Descreve o uso do fenômeno de Ressonância estocástica para amplificar sinais elétricos provenientes de Matrizes de Microeletrodos (MEA).

1. Noise-induced amplification of MEA
signal based in Stochastic Resonance
Francisco Fambrini
José Hiroki Saito

2. Stochastic Resonance
• Stochastic resonance (SR) is a phenomenon where a signal that is normally
too weak to be detected by a sensor, can be boosted by adding white
noise to the signal, which contains a wide spectrum of frequencies.
• The added white noise can be enough to be detectable by the sensor,
which can then filter it out to effectively detect the original, previously
undetectable signal.
•
Extends to many other systems, whether electromagnetic, physical or
biological, and is an area of intense research.
R. Benzi, A. Sutera and A. Vulpiani, The mechanism of stochastic resonance, J. Phys. A14, L453-L457
(1981).

3. What is Stochastic Resonance ?
• Stochastic Resonance (SR) is the name of a
phenomenon that has been studied by
physicists for more than 25 years, because
there are circunstances in which a noise or
unpredictable fluctuations can be used
purposefully or deliberately introduced to
obtain a benefit

4. What is Stochastic Resonance ?
• When the random noise in the form of
electronic fluctuations corrupts or transmitted
electromagnetic interference messages, it
imposes limits on the rate at which error-free
Communication can be achieved. If everything
else is optimal, then Noise is the enemy.

5. What is Stochastic Resonance ?
• In particular, the paradoxical notion of'' good''
noise is a double-edged sword for researchers
to SR.
• How to use a good noise so as to ensure its
operation within a solution with stochastic
resonance?

6. What is Stochastic Resonance ?
• Stochastic Resonance (SR) is a term originally
used for a specific, and is now widely applied
context to describe any phenomenon whose
which the presence of noise in a nonlinear
system is better for the quality of the output
signal of his absence.

7. What is Stochastic Resonance ?
• This idea can be distilled into stating that
whenever SR occurs, it must be true that
Performance(noise+nonlinearity) >
performance(nonlinearity).

8. What is Stochastic Resonance ?
• The term stochastic resonance was first used in
the context of noise enhanced signal processing
in 1980 by Roberto Benzi, at the International
School of Climatology, as a name for the
mechanism suggested to be behind the periodic
behavior of the Earth's ice ages. The same idea
was independently proposed. Stochastic
resonance has been used in accordance with the
ISI-Web of Science database from over 2,300
publications Fig. 1.

9. Fig. 1
Frequency of stochastic resonance papers by
year—between 1981 and 2007—according to
the ISI database.

10. "What is Stochastic Resonance ?
• About 20% of papers in SR also include a
reference in the title, abstract or keywords
with the neuron or neural words, illustrating
the great interest in studying a positive role
for randomness in neural function.

11. What is Stochastic Resonance ?
• Stochastic resonance is often described as a
phenomenon. This is mainly due to its historic
past, once since coining the term in 1980,
virtually all research only considered systems
where the input SR was a combination of a
periodic input signal single frequency and
broadband noise.

12. Stochastic resonance as ‘‘noise
benefits’’
• The term stochastic resonance is now used so
frequently in the much wider sense of being the
occurrence of any kind of noise-enhanced signal
processing, that we believe this common usage has, by
‘‘weight of numbers’’, led to are definition. Indeed,
electrical engineer Bart Kosko, who made pioneering
developments in fuzzy logic and neural networks,
concisely defines SR in his popular science book Noise
as meaning ‘‘noise benefit’’. Kosko also states the
caveat that the noise interferes with a ‘‘signal of
interest’’, and we concur that SR can be defined as a
‘‘noise benefit in a signal-processing system’’, or
alternatively ‘‘noise-enhanced signal processing’’.

13. Stochastic resonance as ‘‘noise
benefits’’
• We emphasize here is the fact that only occurs
within the context SR increase the signal, since
this is the feature that differentiates it from
make the list of phenomena that could be
described as operating some form of noise,
and still may not be all defined in terms of an
improved signal.

14. Biomedical Applications of SR
• A different form of indirect evidence for SR
existing naturally in biology is successful
biomedical applications. A particularly notable
example is the use of electrically generated
subthreshold stimuli in biomedical prosthetics
to improve human balance control and
somato sensation. This work led to James J.
Collins winning a estigious MacArthur
Fellowship in October 2003.

15. What is Stochastic Resonance? Definitions,
Misconceptions, Debates, and Its Relevance to Biology
“ Stochastic resonance is said to be observed when increases in levels
of unpredictable fluctuations—e.g., random noise—cause an
increase in a metric of the quality of signal transmission or
detection performance, rather than a decrease.
This counterintuitive effect relies on system nonlinearities and on
some parameter ranges being “suboptimal”.
Stochastic resonance has been observed, quantified, and described
in a plethora of physical and biological systems, including neurons.
Being a topic of widespread multidisciplinary interest, the definition
of stochastic resonance has evolved significantly over the last
decade or so, leading to a number of debates, misunderstandings,
and controversies. Perhaps the most important debate is whether
the brain has evolved to utilize random noise in vivo, as part of the
“neural code”. Surprisingly, this debate has been for the most part
ignored by neuroscientists, despite much indirect evidence of a
positive role for noise in the brain.”
Mark D. McDonnell1 and Derek Abbott, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660436/

16. Symmetrical double-sided potential well
Stochastic Resonance: from climate to biology
Roberto Benzi, Dipartimento di Fisica and INFN, Universit`a di
Roma ,2008.

17. Detection of weak signals
P. Hänggi, Stochastic resonance in biology - How noise can enhance detection of weak
signals and help improve biological information processing, ChemPhysChem 3, 285-290
(2002).

18. Signal-noise-ratio (SNR)
Three power spectra of spike trains recorded at
near optimal noise intensity using a neuron
model (Fitzhugh-Nagumo). Stimulated at 55
Hz, the signal features (sharp peaks at about
55 Hz) are clearly visible. It is obvious that their
amplitude changes with noise intensity with
the maximum amplitude obtained at the
optimal value of noise. The lower panel also
shows clearly that noise larger than optimum
raises the noise floor and reduces the relative
amplitude of the signal feature.
Mechanoreceptors and stochastic resonance,Dr. Lon A. Wilkens,
Biology and Center for Neurodynamics, University of Missouri-St. Louis

19. Convencional photo spectroscopy

20. noise is added in each pixel of image
Dmitry V. Dylov, Jason W. Fleischer, 2010:
Nonlinear self-filtering of noise images via dynamical stochastic resonance, Nature
Photonics, Vol.: Advance online publication, DOI: 10.1038/nphoton.2010.31

21. Photoacoustic spectroscopy

22. Photoacoustic spectroscopy - 2

23. SR in signal analysis
• A related phenomenon is dithering applied to
analog signals before analog-to-digital
conversion. Stochastic resonance can be used to
measure transmittance amplitudes below an
instrument's detection limit. If Gaussian noise is
added to a subthreshold (i.e., immeasurable)
signal, then it can be brought into a detectable
region. After detection, the noise is removed. A
fourfold improvement in the detection limit can
be obtained.

24. Photoacoustic signals:
photospectrometry with SR
This work is based in this paper:
Huiyu Song · Xueguang Shao · Qingde Su, 2001
“A study on the detection of weak photoacoustic signals by
stochastic resonance”, Fresenius J Anal Chem, 370 :1087–
1090

25. “A study on the detection of weak photoacoustic
signals”
A simulated signal was used to test the performance of adjusting μ. In the simulated signal,
A ×Sign(t) of Input(t) was s imulated from the Gaussian equation.
A =0.05 · D × Noise(t) was chosen randomly from a uniform distribution on the interval
(0.0,1.0). The length of Input(t) was 2000 points
Ordinate S and abscissa X were arbitrary units because they were simulated signals. Equation (1)
was adopted as the non-linear system μ was adjusted with other conditions unchangeable to
obtain the value which output the optimum SNR. The program was written in C++ and
implemented on an IBMP166/32 M computer. PA spectrum of real sample. The PA
spectrometer was constructed in our laboratory without a lock-in amplifier. Light from a 500
W xenon lamp was converted into monochromatic light by means of a CT-30F
monochromator and the modulated light was then incident on a CH-353 chopper. The
acoustic wave generated after illumination of the sample by light was detected by means of a
microphone (ERM-10). The output signal of the microphone was fed to a preamplifier
(Model-115). The data from the sample were collected on an A/ D converter and processed
by means of a computer.
• Erythrosin was used as the sample for the PA signal. Erythrosin spectra were normalized
against carbon black to take into account spectral variations resulting from the light source
and the spectrometer. The spectral range was 450–650 nm.

26. Convencional amplification in MEA
systems
• Operational Amplifier: non-inverter and inverter
But... SR need nonlinear device or system: the
input-output relationship must be nonlinear !

27. Dead zone: non-linear region

28. Weak signal + noise in op amp

29. Multielectrode Array aplications of SR
• Can we perform MEA signal amplification
using SR ?

30. Diode white noise generation
The first stage is noise generation, where the constant power output is produced.
We can to generate noise with the zener breakdown phenomenon that´s occur when
a zener diode is run in the reverse breakdown region of operation.
This usually occurs when approximately -1mA of current is passed through the diode.
At this current level the zener diode enters reverse breakdown and the current
through it drops rapidly while the voltage across it remains relatively constant.
This voltage level is termed zener voltage and is represented by VZ.
The I-V plot showing
this phenomenon is shown in Figure.
The noise generated while operating a
zener diode in this region is based on the
avalanche breakdown that occurs in the
pn junction.

31. Electronic white noise generator
• A Noise generator is a circuit that produces electrical noise
(random signal).
Two NPN bipolar transistors (BJT) are tied together at their bases and
connected to the same power supply. One of the BJTs is connected to
the powersupply at its collector terminal and tied to ground at the
emitter. The other BJT is connected to the power supply at its emitter
terminal and the collector terminal is floating. This essentially creates a pn junction all the same
creates a pn junction all the same as
zener diode. The next step in the generation process was to
make sure that we were operating the transistors (pn junction)
in the reverse breakdown region.

32. Complete first stage: noise gen

33. Optimal intensity of the noise
• The optimal intensity of the noise must be adjusted
as the nature of the signal to be detected changes.
Intensity of noise x Signal noise ratio
Austrian Journal of Statistics, Vol. 32 (2003), No. 1&2, 49-70

34. Proposed circuit

35. Photo of the prototype

36. Experimental results

37. Senoidal pure signal

38. Attenuated sinusoidal signal

39. Noise + sinusoidal signal - 1

40. Noise + sinusoidal signal - 2