ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 15 Interactive Classroom: 10 Total: 26
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Physiology, Electronics and Computing
Areas: Physiology, Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable
The objective of the course is to learn about the physiology of the nervous system and the brain. An introduction to the concept of the concept of natural intelligence, where the notion of intelligence and a general description of the different types of intelligence that are known. The physical substratum that sustains intelligence will then be discussed, providing an overview of the central nervous system and the brain in particular. The physiology of the biological computing units, the neurons, and how they process and propagate information will be discussed. Finally, the course will cover examples of neural circuits and networks, with emphasis on the associative and integrative mechanisms that allow explaining simple behaviors.
The specific objectives of the course are:
- To know the physiology of the nervous system and the brain, the notion of intelligence and its types.
- To know the basis of the physical substrate that sustains intelligence: central nervous system and brain.
- To know the basic mechanisms of functioning of neurons for the propagation of information.
The contents are distributed in theory and practices:
Theory:
1. Introduction: Artificial Intelligence and Natural Intelligence.
2. Membrane level: Intra and extracellular media: ion movement, resting membrane potential, ion channels.
3. Neuron level: neuronal morphology, electrical activity in dendrites-soma-axon, action potential, propagation of potential variations.
4. Synapse level: chemical and electrical synapses, processes in the presynaptic and postsynaptic terminal, synapse models.
5. Circuit level: general systems, microcircuits, lateral inhibition and feedback systems, macrocircuits, somatosensory system, visual system.
Labs:
Lab 1: Membrane and neuron level: membrane potential/action potential. Tool: Neurosim.
Lab 2: Synapse level: chemical synapse. Tool: Neurosim.
Lab 3: Circuit level: lateral inhibition and feedback systems. Tool: Neurosim.
Basic:
Principles of neural science by Eric R. Kandel, John D. Koester, Sarah H. Mack, Steven A. Siegelbaum Ed McGraw-Hill
Complementary:
From molecules to networks : an introduction to cellular and molecular neuroscience by John H. Byrne and James L. Roberts Editor Academic Press
The course contributes to the development of the following general and specific competences included in the degree report:
Basic and general competences:
CG2 - Ability to solve problems with initiative, decision-making, autonomy and creativity.
CG4 - Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on artificial intelligence.
Transversal competences:
TR1 - Ability to communicate and transmit their knowledge, skills and abilities.
TR3 - Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
The learning outcomes will be:
- To know the physiology of the nervous system and the brain, the notion of intelligence and its types.
- To know the basis of the physical substrate that sustains intelligence: central nervous system and brain.
- To know the basic mechanisms of neuron functioning for the propagation of information.
The teaching methodology will be different for the theory/exhibition classes and for the practical/laboratory classes:
- The theory classes will consist of expository classes where the concepts and fundamentals of the subject will be explained. Exercises and exercises and questions for the student to apply the concepts and reasoning using the fundamentals of the subject. Competences worked: CG2 and TR3.
- The practical or laboratory classes will consist of experiments and practical problems to understand the fundamentals of the nervous system and neuron activity. The student will work individually or in small groups to perform the experiments and solve the problems posed. At the end of the sessions they will have to answer a set of questions about the practices performed. Competences covered: CG2, CG4, TR1 and TR3. 100% attendance is mandatory for lab report evaluation.
The Virtual Campus will be used as a basic platform (content repository and virtual tutoring of students). In the virtual classroom of the students will have all the information (theoretical material, class slides, practice scripts, etc.).
The evaluation will take into account both the theoretical and practical parts. In order to pass the subject, the student will have to obtain an overall mark 5 or higher (out of a maximum of 10 points)
The weight of each part in the overall grade is as follows:
Lab reports: 40% of the grade. 100% attendance is mandatory for lab report evaluation. Delivery of reports: L1, L2, L3 and L4, at the end of each session; L5 during an interval of two weeks after the end of the session.
Final exam: 60% of the grade
A minimum grade in the final exam of 4.0 out of 10 (2.4 out of 6) will be necessary to add the grade obtained from the evaluation of the practice reports in the calculation of the final grade.
Students who have not taken the exam and have not submitted the internship reports will receive a grade of “not submitted”. practice reports.
In order to pass the course in the second opportunity, students must submit to the evaluation of the parts of the subject that have not been passed in the first opportunity. the first opportunity. For the rest, the grades obtained during the course will be retained.
In the case of fraudulent performance of exercises or tests, the provisions of the regulations on the evaluation of the academic performance of students and review of grades will apply (https://www.xunta.gal/dog/Publicados/2011/20110721/ AnuncioG2018-190711-4180_gl.html).
In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copy of any exercise of practices or theory will mean the failure of the two opportunities of the course, with the grade of 0.0 in both cases (https://www.usc.es/etse/ files/u1/NormativaPlagioETSE2019.pdf)
The face-to-face work time for the subject is 25 hours, with the following distribution:
Theory hours: 15h
Practice hours: 10h
The estimated study time for the student is 50 hours.
It is recommended that students keep the theoretical contents of the subject up to date. And on the other hand, solve all the proposed exercises and practices (not just the evaluable ones). It is also considered important to make intensive use of tutorials to resolve doubts and active participation in expository and interactive sessions.
Eduardo Manuel Sánchez Vila
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816466
- eduardo.sanchez.vila [at] usc.es
- Category
- Professor: University Lecturer
Francisco Javier Martin Cora
Coordinador/a- Department
- Physiology
- Area
- Physiology
- Phone
- 881812295
- franciscoj.martin.cora [at] usc.es
- Category
- Professor: Temporary PhD professor
| Monday | |||
|---|---|---|---|
| 09:00-10:30 | Grupo /CLE_01 | Spanish | IA.01 |
| Wednesday | |||
| 15:30-17:30 | Grupo /CLIL_02 | Spanish | IA.S1 |
| 17:30-19:30 | Grupo /CLIL_01 | Spanish | IA.S2 |
| 12.19.2025 16:00-20:00 | Grupo /CLIL_01 | IA.11 |
| 12.19.2025 16:00-20:00 | Grupo /CLIL_02 | IA.11 |
| 12.19.2025 16:00-20:00 | Grupo /CLE_01 | IA.11 |
| 06.16.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
| 06.16.2026 16:00-20:30 | Grupo /CLIL_02 | IA.01 |
| 06.16.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |