COMPUTATIONAL NEUROSCIENCE

AIM

The objective of the course is to evaluate encoding and decoding neural information using mathematical models and statistical analysis methods.

CONTENT

This course contains; What is computational neuroscience?,Brain regions, neuron, synapses,Types of neurons. Neurophysiology.,Hodgkin–Huxley model,Integrate-and-fire neurons,Izhikevich neuron model,Synaptic interaction models,Spike-timing-dependent plasticity,Neuromodulation,Supervised & unsupervised learning,Reinforcement learning,Oscillations,Neuronal network simulation,The future: challenges and open problems.

1. 1

   1. will be able to explain how neurons encode information.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

2. 2

   1.1. defines the brain regions and cytoarchitecture of neurons.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

3. 3

   1.2. assesses the relationship between the cytoarchitecture of the neurons and their membrane potential dynamics.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

4. 4

   2. will be able to quantitatively evaluate what a given component of a neural system is doing based on experimental data.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

5. 5

   2.1. formulates the computational principles in neuronal networks.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

6. 6

   2.2. assesses statistical relationship between experimental neuronal data and the behavior and/or stimulus.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

7. 7

   3. will be able to simulate neuronal networks.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

8. 8

   3.1. formulates the computational principles in neuronal networks.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

9. 9

   3.2. compares the dynamics of simulated neuronal network with experimental data.

   Taught by: Discussion Method, Problem Solving Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Simulation Technique, Lecture Method · Assessed by: Homework, Project Task

WEEKLY PLAN

1. WEEK 1

   What is computational neuroscience?

2. WEEK 2

   Brain regions, neuron, synapses

3. WEEK 3

   Types of neurons. Neurophysiology.

4. WEEK 4

   Hodgkin–Huxley model

5. WEEK 5

   Integrate-and-fire neurons

6. WEEK 6

   Izhikevich neuron model

7. WEEK 7

   Synaptic interaction models

8. WEEK 8

   Spike-timing-dependent plasticity

9. WEEK 9

   Neuromodulation

10. WEEK 10

    Supervised & unsupervised learning

11. WEEK 11

    Reinforcement learning

12. WEEK 12

    Oscillations

13. WEEK 13

    Neuronal network simulation

14. WEEK 14

    The future: challenges and open problems

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge.
• Katz, B. F., (2008) Neuroengineering the future, Infinity Science Press, Ingham. Berger, T.W., Glanzman, D. L., (2005) Toward replacement parts for the brain implantable biomimetic electronics as neural prostheses, MIT Press, Cambridge Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience.

TEACHING STAFF

• Assist.Prof. Mehmet KOCATÜRKCOORDINATOR
• Assist.Prof. Mehmet KOCATÜRK