Summer course: Modeling and exploring of brain neurons and networks: how and why

Confirmed Speakers:

• Ruben A. Tikidji-Hamburyan – The George Washington University, DC, USA
• Boris Olifirov – Bogomoletz Institute of Physiology, Kyiv, Ukraine
• Ivan Blashchak – Bogomoletz Institute of Physiology, Kyiv, Ukraine
• Pavel Belan – Bogomoletz Institute of Physiology, Kyiv, Ukraine
• Nana Voitenko – Dobrobut Academy, Kyiv, Ukraine

Organizers:

• Anton Popov – Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute
• Ivan Zahorulko – Noosphere Engineering School, Igor Sikorsky Kyiv Polytechnic Institute

Course Description

The brain is one of the most complex objects known to us. It consists of very special cells called neurons: they are (1) the largest in size cells in our body, (2) the longest living cells in our body - many of them stay with us throughout our entire lives, and (3) they create astonishing and intricate interconnected structures – the brain networks. Although the brain is the least understood part of our body, extraordinary progress in understanding brain functioning has been achieved in the last 20-30 years. This progress cannot be imagined without mathematical and computational models - the research field of one of the youngest sciences on the planet, called Computational Neuroscience. Modeling neurons and neural networks is a truly multidisciplinary field requiring solid knowledge of neuroscience, many aspects of mathematics, and well-developed computer science skills. Of course, Computational Neuroscience is intrinsically related to Experimental Neuroscience, which is necessary to describe the processes in the brain that need to be modeled.

This summer course is an introduction to computational and experimental neuroscience, specifically to modeling neurons and brain networks. We will start with fundamental neuroscience and, right from the beginning, in the first lecture, will arrive at the “brain paradox” – the very reason why modeling is an essential and crucial tool to study the brain. In the second lecture, we will look at the art of model selection from very detailed to phenomenological, abstract, and theoretical. The following two lectures will be dedicated to examples illustrating the concept of discovery through modeling. On the last day of this summer course, we will introduce you to experimental approaches employed in modern Neuroscience research that are necessary for validating models developed in Computational Neuroscience. We will be centered on digital fluorescent imaging, confocal and 2-photon microscopy, electrophysiology, and optogenetics. The development of new research techniques based on the current advances in physics and engineering will also be surveyed.

Alongside the lectures, we will provide practical sessions where you can engage in hands-on activities, live coding, and conversational programming. These sessions will teach you how to create models using two of the most popular simulators, NEURON and Brian.

We aim to flatten the learning curve for anyone interested in learning how to study and model the brain.

[ Please fill this registration form ]

Course requirements

It is NOT necessary. However, the basic knowledge listed below will help you understand the course:

• physics and math
• programming in Python
• differential equations

Class schedule

10:00 am Lecture: Complexity of the system

1. Historical overview and general introduction: neurons, synapses, networks
2. Neuron excitability and nonlinearity
3. Synaptic transmission
4. Synaptic plasticity
5. The brain paradox, the spatial place of the neuroscience in the history of the science, and the critical role of computational neuroscience as the knowledge synthesizer.
   

10:00 am Lecture: The art of computational neuroscience: The complexity game

1. Hierarchy of complexity: spatial, temporal, and functional resolutions.
2. What model should we choose?
3. Details vs. computational resources: the triad off and informed decision
4. The luck of knowledge
5. From phenomenological to realistic, or vice versa?
6. Do we have any fundamentals?
   

11:30 am Hands-on: Introduction to NEURON

10:00 am Lecture: Example #1. Gamma oscillations: the oscillator, the moderator, and the resonator

1. Quick intro to the brain oscillations: alpha, beta, gamma, delta etc
2. Do we know the mechanisms?
3. Three models of gamma oscillations - but do they really match what we know?
4. Is something missing?
5. What are the different types of neuron excitability, and how do they relate to bifurcations in dynamic systems?
6. What is the difference between type 1 vs type 2 interneurons in gamma oscillations?
7. When is type 2 the answer?
8. “Oh now, clustering!” vs “Ah yes, clustering!” in type 2 inhibitory networks
9. the PING, the ING, and the RING
10. How is it useful for information processing?
11. There are lots of open options
    

11:30 am Hands-on: Introduction to Brian2

10:00 am Lecture: Example #3: enhance useful, suppress dangerous - how is the brain making itself?

1. Basic biology of cortical and subcortical development
2. How do connections from sensors grow to the brain
3. Visual map formation – who is playing this game?
4. Chaos on purpose - the chaos saves information
5. What happens in the cortex?
   

11:30 am Play ground with Python, NEURON, Brian2, XPP and GENESIS

10:00 am Lecture: Exploring neurons and neuronal networks using state-of-the-art approaches in physics and genetics.

1. Digital fluorescence microscopy, confocal and 2-photon imaging and molecular genetics in studies of neurons and neuronal networks
2. Optogenetic and electrophysiological studies of pain regulation
3. Development of new research methods for biomedical research
   

[ Please fill this registration form ]