7. Models of network dynamic and learning

Neurons communicate with each other with vastly distributed synaptic connections. Each neuron might be connected to 1,000 other neurons, creating an amazingly complex dynamic. In Chapter 1.1, one of the most important paradigm shifts in neuroscience was described: the advancement from the neuron doctrine to neural networks. In neural networks, a function emerges from the joint activation patterns of the interconnected neurons. These neuron ensembles can generate emergent functional states which cannot be observed by studying the single entities they comprise. Importantly, as was described in Section 1.2.2, morpho- logically higher-order structures imposed by the morphological diversity within neuronal types impact emergent network activity. This chapter will demonstrate how detailed neural networks are described and simulated and how networks are utilized for learning.