{"id":12336,"date":"2017-11-17T14:16:49","date_gmt":"2017-11-17T16:16:49","guid":{"rendered":"http:\/\/www.institutosantosdumont.org.br\/?page_id=12336"},"modified":"2025-02-26T09:46:30","modified_gmt":"2025-02-26T12:46:30","slug":"matriz-curricular-neurogengenharia","status":"publish","type":"page","link":"https:\/\/institutosantosdumont.org.br\/en\/matriz-curricular-neurogengenharia\/","title":{"rendered":"Curriculum matrix and syllabi"},"content":{"rendered":"
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The curriculum matrix of Postgraduate Program in Neuroengineering<\/strong><\/a> of I<\/a>Santos Dumont Institute (ISD)\u00a0<\/strong>consists of mandatory and optional disciplines aimed at training a versatile professional in\u00a0interface between engineering and neuroscience<\/strong>.\u00a0This structure was designed to overcome existing barriers between exact, biological and human graduations.\u00a0<\/span><\/p>

Check below the disciplines and their respective syllabi, which also\u00a0are available at\u00a0<\/span>Sucupira Platform\u00a0HERE<\/a><\/span><\/span>\u00a0<\/span>(<\/span>Select: Higher Education Institution: 33280010 \u2013 Alberto Santos Dumont Teaching and Research Institute\/ Program: Neuroengenharia \u2013 33280010001P2<\/em>).<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\"\"<\/a>Compulsory disciplines<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tNeuroanatomy<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

Description:<\/b><\/p>

The discipline of neuroanatomy covers the anatomical, structural and functional aspects of the nervous system, with emphasis on the study of the central (brain and spinal cord) and peripheral nervous systems. The anatomy of the nervous system will be addressed both at the cellular and systems level (somatosensory, visual, auditory, olfactory, gustatory, motor, vestibular, limbic and autonomic, memory and learning). In this way, the general objective of the discipline is to characterize the nervous system, in all its components, in terms of systems and their macro and micro anatomical structures in a theoretical and practical way, consisting of lectures, discussion of texts, carrying out activities practices and presentation of seminars related to the topics discussed.<\/p>

Thematic content<\/b><\/p>

  1. Principles and fundamentals of functional neuroanatomy
    \u2022 Brain \u2022 Spinal cord \u2022 Peripheral nerves<\/i><\/li>
  2. Cell anatomy: morphological, physiological aspects and functional interaction<\/li>
  3. somatosensory system<\/li>
  4. visual system<\/li>
  5. auditorium system<\/li>
  6. Olfactory and gustatory systems<\/li>
  7. engine system<\/li>
  8. vestibular system<\/li>
  9. limbic system<\/li>
  10. memory and learning<\/li>
  11. autonomous system<\/li><\/ol>
    \u00a0<\/div>
    Workload: <\/b>60 hours\u00a0<\/div>
    Total Credits:<\/b> 4<\/div>
    \u00a0<\/div>
    Offering: <\/b>Annual - 1st Semester

    Bibliography:\u00a0<\/b>\u2022 Afifi A, Bergmann R. Functional Neuroanatomy \u2013 Text and atlas. 2nd ed. S\u00e3o Paulo: Roca, 2007. 526 p. \u2022 Bear M, Connors B, Paradiso M. Neuroscience \u2013 Uncovering the Nervous System. 3rd ed. S\u00e3o Paulo: Artmed, 2008. 896 p. \u2022 Purves D, Augustine GJ, Fitzpatrick D, Hall WC, LaMantia AS, White LE. Neuroscience. 5th ed. Sinauer, 2012. 759 p. Complementary bibliography: \u2022 Lent, R. One hundred billion neurons? \u2013 Fundamental concepts of neuroscience. 2nd ed. S\u00e3o Paulo: Atheneu, 2010. 765 p.<\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tNeurophysiology<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

    Description:<\/b><\/p>

    The discipline's main objective is to characterize the physiological and functional bases of the nervous system. It will consist of lectures, discussion of texts and questions raised by teachers.<\/p>

    Thematic content:<\/b><\/p>

    1. Neuron cytology and ion channels<\/li>
    2. Conceptual bases of electrophysiology
      \u2022 Membrane potential \u2022 Bioelectrogenesis \u2022 Neural signal propagation \u2022 Synapse and synaptic integration \u2022 Neurotransmitters <\/i><\/li>
    3. Sensory Processing
      \u2022 Sensory transduction and encoding \u2022 Stimulus modalities \u2022 Sensory systems: touch, pain, vision, hearing, taste and smell \u2022 Cortical representation of the senses <\/i><\/li>
    4. Planning, execution and motor control
      \u2022 Muscle contraction \u2022 Reflex, voluntary and autonomous movements \u2022 Planning and execution of movements \u2022 Learning and motor control \u2022 Major motor disorders <\/i><\/li>
    5. Autonomic nervous system, homeostasis and hypothalamus<\/li>
    6. Neurophysiological bases of emotions; limbic system<\/li>
    7. Neural bases of cognition
      \u2022 Attention \u2022 Memory and learning \u2022 Executive control<\/i><\/li><\/ol>
      \u00a0<\/i><\/div>
      Workload: <\/b>60 hours\u00a0<\/div>
      Total Credits: <\/b>4<\/div>
      Offering: <\/b>Annual - 1st Semester

      Bibliography: <\/strong>\u2022 Kandel, E., Schwartz, J., Jessell, T. Principles of Neural Science. McGrall-Hill, 2000. \u2022 Bear, M., Connors, B., Paradiso, M. Neuroscience \u2013 Exploring the brain. Lippincott, 2006. \u2022 Purves, D., Augustine, GJ, Fitzpatrick, D., Katz, LC, LaMantia, AS, McNamara, JO, Williams, SM Neuroscience. Sinauer, 2007.<\/div><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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      \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tFundamentals of Programming and Project Development Applied to Neuroengineering<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

      Description:<\/strong><\/p>

      Algorithm definition and introduction to computer programming. git. Building a collaborative environment. Handling input and output data. Conditional structures. Repeating structures. Vectors. Pointers. Functions. Function Overloading. Class. Heritage. Polymorphism. Operator overload. Execution of projects associated with Neuroengineering.

      <\/b><\/p>

      Skills:<\/b><\/p>

      1. Domain of Neuroscience and Biomedical Engineering \u2013 Apply engineering knowledge in neuroscience;<\/li>
      2. Scientific Domain \u2013 Research and carry out experiments with scientific rigor to solve problems, seeking innovation.<\/li>
      3. Creation and Development of Applied Systems \u2013 Design, develop and implement computational systems aiming at the integration of physical and logical resources.<\/li>
      4. Teaching Skills \u2013 Develop pedagogical mastery associated with knowledge of neuroengineering.<\/li>
      5. Teamwork \u2013 Develop the ability to work in a collaborative environment.<\/li>
      6. Society Transformation \u2013 Using the acquired knowledge to cause social transformations.<\/li>
      7. Ethical Training \u2013 Develop professional activities based on ethical principles.<\/li><\/ol>

        \u00a0<\/b><\/p>

        Learning Objectives:
        <\/b>Develop programming logic through a structured and object-oriented programming language. \u2013 Conceptualize variable, decision structure, repetition structure and vector in the context of algorithms. \u2013 Implement simple algorithms using a programming language. \u2013 Abstract dynamic data structures into a computational memory. \u2013 Build systems applicable to neuroengineering. \u2013 Identify the most appropriate framework for solving neuroengineering problems.<\/p>

        Thematic content:<\/b><\/p>

        1. basic computer architecture<\/li>
        2. Hardware elements (memory, processor, input\/output devices)<\/li>
        3. Numbering systems and number bases<\/li>
        4. Basic organization of a computational environment (Hardware, Operating System, compilers, programs)<\/li>
        5. Introduction to programming<\/li>
        6. Algorithms<\/li>
        7. Basic aspects of programming languages<\/li>
        8. Neuroengineering projects<\/li>
        9. Project Management Tools<\/li>
        10. Git: Workflow in a collaborative environment.<\/li>
        11. Programming language<\/li>
        12. Constants, Variables, and Assignments<\/li>
        13. Data input\/output and arithmetic operators<\/li>
        14. Logical expressions and conditional statements<\/li>
        15. Repeating structures \u2022 Data structures, vectors, and matrices<\/li>
        16. Modularization of programs (procedures and functions)<\/li>
        17. Records<\/li>
        18. pointers<\/li>
        19. file manipulation<\/li>
        20. Function Overloading<\/li>
        21. Class<\/li>
        22. Heritage<\/li>
        23. Polymorphism<\/li>
        24. Operator Overload<\/li>
        25. Execution of projects associated with Neuroengineering<\/li><\/ol>
          \u00a0<\/div>
          Workload: <\/b>60 hours\u00a0<\/div>
          Total Credits:<\/b> 4<\/div>

          Offering: <\/b>Annual - 2nd Semester

          <\/p>

          Bibliography:<\/b><\/div>
          Basic Bibliography: \u2013 SOUZA, Marco A. Furlan de et al. Algorithms and programming logic. 2nd ed. S\u00e3o Paulo: Cengage Learning, 2011. \u2013 WALLISCH, Pascal et al. MATLAB for neuroscientists: an introduction to scientific computing in MATLAB. Academic Press, 2014. \u2013 MENEZES, NNC Introduction to Programming with Python. 1st ed. S\u00e3o Paulo: Novatec, 2010.
          Complementary Bibliography: \u2013 SOMMERVILLE, Ian. Software Engineering. 9th Edition, Addison Wesley, 2011. \u2013 PRESSMAN, RS Software Engineering: A Professional Approach. 7th Edition. Porto Alegre: AMGH, 2011. \u2013 PAULA FILHO, WP Software Engineering \u2013 Fundamentals, Methods and Patterns, LTC, 2003. \u2013 PRESSMAN, R. Software Engineering, MacGrawHill, 2006. \u2013 SCHACH, Stephen. Object-Oriented and Classical Software Engineering \u2013 Fifth Edition \u2013 McGrawHill International Edition, 2002. \u2013 PEZZE, M. And Young, M. Software Testing and Analysis, Bookman, 2007. \u2013 PFLEEGER, SL Software Engineering \u2013 Theory and Practice , Pearson\/Prentice-Hall, 2004. Internet Links: https:\/\/www.codecademy.com\/catalog\/language\/python http:\/\/www.programmr.com\/ https:\/\/www.programminghub.io\/learn-programming<\/span><\/div>



          <\/p><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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          \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tTransduction and Processing of Biological Signals<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

          Description:<\/b><\/p>

          Introduction to continuous and discrete signals. Sampling. Characterization of time-invariant linear systems. Fundamentals of data acquisition and noise. Fourier analysis of continuous and discrete signals. Analysis and design of filters. Laplace transform and Z transform. Transduction methods. Applications in neural data analysis.<\/p>

          Skills:
          <\/b>Mastery of Neuroscience and Biomedical Engineering \u2013 Knowing and mastering the main techniques for analyzing biological signals;
          Scientific Mastery \u2013 Be able to understand and use the techniques used in the state of the art of data analysis.
          Creation and Development of Applied Systems \u2013 Design, develop and implement computational systems aiming at the integration of physical and logical resources.
          Teaching Skills \u2013 Develop pedagogical mastery associated with knowledge of neuroengineering.
          Teamwork \u2013 Develop the ability to work in a collaborative environment.
          Society Transformation \u2013 Using the acquired knowledge to cause social transformations.
          \u00a0Ethical Training \u2013 Develop professional activities based on ethical principles.

          <\/b><\/p>

          Learning Objectives:
          <\/b>Know and understand the main signal processing techniques applied to biological data. \u2013 Use signal processing libraries in MATLAB or python. \u2013 Develop pipelines for signal processing in python. \u2013 Ability to choose methods best suited to signal properties.<\/p>

          Thematic content
          <\/b><\/p>

          1. Acquisition and sampling of biological signals<\/li>
          2. Continuous and discrete signals; \u2022 Signal classification<\/li>
          3. Linear and time-invariant systems<\/li>
          4. Noise<\/li>
          5. Fourier Analysis<\/span><\/li>
          6. Filters: ideal filters, filter types, continuous and discrete filters<\/span><\/li>
          7. Filter design<\/span><\/li>
          8. Laplace Transform and Z Transform<\/span><\/li>
          9. Applications and analysis of neural signals<\/span><\/li>
          10. wavelets<\/span><\/li>
          11. Inter-spike Interval and PSTH<\/span><\/li>
          12. connectivity analysis<\/span><\/li><\/ol>
            Workload: <\/b><\/span>60 hours\u00a0<\/span><\/div>
            Total Credits: <\/b>4<\/div>
            \u00a0<\/div>
            Offering: <\/b>Annual - 2nd Semester

            Bibliography:<\/b><\/div>
            Basic Bibliography: \u2022 van Drongelen, W. Signal Processing for Neuroscientists, An Introduction to the Analysis of Physiological Signals, Academic Press, 2006 \u2022 Wallisch, P., Lusignan, M., Benayoun, M., Baker, T., Dickey, A., Hatsopoulos, N. MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB, Academic Press, 2008
            Complementary Bibliography: \u2022 Oppenheim, AV, Willsky, AS, Hamid, S., Nawab, SH Signals and Systems, 2nd edition, Pearson 2010 \u2022 Haykin, S. & Van Veen, B. Signals and Systems, Wiley; 2nd edition, 2002. \u2022 Niedermeyer E. & Silva AFL, Electroencephalography Basic Principles, Clinical Applications, and Related Fields. Williams & Wilkins, 1993.<\/span><\/div><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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            \"\"<\/a>Optional subjects<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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            \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tAnalysis of histological images<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

            Description:<\/b><\/p>

            The subject theoretically supports aspects related to the stages of histological processing and types of microscopy. The discipline also has practical classes directed to the use of the microscope, as well as software for analysis of histological data such as: Volume, number of cells, relative optical density, 3D reconstructions and branching processes.<\/p>

            Skills:<\/b><\/p>

            1. Domain of Neuroscience and Biomedical Engineering \u2013 Apply knowledge of image analysis in scientific studies.<\/li>
            2. Scientific Mastery \u2013 Analyze and interpret histological data with scientific rigor to solve problems.<\/li>
            3. Teaching Skills \u2013 Develop pedagogical mastery associated with knowledge of neuroengineering.<\/li>
            4. Teamwork \u2013 Develop the ability to work in a collaborative environment.<\/li>
            5. Society Transformation \u2013 Using the acquired knowledge to cause social transformations.<\/li>
            6. Ethical Training \u2013 Develop professional activities based on ethical principles.<\/li><\/ol>

              \u00a0<\/b><\/p>

              Learning Objectives
              <\/b>The general objective of the course is to provide a theoretical-practical approach through which the student will develop skills related to extracting and interpreting data from histological images and how to use them in their research.<\/p>

              Thematic content <\/b><\/p>

              1. Histological processing: Overview (Theoretical)
                \u2022 The stages of histological processing \u2022 Systematic sampling of tissue collection \u2022 Types of stains and histochemistry \u2022 General concepts of microscopy \u2022 Possible sources of error arising from processing \u2022 Morphometry vs Stereology vs Image analysis \u2022 Neuroanatomy open science: Open databases \u2022 Examples of articles using the software and analysis that will be covered in the course
                <\/i><\/li>
              2. Acquisition of images in the microscope (Practice)
                \u2022 Microscope components \u2022 Using the microscope for light microscopy \u2022 Using the microscope for fluorescence microscopy \u2022 Adjusting lighting and camera settings \u2022 Acquiring 2D mosaics \u2022 Acquiring 3D stacks
                <\/i><\/li>
              3. Image analysis in imageJ software (Practice)
                \u2022 Insertion of images and stacks in the program \u2022 Image scale calibration \u2022 Saving coordinates in the ROI manager \u2022 Area measurement \u2022 Manual counting of cellular profiles \u2022 Automatic counting of cellular profiles by image segmentation \u2022 Relative optical density \u2022 3D reconstruction of blocks of tissue \u2022 Pixel per line variance analysis \u2022 Kymographs
                <\/i><\/li>
              4. Stereological analysis in the StereoInvestigator software (Practice)
                \u2022 StereoInvestigator tools and operation \u2022 Cavalieri Estimator: Regional volume estimation \u2022 Optical fractionator: Total cell number estimation \u2022 Nucleator: Somatic and nuclear volume estimation \u2022 Sholl analysis
                <\/i><\/li>
              5. Image analysis in iLastik software (Practice)
                \u2022 Machine learning applied to image analysis \u2022 Automatic counting of cell profiles \u2022 Advantages and disadvantages of automatic classifiers <\/i><\/li><\/ol>

                Workload:<\/b> 30h\u00a0<\/p>

                Total Credits: <\/b>2<\/p>

                Bibliography:<\/b><\/div>
                \u2022 Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, et al. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J Pathol Inform. 2019. \u2022 Ferreira T, Rasband W. ImageJ user guide- IJ1.46r, 2012. \u2022 West, MJ Basic Stereology for Biologists and Neuroscientists, 2012. \u2022 Wootton R, Springall DR, Polak JM. Image Analysis in Histology: Conventional and Confocal Microscopy. CambridgeUniversity Press, 1995.<\/span><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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                \n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/image><\/pattern><\/defs><\/rect><\/svg><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tBiostatistics<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

                Description:<\/b><\/span><\/p>

                Biostatistics is a fundamental tool for the development of basic definitions for a large part of modern biological theories, covering the planning of biological experiments, summarization and analysis of data from experiments and the interpretation and inference of results. The discipline will prioritize understanding ideas rather than memorizing formulas.<\/span><\/p>

                The general objective of the discipline is to allow the student to manage statistical methods that help them in the correct interpretation of the data generated by the experimental practice, based on expository classes, always associated with the performance of activities of fixation of the contents taught in traditional software of statistical analysis, such as SPSS, or even with free software (PSPP), using examples that allow the student to identify which type of statistical analysis best fits their own results.<\/p>

                Thematic content
                <\/b><\/p>

                1. Experimental research planning
                  Introduction: Origin, biostatistics \u2022 Experimental research planning: Scientific methods (deductive, inductive, experimental\u2026) \u2022 Scientific research: Structure (decision, execution, analysis, writing) \u2022 Research formulation: Feasible, new, ethical, interesting \u2022 Outlines of research studies: Observational, experimental and meta-analysis research
                  Introduction to SPSS and PSPP
                  Probability: Random experiment, sample space, mutually exclusive examples (or) non-exclusive examples (e) arrangement and combination \u2022 Sample size and randomization: probabilistic and non-probabilistic samples, errors in the sampling process, calculation of sample \u201cn\u201d, randomization methods \u2022 Data collection, organization and analysis: accuracy, precision and bias (selection, measurement\u2026)<\/i><\/li>
                2. Introduction to descriptive statistics
                  Study of statistical variables \u2022 Frequency distribution: data ordering, total amplitude, number of classes, histogram, stem and leaf \u2022 Frequency distribution parameters: measures of central tendency (mode, median, mean (arit., geom., harmonic) )), measures of dispersion (total amplitude, variance, standard deviation...), measures of skewness and kurtosis (flattening), measures of position (quartile, decile, box plot, outlier), standard error of the mean, confidence interval \u2022 Probability distribution: Gaussian, Bernoulli (binomial), and Poisson Statistical inference and hypothesis testing: H0, H1, type 1 and type 2 errors <\/i><\/li>
                3. Parametric hypothesis tests
                  Student's t test: for independent samples, for paired samples \u2022 Correlation and simple linear regression: scatterplot, r-value; coefficient of determination (r\u00b2), equation of the line<\/i><\/li>
                4. Non-parametric hypothesis tests
                  Chi-square distribution \u2022 Normality tests: Kolmogorov-Smirnov \u2013 KS, and Shapiro-Wilk \u2022 Diagnostic Screening Tests \u2013 Screening test: sensitivity and specificity \u2022 Wilcoxon-Mann-Whitney U test (WMW) \u2022 Wilcoxon t test \u2022 Tables 2\u00d72 <\/i><\/li>
                5. Multivariate analysis
                  Analysis of Variance (One way Anova) (F Test) \u2022 Two way Anova \u2022 Repeated measures Anova \u2022 Kruskal-Wallis Test (H Test) \u2022 Friedman Test \u2022 Factorial experiments (factorial \/ multivariate analysis)<\/i><\/li><\/ol>
                  Workload: <\/b>45 hours\u00a0<\/div>
                  Total Credits: <\/b>3<\/div>
                  \u00a0<\/div>

                  Offering: <\/b>Annual - 1st Semester<\/p>

                  Bibliography:<\/b><\/div>
                  \u2022 Fontelles, MJ Biostatistics applied to experimental research \u2013 vol. 1, 2012 \u2022 Fontelles, MJ Biostatistics applied to experimental research \u2013 vol. 2, 2012 \u2022 S\u00f4nia Vieira. Introduction to Biostatistics, 2008 \u2022 Andy Field. Discovering Statistics Using Spss, 2013 \u2022 Arthur Griffith. SPSS For Dummies, 2010 \u2022 Thomson Learning Kirkpatrick. A Simple Guide to Advanced Statistics in Spss Bibliographic references: \u2022 Julie Pallant. The SPSS Survival Guide, 2013 \u2022 John Wiley & Sons. Biostatistics For Dummies, 2013 \u2022 Deborah Rumsey. Statistics for Dummies, 2009 \u2022 Stanton A. Glantz. Principles of Biostatistics, 2014. \u2022 SAGE Publications. Discovering Statistics Using SPSS (Introducing Statistical Methods series), 2009 \u2022 Adriano Leal Bruni. Spss: Practical Guide for Researchers, 2012 \u2022 Harvey Motulsky. Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 2013 \u2022 S\u00f4nia Vieira. Biostatistics \u2013 Advanced Topics, 2010<\/span><\/div><\/div><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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                  Description:<\/b><\/p>

                  Ethics, morals and law; bases of bioethics: origin, concept and principles; ethics and human rights; situations of conflicts of values and ethical principles; research ethics; bioethics and interdisciplinarity; current ethical dilemmas; neuroethics; social responsibility in scientific research.<\/p>

                  Main goal:\u00a0<\/b><\/p>

                  Discuss the interrelations between the fields of bioethics and scientific research. Specific Objectives: 1) Recognize the conceptual foundations of bioethics and its principles 2) Identify situations involving ethical dilemmas in scientific research 3) Discuss current ethical dilemmas, including neuroethics.<\/p>

                  Thematic Contents:<\/b><\/p>

                  1. Introduction to Bioethics
                    \u2022 Ethics, morals and law \u2022 Origin, concept, sources of Bioethics \u2022 Bioethics principles \u2022 ethics and human rights.<\/i><\/li>
                  2. Identifying situations of conflict of values and ethical principles: video sessions<\/li>
                  3. Today's ethical dilemmas
                    \u2022 Science and Technology \u2022 Medical Ethics \u2022 Genetics \u2022 Human Reproduction \u2022 Neurosciences \u2022 Social Ethics<\/i><\/li>
                  4. Ethics in research \u2013 Ethical principles in research involving human beings \u2013 Ethical limits for the use of animals in scientific research<\/li>
                  5. Neuroethics<\/li>
                  6. Social responsibility in scientific research.<\/li><\/ol>
                    \u00a0<\/div>
                    Methodology:\u00a0<\/b><\/span>
                    The contents will be worked through the following methodologies: expository dialogued classes, seminars and discussions based on problem situations, films and texts involving the related theme. The evaluation process will consist of two instruments, with equal weights (5.0 + 5.0): collective production, by the seminar presentation group and individual production of narrative, theme, style and form chosen by the master's student, on one of the thematic contents III, IV, V and VI.<\/span><\/div>
                    \u00a0<\/span><\/div>
                    Workload: <\/b>30h\u00a0<\/div>
                    Total Credits:<\/b> 2<\/div>

                    Offering:<\/b> Biennial - 1st Semester<\/p>

                    Bibliography:<\/b><\/div>
                    Darragh, M. The Neuroscience of Morality and Consciousness: A Brief Introduction. Available at: https:\/\/bioethics.georgetown.edu\/explore-bioethics\/neuroethics\/ Diniz D; Guilhem D. What is Bioethics. Editora Brasiliense, S\u00e3o Paulo, 2012. Kogut M. Foresight Europe no 2, 2005-2006, EIASM, Brussels, Belgium. Lalancette H;\ufffd\ufffd Campbell, SR. Educational neuroscience: Neuroethical considerations. International Journal of Environmental & Science Education, vol. 7, No. 1, January 2012, 37-52 Moratalla, NL. Neuroethics: the ethical endowment of the human brain. Cuadernos de Bio\u00e9tica XXVI, 2015\/3a, 415-425.<\/span><\/div><\/div><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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                    Description:<\/b><\/p>

                    The course aims to describe and analyze different aspects of cognition in rodents and primates, establishing their neural correlates. Classes will consist of discussions of book chapters and recent articles related to the subject's thematic content.<\/p>

                    Skills<\/b><\/p>

                    1. Scientific Domain \u2013 Research in the indicated materials to expand knowledge of the subject discussed in the discipline.<\/li>
                    2. Teaching Skills \u2013 Develop pedagogical mastery associated with knowledge of neuroscience, with emphasis on cognitive processes, and neuroengineering.<\/li>
                    3. Teamwork \u2013 Develop the ability to work in a collaborative environment in a multidisciplinary environment.<\/li>
                    4. Transformation of Society \u2013 Using the acquired knowledge to promote social transformations.<\/li>
                    5. Ethical Training \u2013 Develop professional activities based on ethical principles.<\/li><\/ol>

                      Learning Objectives
                      <\/b>Develop students' knowledge about cognition in different animal species. \u2013 Conceptualize the main topics about cognition, forming a framework of knowledge that will guide students in the following disciplines of the program. \u2013 Stimulate and guide students in reading and discussing articles and book chapters on animal cognition. \u2013 Draw a parallel between animal cognition and applicability in research that uses humans as experimental subjects.<\/p>