1. Neural Modeling - Fall 13861 Neural Modeling An Introduction to the course Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

2. Neural Modeling - Fall 13862 Communication Time: Sundays & Tuesdays 13:30 to 14:45 Place: EE 317 ( New building) Lecturer: Bijan Vosoughi Vahdat Room: VP of Student affairs, NE of Uni Office hours: Sundays & Tuesdays 9:30 to 11:00 Vahdat@sharif.edu http://sina.sharif.edu/~vahdat Phone: (6616) 5001 Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

3. Neural Modeling - Fall 13863 Grading Policy Homework: 10 Quiz: 10 Mid-term Exam 20 Tuesday 29 Aban Final Exam: 30 Final project20project Due Date: Tuesday 2 Bahman Paper Discussion: 10 Paper Paper Preparation:+10 Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

4. Neural Modeling - Fall 13864 Course Text Neural Engineering Computation, Representation, and Dynamics in Neurobiological Systems Chris Eliasmith and Charles H. Anderson The MIT Press Download it from here The Slides are on this PC Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

5. Neural Modeling - Fall 13865 First Email Send an Email to me vahdat@sharif.edu Subject: NeuroScience1386 Greeting IDxxxxxxxx Contents: Full name (Identify how do you like to be called ) Student ID Email Address (if more than one please identify) Phone (Cell + home) Photo Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

6. Neural Modeling - Fall 13866 Of neurons and engineers Introduction What is a Neuron How to explain Devising an approach EXPLAINING NEURAL SYSTEMS Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

7. Neural Modeling - Fall 13867 What is a Neuron An excitable Cell Axon: 100 microns (typical granule cell) to 15 feet (Giraffe primary afferents) Communication with/without spikes (Pyramidal/ Retinal) Speed 2 to 400 km/h Inputs from 500 to 200,000 1,000 kinds of different neurons 10,000,000,000 neurons in Brain 10,000,000,000,000 synapses 100 different kinds of neurotransmitters 45 miles of fiber in the human brain Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

8. Neural Modeling - Fall 13868 How to explain Engineering Tools Physics Mathematics Pure logic Considering brains as purely physical Information theory Control theory Signal processing theory Two Kinds of questions Neuroscience: how neurons give rise to brain function. Engineers: A neuron functionality and NS Structure Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

9. Neural Modeling - Fall 13869 Devising an approach Actual neural systems Natural physical systems Not been designed to function like theoretical computational systems such as Turing machines Still computational theory is useful Adopt and adapt the engineer’s tools Design Constraints due to real world A synthesis of the available approaches to understanding the brain Not trying to provide new tools but to articulate a new way to use them Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

10. Neural Modeling - Fall 138610 NEURAL SYSTEMS Amazingly profesion at solving problems Seagulls and Shellfish Bees and finding their ways Rats and sense of direction Explanation: Representation Serving to relate the internal state of the animal to its environment Can be manipulated internally without manipulating the actual, external, represented object. Penfild Observations Transformation Exploiting representations Updating Manipulating Relating Explaining how neurobiological systems represent the world, and how they use those representations, via transformations, to guide behavior Biomedical engineering Group School of Electrical Engineering Sharif University of Technology

11. Neural Modeling - Fall 138611 NEURAL REPRESENTATION The main problem is to determine the exact nature of the representation relation; that is, to specify the relation between things ‘inside the head’ and things ‘outside the head’. We define The representational relationship To see if it does the explanatory work that is needed A close tie between neural representations as understood by neuroscientists and codes as understood by communications engineers Neural firings encode properties of external stimuli Decoding procedure: Biomedical engineering Group School of Electrical Engineering Sharif University of Technology