Mind and Brain I
Code: 106578 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Artificial Intelligence | FB | 2 |
Contact
- Name:
- Margalida Coll Andreu
- Email:
- margalida.coll@uab.cat
Teachers
- Elena Martin Garcia
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
No prerequisites are required.
Objectives and Contextualisation
The general aim of this course intends that the students understand how the nervous system works, which are the neural bases of cognitive processes, and the bidirectional relationship between neuroscience and artificial intelligence. Taking into account the former considerations, this course will be addressed to the following specific aims:
1. To know and understand the anatomical, cellullar and molecular foundations of information processing in the nervous system.
2. To understand the mechanisms of plasticity in the nervous system, from the synaptic level to experience-dependent functional multimodal reorganization.
3. To know and understand the neural bases of sensory processing at multiple levels of the nervous system.
4. To know and understand the neural bases of learning and memory, and emotions.
5. To know the main techniques for the recording and stimulation of neural activity, and to identify the practical applications based on artificial intelligence.
Competences
- Act within the field of knowledge by evaluating sex/gender inequalities.
- Communicate effectively, both orally and in writing, adequately using the necessary communicative resources and adapting to the characteristics of the situation and the audience.
- Identify, understand and analyse the fundamental characteristics of neural mechanisms and human psychological and cognitive processes and relate them to the processes of automatic intelligent systems.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Work independently, with responsibility and initiative, planning and managing time and available resources, and adapting to unforeseen situations.
Learning Outcomes
- Analyse sex/gender inequalities and gender bias in the field of knowledge.
- Communicate effectively, both orally and in writing, adequately using the necessary communicative resources and adapting to the characteristics of the situation and the audience.
- Identify the main anatomical and histological characteristics of the nervous system, as well as the cellular, molecular and electrophysiological bases of chemical synaptic transmission.
- Relate the different mechanisms and types of synaptic plasticity to cognitive and behavioural plasticity, and in particular to learning and memory.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Understand information processing mechanisms in terms of both the synaptic and neural systems.
- Understand the main techniques for recording and stimulating neural activity, as well as their usefulness and limitations.
- Understand the main techniques for recording perception in terms of behaviour and cognitive systems (psychophysics), as well as their usefulness and limitations.
- Understand the neurobiology of hearing and its implications and applications in the field of artificial intelligence.
- Understand the neurobiology of somatosensory systems and their implications and applications in the field of artificial intelligence.
- Work independently, with responsibility and initiative, planning and managing time and available resources, and adapting to unforeseen situations.
Content
1. Introduction: The interrelationship between neuroscience and artificial intelligence
- Bioinspired artificial intelligence
- Applications of artificial intelligence to neuroscience and to the study of behavior and mental functions.
2. Structure and function of the nervous system: Molecular, cellular and synaptic levels
2.1. The cells of the nervous system
2.2. Membrane potential, action potential and synaptic transmission.
2.3. Mechanisms of synaptic plasticity.
2.4. Biological neural networks.
3. Structure and function of the nervous system: systems level.
3.1. Main divisions of the nervous system and their organization.
4. Techniques for recording and stimulation of neural activity
4.1. Electrophysiological techniques of stimulation and recording of neuronal populations and individual neurons
4.2. Calcium imaging techniques
4.3. Optogenetics
4.4. Structural and functional neuroimaging.
4.5. Neural interfaces
5. How the brain perceives the world.
5.1. General organization of the sensory systems
5.2. Transduction and codification in the somatosensory systems
5.3. Transduction and codification in the auditory system
6. How the brain learns, remembers and forgets
6.1. Memory systems in the brain: Neural bases of the implicit and explicit systems
6.2. Memory, extinction, forgetting and synaptic plasticity
7. Biological bases of motivations and emotions
7.1. Components of emotions
7.2. Neural bases of emotion expression and comprehension
7.3. The reward neural system and its alterations. Addictions
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Laboratory practicals | 4 | 0.16 | 6, 2, 3, 5 |
| Master classes | 24 | 0.96 | 6, 9, 10, 8, 7, 3, 4 |
| Seminars | 22 | 0.88 | 1, 6, 2, 9, 10, 8, 7, 3, 5, 4 |
| Type: Supervised | |||
| Tutoring (group and individual) | 20 | 0.8 | 1, 6, 9, 10, 8, 7, 3, 5, 4 |
| Type: Autonomous | |||
| Study | 50 | 2 | 6, 9, 10, 8, 7, 3, 4, 11 |
| Team work | 21 | 0.84 | 2, 5, 11 |
The methodology includes several types of activities. Master classes, seminars and laboratory practicals, as well as supervised and autonomous activities, will be scheduled.
Use of Artificial Intelligence
In this subject, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution of the student in the analysis and personal reflection. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity. The lack of transparency in the use of AI will be considered a lack of academic honesty and may lead to a penalty in the grade of the activity, or greater sanctions in serious cases.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Evidence 1. Follow-up activities | 30% | 4 | 0.16 | 1, 6, 9, 10, 8, 7, 3, 4, 11 |
| Evidence 2. Team report based on scientific papers | 20% | 2 | 0.08 | 1, 6, 2, 9, 10, 8, 7, 5, 4, 11 |
| Evidence 3. Partial exam | 15% | 1 | 0.04 | 6, 8, 7, 3 |
| Evidence 4. Final exam | 35% | 2 | 0.08 | 1, 6, 9, 10, 8, 7, 3, 4 |
The evaluation of this subject is carried out continuously. The evaluation has a clear formative function.
The competences of this subject will be evaluated by means of: follow up activities, team presentations and reports, as well as exams.
The learning evidences that the student must deliver will refer to the contents and competences worked in the theoretical classes and seminars, and in the laboratory practicals.
- Evidence 1. Ongoing activities based on exercises that will be carried out throughout the semester. Some of the exercises will be done in the classroom and others autonomously. Likewise, some exercises will be carried out individually and others in group: 30%.
Feedback through Moodle or in tutorials as soon as each activity has been carried out.
- Evidence 2. Oral presentation and defense of a team work based on scientific articles: 20%.
Feedback through Moodle or in tutorials on weeks 15 and 16.
- Evidence 3. Partial exam, which will be held in the middle of the semester: 15%.
Feedback in tutorials after publication of the grades.
- Evidence 4. Final exam, which will be held at the end of the semester and will include content from the entire subject: 35%.
Feedback in tutorials after publication of the grades.
Subject passed
The subject is passed when the student meets the following two conditions:
a) has obtained a grade of at least 5 points (out of 10) when all the evidences are considered
b) has obtained a score of at least 4.5 points (out of 10) in Evidence 4.
If these two conditions are not met, the grade on the academic transcript will be the weighed sum of the grades of the four evidences if this summation is lower or equal to 4.5, and 4.5 otherwise (thus, the màximum grade available for students not fulfilling the two criteria will be 4.5).
A "honors' grade (“Matrícula d’Honor) will be given to the 5% of enrolled students having the best grade, provided that this grade is 9 or higher.
Recuperation
To be allowed to opt for resit the student has to fulfill the following requisites: 1)having presented evidences with a weight of at least two thirds of the total grade of the subject; 2) having a mark equal to or greater than 3.5 points and lower than 5 points.
The recuperation will consist of an exam about all the subject, that will contain questions about all the theoretical contents, as well as the resolution of practical exercises. The maximum grade that can be obtained in the course, in case of overcoming the recovery, will be 5.
Subject 'not evaluable'
A student who has given learning evidences with a weight lower than 4 points (40%) will have a "non-evaluable" label in the transcript of records.
Single assessment act
This subject does not offer the possibility of taking a single assessment (single assessment act).
Bibliography
Books and papers
Carlson, N.R.; Birkett, M.A. (2017). Physiology of Behavior, Global edition. Pearson Education (both paperback and online versions are available in the library).
Cohen Y, Engel TA, Langdon C, Lindsay GW, Ott T, Peters MAK, Shine JM, Breton-Provencher V, Ramaswamy S. Recent Advances at the Interface of Neuroscience and Artificial Neural Networks. J Neurosci. 2022 Nov 9;42(45):8514-8523. doi: 10.1523/JNEUROSCI.1503-22.2022.
Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci. 2023 Jun 28;17:1092185. doi: 10.3389/fncom.2023.1092185
Websites
https://www.ebrains.eu/
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PAUL) Classroom practices | 711 | English | first semester | afternoon |
| (PLAB) Practical laboratories | 711 | English | first semester | morning-mixed |
| (PLAB) Practical laboratories | 712 | English | first semester | morning-mixed |
| (TE) Theory | 71 | English | first semester | afternoon |