Transcript Neural Modeling An Introduction to the course Neural Modeling - Fall 1386 1

Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Neural Modeling
An Introduction to the course
Neural Modeling - Fall 1386
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Communication
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Time: Sundays & Tuesdays 13:30 to 14:45
Place: EE 317 ( New building)
Lecturer:
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Bijan Vosoughi Vahdat
Room: VP of Student affairs, NE of Uni
Office hours: Sundays & Tuesdays 9:30 to 11:00
[email protected]
http://sina.sharif.edu/~vahdat
Phone: (6616) 5001
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Grading Policy
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Homework:
Quiz:
Mid-term Exam
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Final Exam:
Final project
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Tuesday 29 Aban
Due Date: Tuesday 2 Bahman
Paper Discussion:
Paper Preparation:
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Course Text
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Neural Engineering
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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
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
First Email
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Send an Email to me
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[email protected]
Subject: NeuroScience1386 Greeting IDxxxxxxxx
Contents:
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Full name (Identify how do you like to be called )
Student ID
Email Address (if more than one please identify)
Phone (Cell + home)
Photo
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Of neurons and engineers
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Introduction
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What is a Neuron
How to explain
Devising an approach
EXPLAINING NEURAL
SYSTEMS
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
What is a Neuron
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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
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
How to explain
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Engineering Tools
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Considering brains as purely physical
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Physics
Mathematics
Pure logic
Information theory
Control theory
Signal processing theory
Two Kinds of questions
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Neuroscience: how neurons give rise to brain function.
Engineers: A neuron functionality and NS Structure
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
Devising an approach
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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
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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
NEURAL SYSTEMS
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Amazingly profesion at solving problems
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Explanation: Representation
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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
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Seagulls and Shellfish
Bees and finding their ways
Rats and sense of direction
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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Biomedical engineering Group
School of Electrical Engineering
Sharif University of Technology
NEURAL REPRESENTATION
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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
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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:
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