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 subject aims to understand the physiology of the nervous system and the brain. An introduction to the concept of natural intelligence is proposed, where the notion of intelligence will be explained and a general description of the different types of intelligence that are known. The physical substrate that supports intelligence will be discussed below, providing a general view of the central nervous system, and the brain in particular. The physiology of biological computing units, neurons, and how they process and propagate information will be addressed. Finally, the subject will cover examples of neural circuits and networks, focusing on the associative and integration mechanisms that allow explaining simple behaviors.
The specific objectives of the subject are:
• Know the physiology of the nervous system and the brain, the notion of intelligence and its types
• Know the bases of the physical substrate that supports intelligence: central nervous system and brain
• Know the basic functioning mechanisms 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 subject contributes to the development of the following general and specific competencies included in the title report:
Basic and general skills: 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 skills: TR1 - Ability to communicate and transmit your knowledge, skills and abilities. TR3 - Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
The teaching methodology will be different for theory/expository classes and for practical/laboratory classes:
- The theoretical classes will consist of expository classes where the concepts and foundations of the subject will be explained. Exercises and questions will be proposed so that the student applies the concepts and reasons using the fundamentals of the subject.
- 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 carry out 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 carried out.
The Virtual Campus will be used as a basic platform (content repository and virtual tutoring of students). In the subject's virtual classroom, 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. To pass the subject, the student will have to achieve an overall grade equal to or greater than 5 (out of a maximum of 10 points). The weight of each part in the overall grade is as follows:
Practice reports: 40% of the grade
Final exam: 60% of the grade
Students who have not taken the exam or submitted the practice reports will be graded as not presented. To pass the subject on the second opportunity, students must undergo the evaluation of the parts of the subject that have not been passed on the first opportunity. For the rest, the grades obtained during the course will be kept.
In the case of fraudulent completion of exercises or tests, the provisions of the regulations for evaluating the academic performance of students and reviewing grades will apply ((https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…)). In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 12/19/2019), the total or partial copy of any practical or theory exercise will result in failure of the two opportunities in 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 | |||
|---|---|---|---|
| 09:30-11:00 | Grupo /CLE_01 | Spanish | IA.01 |
| Wednesday | |||
| 12:00-14:00 | Grupo /CLIL_02 | Spanish | IA.02 |
| 15:30-17:30 | Grupo /CLIL_01 | Spanish | IA.13 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_02 | Classroom A4 |
| 12.20.2024 16:00-20:00 | Grupo /CLE_01 | Classroom A4 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_01 | Classroom A4 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_02 | IA.01 |
| 12.20.2024 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_02 | IA.11 |
| 12.20.2024 16:00-20:00 | Grupo /CLE_01 | IA.11 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_01 | IA.11 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_01 | IA.12 |
| 12.20.2024 16:00-20:00 | Grupo /CLE_01 | IA.12 |
| 12.20.2024 16:00-20:00 | Grupo /CLIL_02 | IA.12 |
| 06.19.2025 16:00-20:00 | Grupo /CLE_01 | IA.11 |
| 06.19.2025 16:00-20:00 | Grupo /CLIL_01 | IA.11 |
| 06.19.2025 16:00-20:00 | Grupo /CLIL_02 | IA.11 |