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M. Raihan
Email: rianku11@gmail.com
Neural Networks
Neural Networks
18-Sep-17 3
Neural Networks
18-Sep-17
We are born with about 100 billion
neurons
A neuron may connect to as many as
100,000 other neurons
4
Human Brain
18-Sep-17 5
What is Neuron
18-Sep-17
Neurons are the fundamental units of the brain
and nervous system.
The cells responsible for receiving sensory input
from the external world
For sending motor commands to our muscles
For transforming and relaying the electrical
signals at every step in between.
6
How does a Neuron look like
18-Sep-17
A useful analogy is to think of a neuron as a tree.
A neuron has three main parts: dendrites, an axon, and a cell body or soma
which can be represented as the branches, roots and trunk of a tree
respectively.
A dendrite (tree branch) is where a neuron receives input from other cells.
The axon (tree roots) is the output structure of the neuron; when a neuron
wants to talk to another neuron, it sends an electrical message called an
action potential throughout the entire axon.
The soma (tree trunk) is where the nucleus lies, where the neuron’s DNA is
housed, and where proteins are made to be transported throughout the
axon and dendrites.
7
Continue
18-Sep-17 8
Inspiration from Neurobiology
18-Sep-17
A neuron: many-inputs / one output unit.
Output can be excited or not excited.
Incoming signals from other neurons
determine if the neuron shall excite ("fire")
Output subject to attenuation in the
synapses, which are junction parts of the
neuron
9
Hebb’s Rule
18-Sep-17
If an input of a neuron is repeatedly and
persistently causing the neuron to fire, a
metabolic change happens in the synapse
of that particular input to reduce its
resistance.
10
McCulloch and Pitts Neurons
18-Sep-17
McCulloch & Pitts (1943) are generally
recognized as the designers of the first neural
network.
Many of their ideas still used today (e.g.
many simple units combine to give increased
computational power and the idea of a
threshold).
11
McCulloch and Pitts Neurons
18-Sep-17
Greatly simplified biological neurons
Sum the inputs
If total is less than some threshold, neuron fires
Otherwise does not
12
McCulloch and Pitts Neurons
18-Sep-17
The weight wj can be positive or negative
Inhibitory or excitatory.
Use only a linear sum of inputs.
No refractory period.
Use a simple output instead of a pulse (spike train)
13
Biologically Inspired
18-Sep-17
Electro-chemical signals.
Threshold output firing.
14
The Perceptron
18-Sep-17
Binary classifier functions.
Threshold activation function.
15
Limitations of
(McCulloch and Pitts Neurons Model)
18-Sep-17 16
Course Curriculum
18-Sep-17
Credit Hour : 03
3 class test will be taken.
17
Thank You
18-Sep-17 18

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Introduction to Neural Networks

  • 4. Neural Networks 18-Sep-17 We are born with about 100 billion neurons A neuron may connect to as many as 100,000 other neurons 4
  • 6. What is Neuron 18-Sep-17 Neurons are the fundamental units of the brain and nervous system. The cells responsible for receiving sensory input from the external world For sending motor commands to our muscles For transforming and relaying the electrical signals at every step in between. 6
  • 7. How does a Neuron look like 18-Sep-17 A useful analogy is to think of a neuron as a tree. A neuron has three main parts: dendrites, an axon, and a cell body or soma which can be represented as the branches, roots and trunk of a tree respectively. A dendrite (tree branch) is where a neuron receives input from other cells. The axon (tree roots) is the output structure of the neuron; when a neuron wants to talk to another neuron, it sends an electrical message called an action potential throughout the entire axon. The soma (tree trunk) is where the nucleus lies, where the neuron’s DNA is housed, and where proteins are made to be transported throughout the axon and dendrites. 7
  • 9. Inspiration from Neurobiology 18-Sep-17 A neuron: many-inputs / one output unit. Output can be excited or not excited. Incoming signals from other neurons determine if the neuron shall excite ("fire") Output subject to attenuation in the synapses, which are junction parts of the neuron 9
  • 10. Hebb’s Rule 18-Sep-17 If an input of a neuron is repeatedly and persistently causing the neuron to fire, a metabolic change happens in the synapse of that particular input to reduce its resistance. 10
  • 11. McCulloch and Pitts Neurons 18-Sep-17 McCulloch & Pitts (1943) are generally recognized as the designers of the first neural network. Many of their ideas still used today (e.g. many simple units combine to give increased computational power and the idea of a threshold). 11
  • 12. McCulloch and Pitts Neurons 18-Sep-17 Greatly simplified biological neurons Sum the inputs If total is less than some threshold, neuron fires Otherwise does not 12
  • 13. McCulloch and Pitts Neurons 18-Sep-17 The weight wj can be positive or negative Inhibitory or excitatory. Use only a linear sum of inputs. No refractory period. Use a simple output instead of a pulse (spike train) 13
  • 15. The Perceptron 18-Sep-17 Binary classifier functions. Threshold activation function. 15
  • 16. Limitations of (McCulloch and Pitts Neurons Model) 18-Sep-17 16
  • 17. Course Curriculum 18-Sep-17 Credit Hour : 03 3 class test will be taken. 17