Brain exercises control over other organs of the body. This complex control process, is completed in steps which involves commands issued to the muscles to execute movements, receipt of feedback from sensors reporting the actual state of the musculature & skeletal elements, and inputs from the senses to monitor progress towards the goal. This is made possible by neurons through communicating with each other. Neurons are cells specialised for transmitting and receiving information. Exchange of information between neurons is either in the form of chemical or electrical signals. Electrically information is conveyed in the form of neural electrical signal know as action potential. Hodgkin and Huxley conducted Voltage Clamped experiments to study mechanism for generation and propagation of action potential in giant Axon of Squid and proposed a simple mathematical model known as HH model. Since information is conveyed through time and frequency of action potential, dynamic range becomes an important criterion for analysis. This paper analyse the dynamic range of the HH model through simulation, for its ability to convey information within and between neurons for effective control by brain over other organs.

1. Eugene M. Izhikevich," Dynamic Systems in Neuroscience- the Geometry of Excitability and Bursting", MIT Press Landon, 2007.
2. Stephen M. Stahl, "Stahl's Essential Psychopharmacology: Neuroscientific Basis and Practical Applications", Third Edition. Cambridge University Press 978-0-521-8570 2-4.
3. Ryan Siciliano, "The Hodgkin Huxley model its extensions, analysis and numerics". 06Mar 2012.
4. Keith S Elmslie, " Action Potential: Ionic Mechanisms" Penn State College of Medicine, Hershey, Pennsylvania, USA February 2010.
5. Chudler, Eric H. "Milestones in Neuroscience Research". Neuroscience for Kids. Retrieved 2009-06-20.
6. Rob Kass, 'The Hodgkin-Huxley Model:A Quick Historical Overview", Department of Statistics and The center for the Neural Basis of Cognition Carnegie Mellon University.
7. A. L. Hodgkin and A. F. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve", J. Physiol. 1952; 117; 500-544.
8. Bertil Hille "Ionic channels of excitable membranes", (2nd ed) Sunderland, Mass. Sinauer Associates, 1992.
9. Claym. Armstrong, "Voltage-Dependent Ion Channels and Their Gating", Physiological Reviews Vol. 72, No. 4 (Suppl. ), October 1992 U. S. A.
10. Alain Destexhe and John R. Huguenard "Modeling voltage-dependent channels" 2007 UNIC, CNRS, 91198 Gif-sur-Yvette, France.
11. Clay M. Armstrong Voltage-Dependent Ion Channels and Their Gating Physiological Reviews Vol. 72, No. 4 (Suppl. ), October 1992 Department of Physiology, University of Pennsylvania, Philadelphia, Pennsylvania.
12. D. Sterratt, B. Graham, A. Gillies and D. Willshaw, "Principles of computational modelling in neuroscience", Cambridge University Press, New York, 2011.
13. Idam Seger, Chiristof Koch, Method in Neuronal Modeling (From ion to Network), MIT Press, Landon, 1998.
14. Pascal Wallisch,Michacl Lusignan, Marc Benayoun, Tanya I Baker, Adam S Dickey, Nicholas G Hatsopoulos, "Matlab for Neuroscientists (An introduction to scientific Computig in Matlab)",Academic Press USA, 2009.
15. Fred Ricke, David Warland, Rob de Ruyler Van Steveninck and William Bialek "Spikes- Exploring the neural code", MIT press Landon, 1999.

Neuron Information Action Potential Voltage Clamp Dynamic Range.