Abstract
Neurons and synapses are the basic building units of the brain. They form neural circuits of various structures and hence implement different functions. Understanding how neural networks achieve brain functions is at the core of modeling studies. In this chapter, we will introduce some network models, including classical Hopfield model, continuous attractor neural network, and reservoir network. We will also discuss the studies on how short-term plasticity of neuronal synapses affects the dynamics and computations of a neural network.
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Liang, P., Wu, S., Gu, F. (2016). Network Models of Neural Information Processing. In: An Introduction to Neural Information Processing. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7393-5_6
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DOI: https://doi.org/10.1007/978-94-017-7393-5_6
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