Computational Cognitive Neuroscience
Master's Program in Cognitive Science, Comenius University in Bratislava

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).

Course aims

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.

Marking

0-50 % Fx
51-60 % E
61-70 % D
71-80 % C
81-90 % B
91-100 % A

Course schedule

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)

Recommended literature

Emergent Simulator

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.

Useful links

Assesment details

Project Spiking Neurons - guidelines

The Simple Model of Spiking Neurons of Izhikevich (2003) holds a special position among the variety of spiking models proposed, namely because it is simple yet allowing to simulate a large spectrum of firing patterns of biological neurons. The task in this assignment is to study the original paper, explore various neuron firings and summarize your findings in a report. The report in PDF format is to be sent to the lecturer through email by the deadline. You can write you report either in English or Slovak. The emphasis of this project is on how you display the results and how you describe and explain the results in terms of clarity and quality of presentation. The assignments has 2 parts.

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)

Presentation - guidelines

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.