1.Subject: your paper titled “New perspectives on the dialogue between brains and machines”From: Rostyslav SKLYAR, Dr.Ing. (r_sklyar@hotmail.com)Date: June 10, 2010To: Prof. Ferdinando (Sandro) Mussa-Ivaldi (sandro@northwestern.edu)Copy: Dr. Antonio Novellino (antonio.novellino@ettsolutions.com)Copy: Prof. Steve M. Potter (steve.potter@bme.gatech.edu)

Dear Prof. Mussa-Ivaldi,

Having read your paper titled “New perspectives on the dialogue between brains and

machines” (Frontiers in Neuroscience, May 2010, Volume 4), I discovered that the

described “bidirectional interfaces, which operate in two ways by translating neural

signals into input commands for the device and the output of the device into neural

stimuli” are based on the known principle which has been developed by me during

several years:

Sklyar R., "An EM Transistor Based Brain-Processor Interface", in: Nanotech 2009 vol. 2,

Nanotechnology 2009: Life Sciences, Medicine, Diagnostics, Bio Materials and

Composites, chapt. 3: Nano Medicine, May 3-7, 2009, in Houston, Texas, U.S.A., pp.

131 -134, www.nsti.org/procs/Nanotech2009v2/3/T82.602 ;

Sklyar R., "CNT and Organic FETs Based Two-Way Transducing of the Neurosignals", in:

Nanotech 2008 vol. 2, Nanotechnology 2008: Life Sciences, Medicine, and Bio Materials,

Nano Science & Technology Institute, Cambridge, MA, USA, CRC Press, vol. 2, chapt. 6:

Nano Medicine & Neurology, pp. 475-478,

www.nsti.org/procs/Nanotech2008v2/6/M81.404 ;

Sklyar R., “Two-way Interface for Directing the Biological Signals”, European Cells and

Materials, vol. 14, suppl. 3, 2007, page 37,

www.ecmjournal.org/journal/supplements/vol014supp03/pdf/v014supp03a037.pdf ;

Sklyar R., "Sensors with a Bioelectronic Connection", IEEE Sensors Journal (Special

Issue), vol. 7, iss. 5, 2007, pp. 835-841;

Sklyar R., “A SuFET Based Either Implantable or Non-Invasive (Bio)Transducer of Nerve

Impulses”, 13th International Symposium on Measurement and Control in Robotics-

Toward Advanced Robots: Design, Sensors, Control and Applications - ISMCR'03,

Madrid, Spain, Dec. 11-12, 2003, Inst. de Automatica Industrial, Consejo Superior de

Investigaciones Cientificas, pp. 121-126.

Especially functioning of “dynamical behavior of a neural system engaged in a two-way

interaction with an external device” and a left side of Figure 1 are copied directly from

my papers.

That is why I do consider this incident as an extremely impudent attempt to assume my

work. Also I am insisting on publication the relevant corrections/explanations.

Regards,

Rostyslav

2. Re: your paper titled “New perspectives on the dialogue between brains and machines” From: Sandro Mussa-Ivaldi (sandro@northwestern.edu) Date: June 10, 2010 To: Rostyslav SKLYAR, Dr.Ing. (r_sklyar@hotmail.com) Copy: antonio.novellino@ettsolutions.com

Dear Dr. Skylar,

thanks for sending me your reference to your papers. I have not read any of them so

far. But I may read them when I find a moment, since the titles suggest that they are

indeed on topics of mutual interest.

As for your silly and insulting suggestion that we have copied any of your ideas/text or

art, I would just point out to you that

a) all materials in the articles is original and solely our own, and

b) your papers are dated between 2003 and 2009, whereas my own work on

bidirectional interfaces has first been published as B.D. Reger, K. M. Fleming, V.

Sanguineti, S. Alford and F.A. Mussa-Ivaldi, “Connecting brains to robots: The

development of a hybrid system for the study of learning in neural tissue.” /Artificial

Life./6:307-324,/ /2000. in 2000!

That article had quite an echo in the general press. So, perhaps you may have

inadvertently lifted some of my own ideas instead. For which I would grant you my

permission.

Sincerely Sandro Mussa-Ivaldi

3.

R: your paper titled “New perspectives on the dialogue between brains and machines” From: Antonio Novellino (antonio.novellino@ettsolutions.com) Date: June 10, 2010 To: 'Rostyslav SKLYAR, Dr.Ing.' (r_sklyar@hotmail.com) Copy: 'Sandro Mussa-Ivaldi' (sandro@northwestern.edu)

Dear Dr.Skylar,

Let me thank you for your references, it's always interesting having updates from

colleagues especially when the visibility of their work is somehow hidden, I mean if your

contribute is presented in either a congress or a specific-topic book, unless you do some

advertisement, many people can miss them.

At same time I'd like to say that I've been working in the field for about ten year and

both Prof. Mussa-Ivaldi and Prof. Potter can be considered the fathers de-facto of the in-

vitro bidirectional neuronal interfaces: they started publishing in 2000 and 2001 on this

topic. On top of this the specific aim of the paper is a focused review about the research

activity of the invited group, so it's normal the focus is focused (sorry for the word

game) on their results.

What I can suggest you (personal opinion) is to submit your own review to Frontiers in

Neurorobotics, once accepted you can reach a very wide audience and better promote

your results, your research and yourself.

Looking forward to hearing from you

With kind regards

Antonio

4.RE: your paper titled “New perspectives on the dialogue between brains and machines”From: Rostyslav SKLYAR, Dr.Ing. (r_sklyar@hotmail.com)Date: June 17, 2010To: sandro@northwestern.eduCopy: antonio.novellino@ettsolutions.comCopy: steve.potter@bme.gatech.edu

First of all I don't need your permission, because my idea was disclosed a year before

your paper with the correct title: “Connecting Brains to Robots: An Artificial Body for

Studying the Computational Properties of Neural Tissues”, as a poster submission “An

SFET Based Transducer of Nerve Impulses (The Living Being-Machine Interface Scheme

as an Intelligent System's Term)” to “Shaping the Future” project of EXPO 2000 in Sept.

1999. It is necessary to emphasize that an abstract of your paper is an exact description

of my schematic. Of course, your team had time for the experimental implementation of

my method.

Secondly, can you confirm your lack of knowledge (together with Dr. Novellino) about

my papers during the last several years- do you believe this? Moreover that these

references are only the main papers and their results were widely published in the

materials of several European conferences, and web resources. Specifically, they have

been placed during the year on the same website “Frontiers in Science”:

http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=155&pap=2085&ind_abs=1&q=103 -

An EM Transistor/Memristor (EMTM) Based Brain-Processor Interface;

http://frontiersin.org/conferences/individual_abstract_listing.php?

conferid=155&pap=2051&ind_abs=1&q=100 -

Direct Imaging of the Nerve and Neuronic Signals.

Where your recent paper was published and Dr. Novellino is a review/guest editor! That

is why my papers are completely open and that you with Dr. Novellino and Prof. Potter

try to hide yourselves from reality. In any case, I was deligted with your joke- have you

any other ones? It may not be so funny, but it keeps me from having a heart attack.

Unfortunately, I am forced to reject any kind of suggestion by Dr. Novellino about

preparing my own review on this subject, because you have already published it in

general features with his support, as indicated previously. In seems to me, it was hastly

stated: «both Prof. Mussa-Ivaldi and Prof. Potter can be considered the fathers de-facto

of the in-vitro bidirectional neuronal interfaces». I would define their status as

«godfathers», according to Mario Puzo and Francis Ford Coppola.

As a result, I have concluded that the three of you are in concert. But don't console

youselves by illusions. This almost criminal story will be divulged entirely. Consequently,

you have insulted yourself and whose silly position will be made clear after analysing

these facts by the scientific community!

Regards,

Rostyslav

5.From: stevepwork@gmail.comSubject: Papers pleaseDate: June 17, 2010 12:56:50 PM GMT+03:00To: sklyar@tsp.lviv.uaReply-To: steve.potter@bme.gatech.edu

Dear Dr. Skylar,

Thanks for bringing your work to my attention. I apologise that i have not kept up the

European abstracts. So that i may properly cite your work in the future, please, if you

would, send me PDFs of all your abstracts and papers relating to this topic.

Thanks, Dr. Steve Potter

Steve.potter@bme.gatech.edu

6.R: your paper titled “New perspectives on the dialogue between brains and machines”From: Antonio Novellino (antonio.novellino@ettsolutions.com)Date: June 17, 2010To: 'Rostyslav SKLYAR, Dr.Ing.' (r_sklyar@hotmail.com); sandro@northwestern.edu

Dr. SKLYAR,

you’re getting offensive and probably you have to think twice before speaking and/or

writing so HARD SENTENCES.

YOU ARE STILL CITING JUST ABSTRACTS OF CONFERENCES. And if you look at science

fiction probably you can find even better descriptions.

If you don’t start addressing more respect to people I won’t keep discussing.

Regards

Antonio

7.Re: your paper titled “New perspectives on the dialogue between brains and machines”From: Sandro Mussa-Ivaldi (sandro@northwestern.edu)Date: June 19, 2010To: Rostyslav SKLYAR, Dr.Ing. (r_sklyar@hotmail.com)

Dear Dr. Skylar,

very briefly to your question:

> ... can you confirm your lack of knowledge (together with Dr.

> Novellino) about my papers during the last several years- do you

> believe this?

I cannot speak for Dr. Novellino, who is not a collaborator of mine. For some reason you

have decided to include him in your communications, singling him out from the editorial

group. You can see that the manuscript was edited by Dr Potter and reviewed by Drs

DeMarse and Novellino.

As for me, yes, I confirm that before your email of 6/10 I was not even aware of your

existence, let alone of your papers. As I mentioned in an earlier message, the titles of

your papers suggested to me perhaps the existence of topics of mutual interest.

However, after a closer look to a couple of them (what I could get with google) I

immediately realized that the effective overlap is minimal if at all. You work on electronic

devices. We work on brain-machine communications, using rather rudimentary

electronics.

So, the bottom line is that I was not aware of any of your work and I have no significant

interest in it. The source of my ideas was and remains a text by Valentino Braitemberg,

who introduced to me in the early 80's the concept of closed loop brain-machine

interactions. I do not owe you any of the ideas that I have published, including those of

the focused review.

Of course you are free to insist on your claims with the publishers of the journals, with

the scientific community at large and whomever you choose, except me. If our

interactions had started on a more friendly tone, maybe we could have had some useful

discussion. But at this point and on these premises, I have no interest in (or the spare

time for) continuing this discussion or having any further interaction with you.

Sincerely,

Sandro Mussa-Ivaldi

--

Ferdinando (Sandro) Mussa-Ivaldi

Professor

Department of Physiology

Department of Physical Medicine and Rehabilitation

Department of Biomedical Engineering

Northwestern University

Founder and Director, Robotics Laboratory

Rehabilitation Institute of Chicago

Tel: (312) 238 1230

Fax: (312) 238 2208

email: sandro@northwestern.edu

Web: http://www.bme.northwestern.edu/faculty_staff/core/mussa-ivaldi.html

http://www.ric.org/research/centers/smpp/labs/robotics

Frontiers in Neuroscience May 2010 | Volume 4 | Issue 1 | 44

FOCUSED REVIEWpublished: 15 May 2010

doi: 10.3389/neuro.01.008.2010

New perspectives on the dialogue between brains and machines

Ferdinando A. Mussa-Ivaldi1,2,3*, Simon T. Alford4, Michela Chiappalone1,5, Luciano Fadiga6,7, Amir Karniel1,8, Michael Kositsky1, Emma Maggiolini6, Stefano Panzeri6, Vittorio Sanguineti1,9, Marianna Semprini1,6 and Alessandro Vato1,6

1 Department of Physiology, Northwestern University, Chicago, IL, USA2 Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA3 Rehabilitation Institute of Chicago, Chicago, IL, USA4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA5 Department of Neuroscience and Brain Technologies, Italian Institute of Technology, Genova, Italy6 Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Genova, Italy7 Department of Human Physiology, University of Ferrara, Ferrara, Italy8 Department of Biomedical Engineering, Ben Gurion University, Beer Sheva, Israel9 Department of Informatics, Systems and Telematics, University of Genova, Genova, Italy

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.

Keywords: brain-machine interface, dynamical system, dynamical dimension, neural plasticity, lamprey

IntroductIonThe possibility of controlling the motion of a robotic arm “by mere thought,” as suggested by popular media since the advent of brain-machine interfaces (BMIs), has captured the imagina-tion of fiction writers and science journalists. The image of a magician displacing objects by mental powers can be entertaining. But is mind control a reasonable or even a desirable practi-cal goal for the future of neuroprosthetics? If the ultimate clinical objective is to endow amputees and paralyzed people with the ability to act natu-rally through the interaction of their brain with an artificial limb, then “controlling by thought”

is not quite an appropriate objective. The fact is that, as we carry out the simplest actions, such as operating the handle of a door, we do not occupy our minds with what we are doing. We do not think about opening up the grasp, closing it on the handle, twisting the wrist and so on. This is because motor acts are stored in the brain in hierarchically organized goal-directed actions. The addressing of a given action representation is the only thing the brain must do in order to cause the cascade of events leading to execution. In other words, our nervous systems do all that is needed without loading our thought proc-esses, apart from the explicit activation of a very

Edited by:Steve M. Potter, Georgia Institute of Technology, USA

Reviewed by:Antonio Novellino, Institute for Health and Consumer Protection – Joint Research Centre, ItalyThomas DeMarse, University of Florida, USA

*Correspondence:

Ferdinando (Sandro) Mussa-Ivaldi, has a degree (Laurea) in Physics and a Ph.D. in Biomedical Engineering. He is Professor at Northwestern University, and a Senior Research Scientist at the Rehabilitation Institute of Chicago, where he founded the Robotics Laboratory. His main research contributions are in motor system and computational neuroscience. His team created the first hybrid system, in which neural tissue from the Lamprey’s brain stem was bi-directionally interfaced with a mobile robot. Mussa-Ivaldi is also studying the mechanisms of motor remapping in a clinical context.sandro@northwestern.edu

Frontiers in Neuroscience May 2010 | Volume 4 | Issue 1 | 45

we review more recent work aimed at character-izing the dynamical behavior of a neural system engaged in a two-way interaction with an exter-nal device. This knowledge is likely to be critical, also for pursuing the goal of “programming” the operation of BMIs by gaining control on the plas-tic properties of neurons. We conclude with a new perspective on tuning the maps implemented by bidirectional interfaces so as to approximate the desired behavior of a control system expressed as a force field.

A neurAlly controlled vehIcleAlmost three decades ago, Valentino Braitenberg wrote a small manifesto in semi-fictional form (Braitenberg, 1984). He considered a family of hypothetical vehicles, endowed with various sen-sors and motor-driven wheels, in the form of mobile robots. The book narrates in entertaining but also thoughtful terms, how the electrical con-nections between sensors and wheels determine a repertoire of different responses to the stimuli in the environment. It presents two distinct view-points: one is the viewpoint of an electrical engi-neer who puts together the wiring scheme starting from a desired behavior of the vehicle; the other is the analytical viewpoint of a scientist who observes the behavior and attempts to find out how it derives from some possible “neural wiring”. The insight that we obtained from Braitenberg’s vehicles is that neural structures and properties can be established by artificially constraining the relation between neural system and behavior. This guided our group to develop an experimental approach, in which the behavior of a simple artificial device is generated by an isolated neural preparation (Reger et al., 2000; Karniel et al., 2005).

Figure 1 presents the scheme of our initial setup. The brains of sea lamprey larvae were extracted and placed in a recording chamber where they were maintained at constant physiologically relevant temperature in a Ringer’s solution. We placed two stimulation microelectrodes, one on the right and one on the left side of the midline, among the axons of the rhombencephalic vestibular pathways. We also placed two recording glass-electrodes, one on each side of the brainstem’s midline, among visually identified reticulospinal neurons of the reticular formation, which represent the final com-mand neurons to activate and maintain locomo-tion in vertebrates (Grillner et al., 2008). A simple interface decoder converted the spiking activities detected by the recording electrodes into driving signals for the corresponding wheels of a small robot (a Khepera, by K-Team). A set of optical sensors on the robot measured the light coming from the right and left side, implementing two very

general action procedure. It is only in the early stages of learning that one must be aware of the details of one’s detailed movements. Once a skill is practiced it becomes automatic and requires minimal thinking. The goal of this review is to provide a perspective that emerged from work by our group and others on how BMIs, based on the bidirectional flow of information between a neural population and a controlled device, may lead to the creation of automatic behavior. But there is more. These interactions are also a fun-damental tool for investigating how information is processed by the brain.

In the early 90s, Sharp, Abbott and Marder, introduced a new method to bridge the gap between experimental and computational analy-sis of neural behavior (Sharp et al., 1992, 1993). They established a direct dialogue between a com-puter simulation and a group of neurons in a dish. The technique is called “dynamic clamp” and is based on an exquisitely simple idea: to simulate on a computer the input/output properties of a membrane conductance by obtaining the input membrane potential from an actual neuron and injecting the output – a current – into another neuron. To derive the current from the potential, one must integrate a system of ordinary differen-tial equations; a task that can be done in real-time if the size of the system is within the available computational power. The difference between this and a more standard computer simulation is that the variables in question are exchanged between simulation and real neurons. The dynamic clamp establishes a symbiosis between the artificial computation and the biological element, or, to quote Sharp and colleagues (Sharp et al., 1993): “the dynamic clamp behaves as if the channels described by the programmed equations were located at the tip of the microelectrode.”

The concepts that led to the dynamic clamp can be extended from the cellular to the system’s level of analysis. A number of recent studies provided a similar closed-loop feedback to neural systems involved in motor task learning. In this focused review, we discuss how the physical connection between biological neural systems and artificial computational processes established by BMIs may lead to new paths for understanding neu-ral information processing and be harnessed to benefit people suffering from paralysis. We begin by describing a simple neuro-robotic system, in which a small mobile robot provides an artifi-cial body to a brain preparation maintained in a Ringer’s solution. We discuss how the analysis of the coupled behavior may provide insight on the connectivity of the neural system that transforms input stimuli into output control signals. Then,

Dialogue between brains and machines

Brain-machine interfaceHardware and software systems that enable the communication between the brain and an external device. BMI research received a strong boost from advances in micro-electrode technologies and in the decoding of neural signals. A bidirectional BMI involves translating neural signals into commands to the external device and translating signals from the device into neural stimulation.

www.frontiersin.org May 2010 | Volume 4 | Issue 1 | 46

phototaxis – a tendency to move away from the light source- was observed as well (Karniel et al., 2005) and reflected the action of ipsilateral connec-tions between vestibular and reticular neurons.

As the robot was exposed to a single source of light, it moved along rather complex and curvi-linear pathways. It was immediately evident that the neural circuitry responsible for the observed movements had properties that go beyond the structure of a simple linear feedforward network. A notable feature of this neuro-robotic interac-tion is that it allowed us to make a direct compari-son between behaviors generated by the neural preparation and behaviors generated by a com-putational model. This was possible (a) because the robotic system was a simple artificial body whose dynamics were simpler and much better known than those of any biological body, and (b) because the interactions between the robot and the neural preparation were confined to a set of well defined signals. The dynamics of the robot were captured by two first-order ordinary differ-

rudimentary “electronic eyes”. The light intensi-ties were then mapped by the interface encoder into the frequencies of two impulse generators connected to the two stimulating electrodes. This was effectively the first implementation of a bidi-rectional interface, which closed the loop from recorded neural activities to electrical stimulation via a robotic device. It was quite impressive to see the small robot responding to a shining light by movements that were most often directed toward it. This response is called “positive phototaxis” and reflects the predominance of excitatory pathways crossing the brainstem’s midline (Figure 2). This was indeed one of the first models discussed in Braitenberg’s book: if the right sensor is connected to the left wheel and vice-versa, then a light shining on one side will cause the wheel on the opposite side to spin faster. As a result, the vehicle will tend to orient itself toward the light and to proceed in the forward direction. However, positive phototaxis was not the only observed behavior of the neuro-robotic system exposed to a light source. Negative

Mussa-Ivaldi et al.

Figure 1 | Bi-directional BMIs. Left. The general scheme includes a brain model, a communication interface characterized by one coding and one decoding block, and a robotic body. Right. Implementation of the first BMI realized at Northwestern University: a hybrid neuro-robotic system connecting a lamprey’s brainstem to a small mobile robot. Signals from the optical sensors of the robot (bottom) are encoded by the communication interface into electrical stimuli, whose frequency depends linearly upon the light intensity. Stimuli are delivered by tungsten microelectrodes to the right and left vestibular pathways (top. nOMI and nOMP: intermediate and posterior octavomotor nuclei). The whole brain is immersed

in artificial cerebro-spinal fluid within a recording chamber. Glass microelectrodes record extracellular responses to the stimuli from the posterior rhombencephalic reticular nuclei (PRRN). Recorded signals from right and left PRRNs are decoded by the interface, which generates the commands to the robot’s wheels. These commands are set to be proportional to the estimated average firing rate on the corresponding side of the lamprey’s brainstem. The robot is placed in a circular arena with light sources on the periphery. The neural system between stimulation and recording electrodes determines the motions in response to each light source (modified from Mussa-Ivaldi and Miller, 2003).

1

A

SuFET

SuFET

S u F E T

S u F E T

A SuFET Based Either Implantable or Non-Invasive (Bio)Transducer ofNerve Impulses

R. V. Sklyar, Space Sensing Instruments, Verchratskogo st. 15-1, Lviv 79010, Ukraine

E-mail: r_sklyar@hotmail.com

AbstractThe main goal is to develop methods and devices forliving being- machine interaction in order to obtain inputand output signals from brain and motor nerves to theexternal devices or organs and vice versa. For thisreason an efficient and accurate method of transducingbiosignals from sense organs to output voltage, orartificial control signals to motor nerves, in limbs isdeveloped and explained in the paper. Interactionbetween living beings and automatic equipment forprocess or environmental control is also presented. Thetransducer circuits and the intelligent system are givenanalytical treatment.

1 Introduction. Biophysical signals,engineering and scientific applications

Steady and rapid progress in the robotics fieldrequires ever quicker and better human- machineinteraction and the development of a new generation ofinterfaces for intelligent systems. Such advances give riseto markedly increased biophysical research on the onehand and the need for new bioelectronic devices on theother. As a result of such efforts the design ofsynthesized neuroelectronic devices is high on theagenda.

Transduction and measurement of biosignals are keyelements of bioelectronic and biomechanic systemsdesign. There are two means involved in signaltransduction: 1) biochemical- by hormones and enzymes;2) biophysical - by nerve impulses (ionic currents). Letus consider the biophysical ones as useful for the saidcombined systems design above. There are two values -voltage and electric current which characterize thepathway of transduction.

1.1 Methods of biosignals measurement: noninvasiveand implantable, electro/magnetic- andbiosensors

Voltage potentials of the living organism and itsorgans are measured by both implantable and externalelectric field probes of high sensitivity [1]. Informationon organ activity is obtained by measuring biomagneticsignals. For such purposes a multi-channel high

temperature superconducting interference (high TcSQUID) system for magnetocardiography (MCG) andmagnetoencephalography (MEG) of humans, with highmagnetic field resolution has been developed [2, 3].

The known amperometric techniques of biosignalsinvolve the Renview bight realising method [4], and thesecond method of "biosensors typically rely on anenzyme system which catalitically convertselectrochemically non-active analytes into productswhich can be oxidized or reduced at a working electrodewhich is maintained at a specific potential with respect toa reference electrode" [5]. The same method is applicablealso to potentiometic measurements "that can measuresubstrates, inhabitors or modulators of the enzyme". TheRenview method requires extra stimulating of theisolated nerve fibre and the other method needsadditional reagents and applied voltage.

1.2 Biosignals application to sensing techniques andcontrol systems

Many sensing organs of different physical valueshave been discovered. The most recent of these was thefinding that "the antennae of jewel beetles can detectsubstances emitted in smoke from burning wood" [6].Taking this into account, the exploitation of animals andeven insects (schedulled for close attention in NASA'snear future space explorations) as "living sensors" couldbe a potential reality in the near future [7]. In that case asecure and reliable biosignals pick-up method will be ofparamount importance.

On the other hand, such living objects could producesome control signals from their nervous systems directly.The first confirmation of the finding was achieved inrecent experiments on fish, rats, monkeys and evenhumans [8,9].

The introduction of a bioelectronic mechanism fordirect limb control by artificial nerve impulse previouslyreceived (implantable or non-invasive) from the nervoussystem or synthesized will be the next logical step[10,11].

2 The transducer arrangementThe extensively developed SQUID systems do not

suit the robotic and brain-machine applications because

achievements in high temperature superconductivityverify the promising nature of the trend [25]. It is clearthat the elimination of the need for a refrigeration systemcould mean a cheaper product that could be mass-produced. Another developmental trend is related to the adopti-on the transducer to living organism conditions via theintroduction of organic superconductivity. A FET devicebased on these technologies has been studied in-depth[26]. Synthesized organic superconductors might befriendly to organisms and effective as a part of theelectronic device. Ideally, high- temperature organicsuperconductor based SuFET device seems suitable forthe above mentioned transducing technique. Increasingthe suitability of the electronic devices was mentionedabove. Implantation could be achieved by employingorganic[27], diamond [28] and CNT [29] based(superconducting) electronic devices.

3 A biotransducer based sensing andcontrol system

The above arrangement seems quite suitable for useas a living object-machine interface or as an element ofthe intelligent system. The system is based on twotechnologies. On the one hand it is based on the the

transducer and the automatic equipment that follows andon the other on the sensory system or motor nerves inlimbs of living organisms. The advanced systemprocedure is shown on Fig. 4 (upper). As a result livingbeings control drives by previously translated biosignals.In the other variant, biosignals from organs of the sensesor brain transduce directly into intelligent or roboticsystems which, in such a way, pick up environmentalinformation (Fig. 4 (lower). Both of the structures aresubjects for further refinement of all the elementsindependently from one another.

4 Conclusions The invented biotransducer has the followingfundamental improvements upon existing ones:a) the sign of the output voltage permits the determina-tion of the direction of the input current passing througha single SuFET device;b) situating the reference electrode outside the livingorganism makes precise measurement possible;c) the capability to regulate the proportion of axons thatare being investigated to the untouched ones- either thewhole cross section of the nerve fibre or any part of it;d) the possibility to substitute the SuFET device or toadjust its ratings to comply with the conditions of themeas. process without repeatedly destroying nerve fibre;e) the transducer could be either implanted or noninva-sive (like the MEG) with conversion in both directions;f) the combination of biocompatibility and tissueequivalence in both the diamond and protein-based(organic) FETs makes them naturally fit for implantation. In what areas can detected nerve impulses be applied?There are two basic applications: 1) process control and2) the connection of artificial sense organs and limbs:a) artificial limbs function by picking up a biosignal offmotor nerves and transducing it after translation toelectromechanical drives. The multiplication of final-control elements is possible after the preliminary stages;

Figure 3. Sensitivity and design merits of the biotransducers

Human

Insect

Supertransducer

SupertransducerOrgans ofthe senses

The nervoussystem

motornerves SFET based

transduser TranslatorControlled

drives

drives controlled

Artificialintelligence

Visualization

Fig. 3. The biotransducer based intelligent system

a) Human (animal, insect) - machine interface

b) A six-stages intelligent system

or

Figure 4. The (bio)transducer based intelligent system

b) lost or damaged organs of the senses could besubstituted or complemented by similarly operatinghuman, animal, etc. organs. Its output biosignals may bepicked up by the transducer and injected into nerve fibresof the recipient after reverse changing;c) substitution of inoperative control or motor nervecenters by control biosignals simulation and transducingthem to living organs as discussed above.

All in all the complete robotic system (see Appendix)consists of a living organism in a feedback relationshipwith automation execution which interact with the aid ofthe proposed transducers in order, for example, to controlsome technological processes according to the state of theexternal environment. There are two operating channels.The first of these is between the sense organs and limbs.The second is between artificial sensors and drives.Between both channels a mutual flow of informationexists by means of the explained external and implantabletransducers. It is presumed that the nerve- machine inter-face will allow the close monitoring of flow data and theadditional input of signals between exposure to physicalenvironmental stimuli and the resultant action response.

5 References[1] Ng K.T. et al., “Noise and sensitivity analysis for

miniature e- field probes”, IEEE Trans. Instrum.Meas., Vol. 30, No. 1, pp. 27-31, 1989.

[2] Itozaki H. et al., “Multi-channel high Tc SQUID”,IEICE Trans. Electron., Vol. E77-C, No. 8, pp. 1185-1190, 1994.

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1

Sensors with a Bioelectronic Connection

Rostyslav SKLYAR

Abstract: The method and devices (SuFETTrs) for design of the bioelectronic sensors has been

proposed. The method is based on combining the artificial sensors and organs of the senses with a

nerve system of living beings for receiving recalibrated output signals. The circuits consist of the

superconducting organic or solid-state field-effect transistor (SuFET) connected to a nerve fibre

by the low-ohmic or nanotubes contacts. Application of organic, chemical and carbon nanotubes

(CNT) based FETs for design of SuFETTrs is the proposed area of research. The range of picked up

signals varies from 0.6 nA to 10 µA with frequencies from 20 to 2000 Hz. The output signal lies in the

range of –5÷5V, (7÷0)⋅1017/cm3 molecules and 2÷10 pH. The placement of the SuFETTr devices

can be carried out both in vivo and in vitro with the possibility of forming the controlling signals s

from the said quantities. Interaction between sensors and bioelectronic or mechatronic system in

order to obtain input and output signals from brain and motor nerves to the external devices or organs

and vice versa for processing or environmental control is also presented in the scheme.

Keywords: sensor, biosensor, organs of the senses, a living being, SuFET, bioelectronic, mechatronic

11

which for cNW-FET varies in the range 50 to 150 nS [34] and gives the order of ST 10-7 V/√Hz. Also

for the noise voltage of parallel SuFETs based transducer is:

(EN)2 (n)=4nkTSuFET γnoise/gdn

VII. A SuFETTr based sensing and control system

Critical to all mechatronic system architectures is the role of sensors, (actuators and other

interfaces to the world within which the system exists and operates and that provide the measurement

and control functions fundamental to any mechatronic system). Sensors are integral to mechatronic

system as providers of both the process and procedural data on which operation is based [36].

Multisensor data fusion is in effect intrinsically performed by animals and human beings to

achieve a more accurate assessment of the surrounding environment. The aim of signal processing by

multisensor systems is to acquire determined information, such as a decision or the measurement of

quantity, using a selected set of measured data stemming from a multisensor system. Thereby, a big

amount of available information is managed using sophisticated signal processing for the achievement

of a high level of precision and reliability [12].

Human

Insect

Supertransducer

SupertransducerOrgans ofthe senses

The nervoussystem

motornerves SFET based

transduser TranslatorControlled

drives

drives controlled

Artificialintelligence

Visualization

Fig. 3. The biotransducer based intelligent system

a) Human (animal, insect) - machine interface

b) A six-stages intelligent system

orArtificial sensors

;

;

/ NaSmaTr

Environment

recalibration loop

brain’s signal

Translator

processes

Fig. 8 A BEleTr based intelligent system

A four-stages intelligent system

action loop

input sensor’s signal

output control signal

SuFETTr SuFETTr

Bioelectronic Transducer (BEleTr)

14

Fig. 9 SuFETTr in the bioelectronic and mechatronic system

SuFET SuFET

SuFET

SuFET

15

e) the transducer could be either implanted or noninvasive (like the MEG) with conversion in both

directions;

f) the combination of biocompatibility and tissue equivalence in both the diamond and protein-based

(organic) FETs makes them naturally fit for implantation.

All in all the complete bioelectronic and mechatronic system (Fig. 9) consists of a living

organism in a feedback relationship with automation execution which interact with the aid of the

proposed transducers in order, for example, to control some technological processes according to the

state of the external environment. There are two operating channels. The first of these is between the

sense organs and limbs. The second is between artificial sensors and drives. Between both channels a

mutual flow of information exists by means of the explained external and implantable transducers. It is

presumed that the nerve-machine interface will allow the close monitoring of flow data and the

additional input of signals between exposure to physical environmental stimuli and the resultant action

response.

The reviewed variety of FETs shows the varying extent of readiness for them to be exploited

them in SuFETTr of BSs. The most appropriate for such an application are the ordinary solid-state

SuFET modifications and novel CNT based SuFETs. The organic SuFETs are not amply developed,

but this work is being carried out in a number of directions. At the same time, the PCs, which are

necessary for the external sensor with respect to the transducing medium (solid-state conductor, nerve

fibre, flow of ions and DNA spiral), and corresponding low-ohmic wire traces for connecting PCs to

the FET’s channel are sufficiently developed, even at nano dimensions.

The preliminary calculations confirm the possibility of broadening the SuFETTr’s action from

magnetic field to the biochemical medium of BSs. The main parameters of such BSs can be gained by

applying the arrangement of the SuFETTr(s) to the whole measurement system. Two directions of

SuFETTr function enable decoding of the BS by comparing the result of its action on some process or

organ with an action on them of the simulated electrical or biochemical signal after their reverse

transducing through the SuFETTr(s). Furthermore, this decoded signal will provide a basis for creating

feedback and feedforward loops in the measuring system for more precize and complete influence on

the biochemical process.

16

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