bam33nin -- Neuroinformatika / Neuroinformatics

Výuka | Course info

Podmínky předmětu a způsob hodnocení/ Course conditions and evaluation method

Rozvrh BAM33NIN 2023/2024 Schedule BEAM33NIN 2023/2024

The course will be taught in a hybrid format: CTU students will attend in person, while international students will be invited to join via Zoom. Please note that all lectures and lab exercises will be recorded.

The course sessions are scheduled for every Wednesday at 12:45 PM CET/CEST (ZOOM link lectures), with laboratory sessions commencing at 4:15 PM CET/CEST(ZOOM link labs).

Throughout the course, we will explore the fundamentals of information processing at the neural single/muliti-cell level. It is also my honour to have esteemed colleagues join us as guest speakers, who are experts in computational neuroscience. David Kala will discuss brain vascularization, an often overlooked yet critical topic. Neuroscientist and clinician Pavel Filip will share insights on advanced techniques in deep brain stimulation. Jan Antolik will delve into the intricacies of visual prosthetics. Jirka Hammer will guide us through the world of cognitive neuron processing in the cortex. Lastly, Karla Stepanova will address the topic of cognitive modelling, which has significant applications in communication with robots.

The internal material can be downloaded here:here

Přednášky | Lectures

  1. 21.2: Úvod do neuroinformatiky, organizace předmětu, model membrány | Introduction Lecture 1, recorded lecture 2024
  2. 28.2.: Modely neuronů: iontové kanály, synapse, Hodgkin-Huxley, Wilsonův model | Neuron models Lecture 2, recorded lecture 2024
  3. 6.3: Hodgkin-Huxley model Lecture 3 , recorded lecture 2024
  4. 13.3: Případová studie - Parkinsonova nemoc | Case study: Parkinson's disease (Eduard Bakštein)Lecture 4, Recorded lecture 2024
  5. 20.3: Případová studie - Epilepsie a Výzkum mozku pomocí MRI | Case study - Epillepsy and MRI for Brain research (David Kala) Lecture 5,recorded lecture 2024
  6. 27.3.: Cable theory, Simplified neuron Lecture 6, recorded lecture 2024
  7. 3.4: Synaptická plasticita | Synaptic plasticity Lecture 7, recorded lecture 2024
  8. 10.4: Random networks ex vivo Lecture 8,recorded lecture 2024
  9. 17.4: Self organizing maps and dynamic neural fields Lecture 9,recorded lecture 2024
  10. 24.4: Advanced analysis of MRI signals (Pavel Filip)
  11. 1.5. svátek
  12. 9.5:(čtvrtek, náhrada výuky) Modelovani vizualniho kortexu|Visual cortex modelling (Jan Antolik) Lecture 10 , recorded lecture, discussion
  13. 15.5: Rozhraní mozek-počítač | Brain-computer interfaces (Jiří Hammer)
  14. 22.5: Kognitivní modelování | Cognitive modelling (Karla Štěpánová) recorded lecture, discussion

Cvičení | Lab exercises

Instruction for lab exercises (update 9.4.2024)

ZOOM link (for international students): https://feectu.zoom.us/j/92565944239

System for assignment hand-in: https://cw.felk.cvut.cz/brute/

Labs: in person, room KN:E230

Assignments: All assignments are to be submitted through the BRUTE system (link above). Upload your assignment as a zip archive with all necessary codes with a short pdf report, briefly summarizing the task and showing the results of the simulations.

Deadlines and penalties: 1 week for completion at full grade, two weeks 50% penalty, 2+ weeks 100% penalty (0 points). All exercises have to be completed and handed in in order to obtain assessment.

Doporučená literatura / Recommended literature

Available from the dept. of Cybernetics' library - contact Dr Petr Novak (novakpe@fel.cvut.cz)

[1] Thomas Trappenberg. Fundamentals of Computational Neuroscience. Oxford University Press, USA, June 2010.

[2] David Fitzpatrick William C. Hall Anthony-Samuel LaMantia Leonard E. White Dale Purves, George J. Augustine. Neuroscience. Sinauer Associates, Inc., 5th. edition edition, 2011.

[3] Michael L. Hines Nicholas T. Carnevale. The Neuron Book. Cambridge University Press, 2006.

[4] Werner M. Kistler Wulfram Gerstner. Spiking Neuron Models: Single Neurons, Populations, Plas- ticity. Cambridge University Press, 2002.

Program cvičení | Exercise materials

  1. Numerical integration - Euler's method | Matematický aparát: numerické metody řešení dif. rovnic
  2. Neuron models: RC model| RC model
  3. EPSP model | EPSP model
  4. Hodgkin-Huxley model | Modelování neuronů II - Hodgkin-Huxley
  5. Poisson spiketrain | Poissonovský spiketrain (+ LIF model)
  6. Generation of artificial uEEG, LIF model | Vytváření simulovaného uEEG signálu, LIF model
  7. Comparison of real and simulated data | Porovnání simulovaných dat s reálnými s reálnými
  8. Real data analysis - spike sorting | Analýza reálných dat - Spike sorting
  9. Real data analysis - eye movements | Analýza reálných dat - okohybné pohyby
  10. Hebbian learning | Hebbovské učení
  11. Public holidays | SVÁTEK
    • 1.5.2024
  12. Spiking networks | Spontánní aktivita neuronových sítí
  13. Self-organizing maps | Samoorganizující mapy
  14. Hand-in of remaining assignments, assessmentodevzdání zbývajících úloh, zápočet
    • 22.5.2024

Kontakty | Contacts

Přednášející | Lecturer: Daniel Novák

Cvičící | Labs TAs: Eduard Bakštein

Konzultace po individuální e-mailové domluvě s cvičícím / Consultations possible upon email request to the TAs

Fórum

courses/beam33nin/start.txt · Last modified: 2024/04/17 17:46 by bakstedu