Noise-induced amplification of MEA

signal based in Stochastic Resonance

Francisco Fambrini

José Hiroki Saito

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).

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 there are circunstances in which a noise or

unpredictable fluctuations can be used

purposefully or deliberately introduced to

obtain a benefit

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 imposes limits on the rate at which error-free

Communication can be achieved. If everything

else is optimal, then Noise is the enemy.

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 • How to use a good noise so as to ensure its

operation within a solution with stochastic

resonance?

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 which the presence of noise in a nonlinear

system is better for the quality of the output

signal of his absence.

What is Stochastic Resonance ?

• This idea can be distilled into stating that

whenever SR occurs, it must be true that

Performance(noise+nonlinearity) > Performance(noise+nonlinearity) >

performance(nonlinearity).

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 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.

Fig. 1Frequency of stochastic resonance papers by

year—between 1981 and 2007—according to

the ISI database.

"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 the great interest in studying a positive role

for randomness in neural function.

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 virtually all research only considered systems

where the input SR was a combination of a

periodic input signal single frequency and

broadband noise.

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 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’’.

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 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.

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 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.

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 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/

Symmetrical double-sided potential well

Stochastic Resonance: from climate to biology

Roberto Benzi, Dipartimento di Fisica and INFN, Universit`a di

Roma ,2008.

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).

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 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

Convencional photo spectroscopy

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

Photoacoustic spectroscopy

Photoacoustic spectroscopy - 2

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 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.

Photoacoustic signals:

photospectrometry with SR

This work is based in this paper:

Huiyu Song · Xueguang Shao · Qingde Su, 2001Huiyu 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

“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 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.

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 !

Dead zone: non-linear region

Weak signal + noise in op amp

Multielectrode Array aplications of SR

• Can we perform MEA signal amplification

using SR ?

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. 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.

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 sameterminal 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.

Complete first stage: noise gen

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

Proposed circuit

Photo of the prototype

Experimental results

Senoidal pure signal

Attenuated sinusoidal signal

Noise + sinusoidal signal - 1

Noise + sinusoidal signal - 2