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 onelayered network 
3 

5.2 Higher order network structure 
3 

5.3 Interval coding of continuous valued input 
3 

5.4 SelfOrganization of internal representation 
3 

6. 
Discussion and Conclusions 
3 