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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EEE 5133 NEURAL COMPUTING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR MEHMET KUNTALP

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH) ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH) ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

This course will provide the students with knowledge on how the nervous system works, how it accomplishes different computational functions and how it learns

Learning Outcomes of the Course Unit

1   Understand the structural organization of the nervous system. 2   Understand the principles of computation in neural systems. 3   Learn about artificial neural models 4   Learn about deep networks 5   Learn about neuromorphic architectures

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction 2 Computational Properties of the Brain 3 Electrical Signaling in the Nervous System 4 Coding in the Nervous System 5 Synaptic Plasticity 6 High-Level Processing in the Brain 7 Midterm 8 Simple Neuronal Systems 9 Artificial Neural Networks 10 Visual Signal Processing 11 Auditory Signal Processing 12 Cerebellum as a Neuronal Machine 13 Recent Advancements 14 Review

Recomended or Required Reading

will be posted

Planned Learning Activities and Teaching Methods

Lectures will be taught by instructor presentations

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 COM 1 COMMITTEE 1 2 COM COMMITTEE 3 FCG FINAL COURSE GRADE COM 1* 0.40 + COM * 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcomes will be evaluated by exam questions.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

mehmet.kuntalp@deu.edu.tr

Office Hours

will be posted

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparing assignments 0 0 0 Preparation for final exam 1 35 35 Preparation for midterm exam 1 20 20 Preparations before/after weekly lectures 13 6 78 Final 1 4 4 Midterm 1 2 2 TOTAL WORKLOAD (hours) 178

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 PO.14 PO.15 LO.1 2 1 LO.2 2 2 2 1 LO.3 2 4 4 3 3 LO.4 LO.5