEE Biomedical Circuits and Systems Conferenc, IEEE. 7. Csercsik D, Farkas I, Szederkényi G, 12. Willms AR, Baro DJ, Harris-Warrick RM, Hrabovszky E, Liposits Z, Hangos KM (2010) Guckenheimer J (1999) An improved Hodgkin Huxley type modelling and parameter estimation method for Hodgkin , JGPT. All Rights Reserved 10

6 Huxley models. Journal of Computational Neuroscience, 6(2): Saïghi S, Buhry L, Bornat Y, N'Kaoua G, Tomas J, Renaud S (2008) Adjusting the neurons models in neuromimetic ICs using the voltage-clamp technique. In 2008 IEEE International Symposium on Circuits and Systems, IEEE. 14. Buhry L, Grassia F, Giremus A, Grivel E, Renaud S, Saïghi S (2011) Automated parameter estimation of the Hodgkin-Huxley model using the differential evolution algorithm: application to neuromimetic analog integrated circuits. Neural computation, 23(10): Ditlevsen S and Samson A (2016) Parameter estimation in neuronal stochastic differential equation models from intracellular recordings of membrane potentials in single neurons: a Review. Journal de la Société Française de Statistique, 157(1): Barik MA, Dutta JC(2014) Fabrication and characterization of junctionless carbon nanotube field effect transistor for cholesterol detection. Applied Physics Letters, 105: Kim M, Kinnon DM, Carthy TM, Rosat B and Kinnon DM (2015)Regulatory Evolution and Voltage-Gated Ion Channel Expression in Squid Axon: Selection Mutation Balance and Fitness Cliffs. PLOS One, 10(4) 18. Jelescu IO, Veraart J, Fieremans E, Novikov DS (2016) Degeneracy in model parameter estimation for multi compartmental diffusion in neuronal tissue. NMR in Biomedicine,1;29(1): Yang XS, He X (2013) Firefly algorithm: recent advances and applications. International Journal of Swarm Intelligence, 1(1): , JGPT. All Rights Reserved 11