Place/Time: Room: I-8, Lectures: Wednesday 14:00 - 15:30, Labs: Thursday 14:00 - 15:30
Credits: 6
Lecturer: Prof. RNDr. Ľubica Beňušková, PhD.
Teaching Assistant: RNDr. Kristína Malinovská, PhD.
TEXTBOOK: O’Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., and Contributors.
Computational Cognitive Neuroscience.
Wiki Book, 4th Edition (2020).
Computational cognitive neuroscience relies upon theories of cognitive science coupled with neuroscience and computational modeling. In this course, we will study neurobiological processes that underlie cognition by means of theory of computational models. We will address the questions of how cognitive processes are affected and controlled by neural circuits in the brain.
Presentation (guidelines) | 25% |
Project (guidelines) - due 15.04.2024 | 25% |
Final written exam | 50% |
0-50 % | Fx |
51-60 % | E |
61-70 % | D |
71-80 % | C |
81-90 % | B |
91-100 % | A |
Date | Lecture | Presentation Student / paper |
Required Reading |
---|---|---|---|
21.02. | Introduction to computational cognitive neuroscience. Main concepts in modelling. (L1 slides) | About assignments, Q&A | O'Reilly,ch.1; Farkas (2012) |
28.02. | Biophysics of an individual neuron. Spiking neurons models. (L2 slides) | Single neuron
(slides) Neuron as a detector |
O'Reilly,ch.2 |
06.03. | Structure of cortical networks, localist and distributed representations, excitation and inhibion of neurons. (L3 slides) | O'Reilly,ch.3 | |
13.03. | Synaptic plasticity and metaplasticity, self-organization and error-driven learning. (L4 slides) | Sarah / Synaptic metaplasticity | O'Reilly,ch.4 |
20.03. | Functional organisation of the brain. Overview of brain areas. (L5 slides) | Alexandra / Signaling in neocortex Project consultation | O'Reilly,ch.5 |
27.03. | Visual perception, attention, bottom-up and top-down mechanisms. Spatial neglect. (L6 slides) | Easter Holiday | O'Reilly,ch.6 |
03.04 | Motor control and reinforcement learning. (L7 slides) | Peter / Attention | O'Reilly,ch.7 |
10.04. | Learning and Memory -- Semantic and episodic memory, implicit vs. declarative, priming, familiarity, etc. (L8 slides) |
Hana / Life-long Learning |
O'Reilly,ch.8 |
17.04. |
Language.
(L9 slides) Executive functions, the role of frontal cortex. (L10 slides) |
Students Science Conference | O'Reilly,ch.9 & ch.10 |
24.04. | Agency, theory of mind, self-awareness. (L11 slides) |
Alica / Semantic systematicity Oleksandr / Predicting meaning of nouns |
|
01.05. | International Workers Day |
XY / Mirror neurons Laura / PFC control |
|
08.05. | Victory over Fascism Day |
XY / Self cognition Marvin / Consciouss perception model Postponed presentations |
|
15.05. | Cognition and Artificial Life | (Teachers attending a conference) |
You can download the simulator according to the instructions on this page.
The Emergent software has a new version. If you encounter any issues opening the Exercise Project or additional files within the project in Emergent (such as pre-trained weights) ask your TA.
The
Part A: Firing Patterns (50%)
Implement the model or use the Python code for task A
to vary parameters to achieve 8 firing patterns (RS, IB, CH, FS, TCa, Tcb, RZ, LTS) from
Izhikevich (2003) (Fig. 2).
Display the firing patterns in graphs and comment on them briefly.
If you choose to implement the model yourself, implement differential equations of form dx/dt = f(x) using the forward
Euler method: x(t+h) = x(t) + hf(x(t)), where h is the step size (e.g. 1 millisecond).
More help can be found on the author's webpage under Research and Models of spiking neurons.
Part B: Network of Neurons (50%)
Implement or use the Python code for task B of a neural network of 1000 spiking
neurons with random connections as described in part IV of
Izhikevich (2003) paper.
Show the network's ability to self-organize causing the neurons to fire synchronously in time as in Fig. 3. of the paper.
Explore, display in pictures and comment on (a) the influence of the inhibition-excitation neuon numbers ratio and (b)
the influence of thalamic noise magnitude on the model's behavior. Answer the following questions: Under which conditions
(i.e. values of which parameters) does the network show periodic synchronizations? Under which conditions the spiking of neurons
does NOT synchronize at all? Any other intersting behavior you can observe?
Evaluation detail: Part A - Firings 4p, Resonator 2p; Part B - Inhib-excit. 2p, B-Thalam noise 2p, B-Synchronization 2p; Overall Form 3p, Text 5p;
Examples of reports:
2021 Komarová, Ľ,
2021 Zvarík, E.,
2019 Vizcaya D. T.,
2019 Koellner J.,
2019 Vella N.,
2013 Dinga, R. (sk),
*2011 Antonič, M. (sk)
Here are some useful GUIDELINES how to make a good paper presentation. The whole presentation should last around 30-40 minutes, then questions and discussion follow. You should sign up for a concrete date and topic (see the column of presentation) through email to both the TA and lecturer. Email the slides to both the lecturer and TA after the presentation.