Comment on "signal-to-noise ratio gain in neuronal systems"

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volumn 67, Issue 4 1, 2003, Pages 439011-439016

  Khovanov, Igor A ^a,b   McClintock, Peter V E ^b  

^a SARATOV STATE UNIVERSITY   (Russian Federation)

^b LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; ELECTRIC POTENTIAL; HARMONIC ANALYSIS; MATHEMATICAL MODELS; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; STATISTICAL METHODS;

HARMONIC SIGNALS;

NONLINEAR SYSTEMS;

BIOLOGICAL MODEL; NERVE CELL; PATHOLOGY; SYSTEMS THEORY; THEORETICAL MODEL; TIME FACTOR;

MODELS, NEUROLOGICAL; MODELS, THEORETICAL; NEURONS; SYSTEMS THEORY; TIME FACTORS;

EID: 37649031254     PISSN: 1063651X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (16)

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11. The SNR for the pulse signal in Refs. [6,7] (like that in Ref. [1]) is defined, by analogy with the conventional definition used for a harmonic signal, as the logarithm of the ratio between the intensity of the a peak in the power spectrum at the pulse repetition frequency to the intensity of the noisy background at the same frequency.
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14. As an additional check of the robustness of our SNR calculation, we replaced the Hodgkin-Huxley model by an over-damped linear oscillator, and calculated the input and output SNR for several noise intensities. We obtained an equality of the input and output SNR values to an accuracy better than 0.3 dB.
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