Neural Modeling

1
Neural Modeling
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• An Introduction to the course

2
Communication
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• Time Sundays Tuesdays 1330 to 1445
• Place EE 317 ( New building)
• Lecturer
• Bijan Vosoughi Vahdat
• Room VP of Student affairs, NE of Uni
• Office hours Sundays Tuesdays 930 to 1100
• Vahdat_at_sharif.edu
• http//sina.sharif.edu/vahdat
• Phone [phone]

3
Grading Policy
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• Homework 10
• Quiz 10
• Mid-term Exam 20
• Tuesday 29 Aban
• Final Exam 30
• Final project 20
• Due Date Tuesday 2 Bahman
• Paper Discussion 10
• Paper Preparation 10

4
Course Text
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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

5
First Email
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• Send an Email to me
• vahdat_at_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

6
Of neurons and engineers
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• Introduction
• What is a Neuron
• How to explain
• Devising an approach
• EXPLAINING NEURAL SYSTEMS

7
What is a Neuron
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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

8
How to explain
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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

9
Devising an approach
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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 engineers 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

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NEURAL SYSTEMS
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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

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NEURAL REPRESENTATION
Biomedical engineering Group School of Electrical
Engineering Sharif University of Technology

• 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