Reading directives for

A Higher Order Bayesian Neural Network with Spiking Units

by A. Lansner and A. Holst, 1996

1 = Important; 2 = Partly important; 3 = For orientation;

1.

Introduction

2

2.

The Naive Bayesian Model

1

3.

The Higher Order Bayesian Model

1
 

3.1 The Hypercolumn Structure

1

 

3.2 Formation of the hypercolumn layer

1

 

3.3 A real world example - fault diagnosis

1

4.

Uncertainty and Continuous Valued Output

2
 

4.1 Graded input and spiking units

2

 

4.2 Representing continuous valued input

2

 

4.3 Evaluation with the Iris database

3

5.

The Biological Connection

3
 

5.1 The basic one-layered network

3

 

5.2 Higher order network structure

3

 

5.3 Interval coding of continuous valued input

3

 

5.4 Self-Organization of internal representation

3

6.

Discussion and Conclusions

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