1. Subject: your paper titled “New perspectives on the dialogue between brains and machines” From: Rostyslav SKLYAR, Dr.Ing. (email@example.com) Date: June 10, 2010 To: Prof. Ferdinando (Sandro) Mussa-Ivaldi (firstname.lastname@example.org) Copy: Dr. Antonio Novellino (email@example.com) Copy: Prof. Steve M. Potter (firstname.lastname@example.org) 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
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
www.nsti.org/procs/Nanotech2008v2/6/M81.404 ; Sklyar R., “Two-way Interface for Directing the Biological Signals”, European Cells and Materials,
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 RoboticsToward 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 (email@example.com) Date: June 10, 2010 To: Rostyslav SKLYAR, Dr.Ing. (firstname.lastname@example.org) Copy: email@example.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
3. R: your paper titled “New perspectives on the dialogue between brains and machines” From: Antonio Novellino (firstname.lastname@example.org) Date: June 10, 2010 To: 'Rostyslav SKLYAR, Dr.Ing.' (email@example.com) Copy: 'Sandro Mussa-Ivaldi' (firstname.lastname@example.org) 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 invitro 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. (email@example.com) Date: June 17, 2010 To: firstname.lastname@example.org Copy: email@example.com Copy: firstname.lastname@example.org 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: email@example.com Subject: Papers please Date: June 17, 2010 12:56:50 PM GMT+03:00 To: firstname.lastname@example.org Reply-To: email@example.com 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.firstname.lastname@example.org
6. R: your paper titled “New perspectives on the dialogue between brains and machines” From: Antonio Novellino (email@example.com) Date: June 17, 2010 To: 'Rostyslav SKLYAR, Dr.Ing.' (firstname.lastname@example.org); email@example.com 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 (firstname.lastname@example.org) Date: June 19, 2010 To: Rostyslav SKLYAR, Dr.Ing. (email@example.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: firstname.lastname@example.org Web: http://www.bme.northwestern.edu/faculty_staff/core/mussa-ivaldi.html http://www.ric.org/research/centers/smpp/labs/robotics
published: 15 May 2010 doi: 10.3389/neuro.01.008.2010
New perspectives on the dialogue between brains and machines Ferdinando A. Mussa-Ivaldi 1,2,3*, Simon T. Alford 4, Michela Chiappalone1,5, Luciano Fadiga 6,7, Amir Karniel 1,8, Michael Kositsky1, Emma Maggiolini 6, Stefano Panzeri 6, Vittorio Sanguineti1,9, Marianna Semprini 1,6 and Alessandro Vato1,6 Department of Physiology, Northwestern University, Chicago, IL, USA Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA 3 Rehabilitation Institute of Chicago, Chicago, IL, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA 5 Department of Neuroscience and Brain Technologies, Italian Institute of Technology, Genova, Italy 6 Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Genova, Italy 7 Department of Human Physiology, University of Ferrara, Ferrara, Italy 8 Department of Biomedical Engineering, Ben Gurion University, Beer Sheva, Israel 9 Department of Informatics, Systems and Telematics, University of Genova, Genova, Italy 1 2
Edited by: Steve M. Potter, Georgia Institute of Technology, USA Reviewed by: Antonio Novellino, Institute for Health and Consumer Protection – Joint Research Centre, Italy Thomas DeMarse, University of Florida, USA *Correspondence:
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
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. email@example.com
Frontiers in Neuroscience
Introduction The 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 imagination 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 practical goal for the future of neuroprosthetics? If the ultimate clinical objective is to endow amputees and paralyzed people with the ability to act naturally 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 processes, apart from the explicit activation of a very
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Dialogue between brains and machines
Brain-machine interface Hardware 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.
Frontiers in Neuroscience
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 fundamental 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 analysis of neural behavior (Sharp et al., 1992, 1993). They established a direct dialogue between a computer 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 differential 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 neural 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 artificial 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,
we review more recent work aimed at characterizing the dynamical behavior of a neural system engaged in a two-way interaction with an external device. This knowledge is likely to be critical, also for pursuing the goal of “programming” the operation of BMIs by gaining control on the plastic 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 vehicle Almost 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 sensors and motor-driven wheels, in the form of mobile robots. The book narrates in entertaining but also thoughtful terms, how the electrical connections between sensors and wheels determine a repertoire of different responses to the stimuli in the environment. It presents two distinct viewpoints: one is the viewpoint of an electrical engineer 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 command neurons to activate and maintain locomotion 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
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Mussa-Ivaldi et al.
rudimentary “electronic eyes”. The light intensities 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 bidirectional 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 neurorobotic system exposed to a light source. Negative
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
phototaxis – a tendency to move away from the light source- was observed as well (Karniel et al., 2005) and reflected the action of ipsilateral connections between vestibular and reticular neurons. As the robot was exposed to a single source of light, it moved along rather complex and curvilinear 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 interaction is that it allowed us to make a direct comparison between behaviors generated by the neural preparation and behaviors generated by a computational 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-
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).
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An SFET Based Transducer of Nerve Impulses
S u F E T
S u F E T
(The Living Being-Machine Interface Scheme as an Intelligent System's Term)
“Shaping the Future” project of EXPO 2000, submitted Sept. 1999 by Rostyslav SKLYAR
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 A SuFET Based Either Implantable or Non-Invasive (Bio)Transducer of Nerve Impulses R. V. Sklyar, Space Sensing Instruments, Verchratskogo st. 15-1, Lviv 79010, Ukraine E-mail: firstname.lastname@example.org
Abstract The main goal is to develop methods and devices for living being- machine interaction in order to obtain input and output signals from brain and motor nerves to the external devices or organs and vice versa. For this reason an efficient and accurate method of transducing biosignals from sense organs to output voltage, or artificial control signals to motor nerves, in limbs is developed and explained in the paper. Interaction between living beings and automatic equipment for process or environmental control is also presented. The transducer circuits and the intelligent system are given analytical treatment.
1 Introduction. Biophysical signals, engineering and scientific applications Steady and rapid progress in the robotics field requires ever quicker and better human- machine interaction and the development of a new generation of interfaces for intelligent systems. Such advances give rise to markedly increased biophysical research on the one hand and the need for new bioelectronic devices on the other. As a result of such efforts the design of synthesized neuroelectronic devices is high on the agenda. Transduction and measurement of biosignals are key elements of bioelectronic and biomechanic systems design. There are two means involved in signal transduction: 1) biochemical- by hormones and enzymes; 2) biophysical - by nerve impulses (ionic currents). Let us consider the biophysical ones as useful for the said combined systems design above. There are two values voltage and electric current which characterize the pathway of transduction. 1.1 Methods of biosignals measurement: noninvasive and implantable, electro/magneticand biosensors Voltage potentials of the living organism and its organs are measured by both implantable and external electric field probes of high sensitivity . Information on organ activity is obtained by measuring biomagnetic signals. For such purposes a multi-channel high
temperature superconducting interference (high Tc SQUID) system for magnetocardiography (MCG) and magnetoencephalography (MEG) of humans, with high magnetic field resolution has been developed [2, 3]. The known amperometric techniques of biosignals involve the Renview bight realising method , and the second method of "biosensors typically rely on an enzyme system which catalitically converts electrochemically non-active analytes into products which can be oxidized or reduced at a working electrode which is maintained at a specific potential with respect to a reference electrode" . The same method is applicable also to potentiometic measurements "that can measure substrates, inhabitors or modulators of the enzyme". The Renview method requires extra stimulating of the isolated nerve fibre and the other method needs additional reagents and applied voltage. 1.2 Biosignals application to sensing techniques and control systems Many sensing organs of different physical values have been discovered. The most recent of these was the finding that "the antennae of jewel beetles can detect substances emitted in smoke from burning wood" . Taking this into account, the exploitation of animals and even insects (schedulled for close attention in NASA's near future space explorations) as "living sensors" could be a potential reality in the near future . In that case a secure and reliable biosignals pick-up method will be of paramount importance. On the other hand, such living objects could produce some control signals from their nervous systems directly. The first confirmation of the finding was achieved in recent experiments on fish, rats, monkeys and even humans [8,9]. The introduction of a bioelectronic mechanism for direct limb control by artificial nerve impulse previously received (implantable or non-invasive) from the nervous system or synthesized will be the next logical step [10,11].
2 The transducer arrangement The extensively developed SQUID systems do not suit the robotic and brain-machine applications because
Figure 3. Sensitivity and design merits of the biotransducers achievements in high temperature superconductivity verify the promising nature of the trend . It is clear that the elimination of the need for a refrigeration system could mean a cheaper product that could be massproduced. Another developmental trend is related to the adoption the transducer to living organism conditions via the introduction of organic superconductivity. A FET device based on these technologies has been studied in-depth . Synthesized organic superconductors might be friendly to organisms and effective as a part of the electronic device. Ideally, high- temperature organic superconductor based SuFET device seems suitable for the above mentioned transducing technique. Increasing the suitability of the electronic devices was mentioned above. Implantation could be achieved by employing organic, diamond  and CNT  based (superconducting) electronic devices.
3 A biotransducer based sensing and control system The above arrangement seems quite suitable for use as a living object-machine interface or as an element of the intelligent system. The system is based on two technologies. On the one hand it is based on the the Supertransducer Human The nervous system motor nerves
SFET based transduser
a) Human (animal, insect) - machine interface
Organs of the senses
Visualization Artificial intelligence
b) A six-stages intelligent system
Figure 4. The (bio)transducer based intelligent system Fig. 3. The biotransducer based intelligent system
transducer and the automatic equipment that follows and on the other on the sensory system or motor nerves in limbs of living organisms. The advanced system procedure is shown on Fig. 4 (upper). As a result living beings control drives by previously translated biosignals. In the other variant, biosignals from organs of the senses or brain transduce directly into intelligent or robotic systems which, in such a way, pick up environmental information (Fig. 4 (lower). Both of the structures are subjects for further refinement of all the elements independently from one another.
4 Conclusions The invented biotransducer has the following fundamental improvements upon existing ones: a) the sign of the output voltage permits the determination of the direction of the input current passing through a single SuFET device; b) situating the reference electrode outside the living organism makes precise measurement possible; c) the capability to regulate the proportion of axons that are being investigated to the untouched ones- either the whole cross section of the nerve fibre or any part of it; d) the possibility to substitute the SuFET device or to adjust its ratings to comply with the conditions of the meas. process without repeatedly destroying nerve fibre; 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. In what areas can detected nerve impulses be applied? There are two basic applications: 1) process control and 2) the connection of artificial sense organs and limbs: a) artificial limbs function by picking up a biosignal off motor nerves and transducing it after translation to electromechanical drives. The multiplication of finalcontrol elements is possible after the preliminary stages;
b) lost or damaged organs of the senses could be substituted or complemented by similarly operating human, animal, etc. organs. Its output biosignals may be picked up by the transducer and injected into nerve fibres of the recipient after reverse changing; c) substitution of inoperative control or motor nerve centers by control biosignals simulation and transducing them to living organs as discussed above. All in all the complete robotic system (see Appendix) 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.
5 References  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.  Itozaki H. et al., “Multi-channel high Tc SQUID”, IEICE Trans. Electron., Vol. E77-C, No. 8, pp. 11851190, 1994.  Lounasmaa O.V. et al., “SQUID technology and braine research”, Physica B, No. 197, pp. 54-63, 1994.  Tasaki I., “Nervous transmission”, Charles C Thomas Publ., Springfield IL USA, 1953.  Sharma A. and Rogers K.R., “Biosensors”, Meas. Sci. Technol., No. 5, pp. 461-472, 1994.  Schütz S. et al., “Insect antenna as a smoke detector”, Nature, Vol. 398, No. 6725, pp. 298-299, 1999.  David L., “Beastly explorers”, New Scientist, Vol. 161, No. 2168, p. 32, 1999.  Miguel A. L., Nicolelis M.A.L. and Chapin J.K., “Controlling robots with the mind”, ScientificAmerican, October 2002.  DeMarse T.B., Wagenaar D.A. and Potter S.M., “The neurally- controlled artificial animal: a neuralcomputer interface between cultured neural networks and a robotic body”, Society for Neuroscience Abstracts 28: 347.1, 2002.  Aliaga J. et al., “Electronic neuron within a ganglion of a leech (Hirudo medicinalis)”, Phys. Rev. E, Vol. 67, 061915, 2003.  Miguel A. L., Nicolelis M.A.L., “Brain–machine interfaces to restore motor function and probe neural circuits”, Nature Reviews Neuroscience, Vol. 4, No. 5, pp. 417-422, 2003.  Prance R.J., Clark T.D. and Prance H., “Compact
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IEEE Sensors Journal (Special Issue), vol. 7, iss. 5, 2007, pp. 835-841 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
which for cNW-FET varies in the range 50 to 150 nS  and gives the order of ST 10-7 V/√Hz. Also for the noise voltage of parallel SuFETs based transducer is: (EN ) (n)=4nkTSuFET γnoise/gdn 2
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 . 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 .
input sensor’s Human signal
output control signal The nervous system
motor nerves Insect
SFET based transduser
Bioelectronic Transducer (BEleTr)
a) Human (animal, insect) - machine interface
Supertransducer Organs of the senses
; action loop
b) A six-stages intelligent system
four-stages intelligent system Fig. 3.AThe biotransducer based intelligent system Fig. 8 A BEleTr based intelligent system
S u F E T
S u F E T
Fig. 9 SuFETTr in the bioelectronic and mechatronic system 14
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 FETâ€™s channel are sufficiently developed, even at nano dimensions. The preliminary calculations confirm the possibility of broadening the SuFETTrâ€™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.
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