1. Introduction 63

1.1 Outline 63

1.2 Universals versus realizations in the study of learning and memory 64

2. Large random cortical networks developing ex vivo 65

2.1 Preparation 65

2.2 Measuring electrical activity 67

3. Spontaneous development 69

3.1 Activity 69

3.2 Connectivity 70

4. Consequences of spontaneous activity: pharmacological manipulations 72

4.1 Structural consequences 72

4.2 Functional consequences 73

5. Effects of stimulation 74

5.1 Response to focal stimulation 74

5.2 Stimulation-induced changes in connectivity 74

6. Embedding functionality in real neural networks 77

6.1 Facing the physiological definition of ‘reward’: two classes of theories 78

6.2 Closing the loop 79

7. Concluding remarks 84

8. Acknowledgments 85

9. References 85

The phenomena of learning and memory are inherent to neural systems that differ from each other markedly. The differences, at the molecular, cellular and anatomical levels, reflect the wealth of possible instantiations of two neural learning and memory universals: (i) an extensive functional connectivity that enables a large repertoire of possible responses to stimuli; and (ii) sensitivity of the functional connectivity to activity, allowing for selection of adaptive responses. These universals can now be fully realized in ex-vivo developing neuronal networks due to advances in multi-electrode recording techniques and desktop computing. Applied to the study of ex-vivo networks of neurons, these approaches provide a unique view into learning and memory in networks, over a wide range of spatio-temporal scales. In this review, we summarize experimental data obtained from large random developing ex-vivo cortical networks. We describe how these networks are prepared, their structure, stages of functional development, and the forms of spontaneous activity they exhibit (Sections 2–4). In Section 5 we describe studies that seek to characterize the rules of activity-dependent changes in neural ensembles and their relation to monosynaptic rules. In Section 6, we demonstrate that it is possible to embed functionality into ex-vivo networks, that is, to teach them to perform desired firing patterns in both time and space. This requires ‘closing a loop’ between the network and the environment. Section 7 emphasizes the potential of ex-vivo developing cortical networks in the study of neural learning and memory universals. This may be achieved by combining closed loop experiments and ensemble-defined rules of activity-dependent change.