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Computational Neuroethology Laboratory


Re: CCLI proposal to NSF

Title: Computational Neuroethology: Curriculum development and Laboratory enhancement


The course outlined in this proposal has passed through MSU’s approval process. The main reason for this is a course conception around April 1 and an NSF proposal deadline of May 9th, which left the PI with little time to pursue both NSF proposal writing and MSU approval. All materials necessary for course approval and subsequent R certification will be submitted shortly after May 1st. The portions of the proposal relevant for the course topic and expected outcomes are included below.


Sincerely,


Alexander Dimitrov

Computational Neuroethology Laboratory


To address these concerns, and to further the goals of the Hughes Biology Curriculum Initiative and MSU’s CORE 2.0, the PI proposes to create a laboratory research course in Computational Neuroethology and Biomimetics. The overarching goal of the course will be to study simple biological systems and simulate key behaviors and structures in a basic robotics system. The course will be used as a practical hands-on introduction to modeling and simulations for biology majors and as a introduction to multidisciplinary collaborative research for non-biology (physics, engineering, math) majors. Simulation in biomimetic robotics was selected as a tools because of its material aspects, which allow a gentler introduction to modeling and simulations than a completely abstract implementation in a purely computational environment. In addition, robots are subject to similar physical constraints as many organisms, which will allow the students to better appreciate the constraints that basic physics and chemistry impose on biological systems.

While presenting students with true research experience, biological simulations with biomimetic robots will strengthen the use of quantitative tools in appropriate biological settings. Students will need to actually apply principles of engineering, computer science, physics and mathematics to solve problems in the simulations, rather than learn them in an abstract and dissociated manner. The introduction of an actual physical entity on which these simulations can be performed is a key aspect of this educational process.

The main equipment to be used is the Lego MINDSTORMS NXT kit, which includes a microcontroller, sensors, actuators and mechanical elements with which the projects will be performed. The choice of this system was driven by its mechanical versatility and an easy to use programming environment, which nevertheless is a powerful engineering platform. For example, the programming platform is a superset of LabView® (National Instruments), a widely used engineering programming environment. This will allow the students to concentrate on the biomimetic aspects of the project and not on fine engineering details. Students will nevertheless learn basing engineering, computational and modeling concepts as a part of their research. The PI has had had several years of experience working with middle school students Dr. Dimitrov had several years of experience working with middle school students with an earlier version of the MindStorms NXT system. It served as a very intuitive introduction to principles of engineering and programming, since it allowed hands-on experience and physical feedback, which typically are absent from introductory programming courses, often making them too abstract for students to apply in their other activities. The enhanced MindStorms NXT system provides the right level of functionality, realism and abstraction to be just the right tool for the job. The students taking the course will also have a different set of goals compared to the middle school students: instead of concentrating on basic programming and design, they will use the kits for biomimetic robotic models.

NXT is compatible with two independent programming platforms: Robolab and Mindstorms Education. Both packages are similar in capability, based on Labview®, and both will be available to the students. In development stages of the course, students will be encouraged to use Robolab, because of its more mature state. The PI is collaborating with Robolab’s developers at Tufts University and will have access to the latest technologies from their development stages (see attached letter of support). (***tufts) are interested in adding modules developed in the Computational Neuroethology lab to the Robolab distribution, or as separate modules.

The proposed class format is not easily amenable to standard textbooks. A more appropriate format will be a small class library, with several key books used for direction and study of current techniques, and access to the primary literature in Neuroethology and Biomimetic robots as a springboard to independent research. The following books will be used as primary references:


  • Vehicles: Experiments in Synthetic Psychology. Valentino Braitenberg. The MIT Press (Bradford Book), Cambridge, Massachusetts (1984)

  • Biological Neural Networks in Invertebrate Neuroethology and Robotics. Edited by R.D. Beer, R. E. Rizhmann and T. McKenna. Academic Press, New York (1993).

  • Neurotechnology for Biomimetic Robots. Edited by J. Ayers, J. L. Davis, and A. Rudolph. The MIT Press (Bradford Book), Cambridge, Massachusetts, 2002

  • Sensors and Sensing in Biology and Engineering. Edited by F. G. Barth, J.A.C Humphrey and T.W. Secomb. Springer-Verlag, Wien New York, 2003


Several copies of the above books will be acquired with funds from the proposal. The lab library will be complemented with classic and review papers in Neuroethology, Computational Neuroethology and Biomimetics. A short non-inclusive list follows. Part of the pre-course development will be spent compiling a more thorough list of articles, with the help of colleagues at MSU and collaborators in Tufts University.


Altendorfer, A., N. Moore, H. Komsuoglu, M. Buehler, H.B. Brown Jr., D. McMordie, U. Saranli, R. Full, D.E. Koditschek. (2001) RHex: A Biologically Inspired Hexapod Runner, Journal of Autonomous Robots 11, 207-213.

Blanchard, M., Rind, F. C. & Verschure, P. F. M. J. (2000) Collision avoidance using a model of the locust LGMD neuron. Robot. Auton. Syst. 30, 17–38

Brooks, R. A. (1991), New Approaches to Robotics, Science 253, 1227–1232.

Chiel, H.J. and Beer, R.D. (1991). Simulation of adaptive behavior. Current Opinion in Neurobiology 1(4): 605-609.

Cliff, D.T. (1991), Computational neuroethology: a provisional manifesto, in From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, J. A. Meyer and S. W. Wilson, Eds. MIT Press (Bradford Books), Cambridge, MA

Delcomyn, F. and Nelson, M. (2000), Architectures for a biomimetic hexapod robot, Robotics and Autonomous Systems 30 5–15

Dickinson, M.H. Farley, C.T., Full, R.J., Koehl, M. A. R., Kram R., and Lehman, S. (2000). How animals move: An integrative view. Science 288, 100-106.

Ezrachi, E., Levi, R., Camhi, J. & Parnas, H. (1999) Right-left discrimination in a biologically oriented model of the cockroach escape system. Biol. Cybern. 81, 89–99.

Horridge, G. A., Loughet-Higgins, H. C. & Horridge, G. A. (1992) What can engineers learn from insect vision? Phil. Trans. R. Soc. Lond. B 337, 271–282.

Lambrinos, D., Moller, R., Labhart, T., Pfeifer, R. & Wehner, R. (2000) A mobile robot employing insect strategies for navigation. Robot. Auton. Syst. 30, 39–64

Quinn, R.D., Ritzmann, R.E. (1998) Construction of a hexapod robot with cockroach kinematics benefits both robotics and biology. Connect. Sci. ^ 10, 239–254.

Webb, B (2002) Robots in invertebrate neuroscience. Nature 417, 359-363 (review)


In addition, students will be made aware of journals with relevant topics, like Journal of Experimental Biology, Robotics and Autonomous Systems, Current Opinion in Neurobiology, Trends in Neuroscience, Journal of Comparative Neurobiology, Biological Cybernetics, International Journal of Robotics Research, and others. All these journals are currently electronically accessible through the MSU Library.


^

Proposed schedule of classes


The course will consist of 2 parts. In a 15 week semester, the first 3 weeks will be devoted to introduction to the concepts of Computational Neuroethology and the NXT programming environment. Students will be divided in small groups of 2 to 3 and work with individual NXT kits. The second part of the course will start by different groups choosing between selected simple neuroethological systems and modeling key structures and behaviors in the NXT robotics system.
^

Introductory period


The students will work with instructor’s help on the tutorial of Robolab (one of NXT’s development environments). This tutorial is part of the software and provides a good overview of the capabilities of the programming environment. The introduction provides instructions for a standard robot model, which is simple to build while providing a demonstration of structural designs possible with the mechanical side of the system.

After the introduction to the robotics kits, students will attempt an independent project with the same materials, but without the guidance of a tutorial. All will be assigned the same project: construct and program “vehicles” from the first 3 chapters of Braitenberg’s seminal book (cite). In his book from over 20 years ago, Braitenberg proposes many thought experiments on what he calls “Synthetic Psychology”, these days better described as Computational Neuroethology. This will be the first connection between the robotics kit and concepts of animal behavior and its neural basis. The connections are made almost perfectly in Braitenberg’s book, so his ideas will be used without much change as instructions for the projects. The first 3 chapters cover relatively simple concepts while being extremely motivational and far-reaching in their scope.

(maybe a brief overview of Braitenberg’s book, it’s old)
^

Research period


After the introductory period it is assumed that students are capable of rudimentary use of the robotics system. The remainder of the course will consist of implementing the existing knowledge into models of biological origin. In order to gradually increase the difficulty level of involvement, the research period will be divided in two parts as well. In the first part, students will study and implement simple reflex behaviors. Biological model systems there will include mostly reflexes: student’s own myotatic reflex, crayfish tailflip, teleost C-start, and various other invertebrate withdrawal reflexes. At this stage, basic concepts in modeling and engineering will be introduced. Students will prepare simple simulations of nerve cells, and model interactions between them to simulate the mechanics of basic reflexes. Alternatively, they may approach the problems from system-wide viewpoint, using basic engineering techniques derived from control theory to achieve similar behaviors. In both cases, programming techniques like preemptive multitasking and modern robotic techniques (subsumptive (?) architecture) will be used to implement the models.

The second stage will consist of implementing more complex behaviors based on the tools developed in the first stage. Several animal models seem very appropriate for that task: taxis (bacterial chemotaxis, insect phonotaxis), navigation (ant, bee navigation) and communication (ant, bee communication). (why are this behaviors important).

Of course, the NXT robotics system does not have many of the elaborate sensors present in real biological systems. This shortcoming will be addressed in two ways. For the first several years, students will use standard NXT sensors or 3rd party NXT-compatible sensors as they appear on the market. Some of the physical signals key for the studied behaviors will be replaced with similar physical signals that can be detected and acted upon by the NXT sensors. As an example, phonotaxis, biological movement guided by sound, is completely feasible through the use of NXT sound sensors. Chemotaxis on the other hand involves sensing and recognizing certain chemicals, which is currently beyond the capabilities of the NXT system, and is actually an active research endeavor in the scientific community (cite Kauer & White?). In lieu of that, a different physical signal can be used. Consider chemotaxis and chemical communication in ants. Their communication signals are non-volatile heavy chemicals that, once deposited, diffuse very slowly. A similar effect can be achieved by depositing color trails through a highlighter attached to the robot. Multiple applications will increase the saturation of the hue, which is analogous to increased concentration of a chemical. This trail however can be easily sensed by the NXT light sensor, and acted upon. Other such substitutions will be discussed and researched in class when necessary.

An alternative approach will be to design different sensors which can process the modality which triggers a particular behavior. This is beyond the scope of the Computational Neuroethology lab; however MSU has a strong engineering school with interest in electronic design and robotics. At this stage of development the PI will explore options of collaborating with engineering design classes already in place in the curriculum. One needs to establish whether it is feasible for the two classes to work on a common project, whereas students in the Computational Neuroethology lab study and describe biological sensors that are key for certain behaviors, and students in the engineering design class design, test and produce NXT-compatible sensors with similar capabilities. As the biomimetic lab is still in development, the PI has not established formal contacts with engineering faculty; however informal inquiries have so far produced a very enthusiastic reaction.

Expected outcomes

Educational outcomes


Students will

  • Gain acquaintance with scientific research in the field of Neuroethology

  • Attain skills in modeling of biological systems through computational and physical models,

  • Synthesize techniques from physics, statistics, engineering and programming through in biological context,

  • Gain experience working in a collaborative multidisciplinary environment.

  • Increase appreciation of physical constraints to animal organs and behaviors; topic not heavily covered in regular curriculum




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