Homogeneously connected neurons

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Table of contents

1 Introduction 
1.1 Neural Coding
1.2 E-I Balance
1.2.1 Theoretical Foundation
1.2.2 Balanced Recurrent Networks
1.2.3 Experimental Evidence of Balance
1.3 Computations
1.3.1 Handcrafted v. Generic Networks
1.3.2 Optimizing the Readout Weights
1.3.3 Optimizing the Recurrent Weights
1.4 Plasticity
1.4.1 Hebbian Learning
1.4.2 Timing and the Variety of Factors that Influence Plasticity
1.4.3 Plasticity and Functions
1.4.4 Local learning in Recurrent Networks
1.5 Objectives and Organization
2 Voltage-Rate Based Plasticity 
3 Learning an Auto-Encoder 
4 Learning a Linear Dynamical System 
5 Discussion 
5.1 Representing Information Within a Highly Cooperative Code
5.2 Fast Connections Enforce Efficiency by Learning to Balance Excitation and Inhibition
5.3 Learning an Autoencoder
5.4 Supervised learning
5.5 Open Questions