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Fig. 3.1. Computational abstractions of neurons and networks. Biological neurons can be modeled at different levels of abstraction depending on the scale of the phenomena studied. (a) A microscopic image of pyramidal cells in a 1.4 mm × 0.7 mm area of layer III in macaque temporo-occipital (TEO) area, injected individually with Lucifer Yellow (reprinted with permission from Elston and Rosa 1998, copyright 1998 by Oxford University Press; circle added). Although this technique shows only a fraction of the neurons in a single horizontal cross-section, it demonstrates the complex structure of individual neurons and their connectivity. (b) A detailed compartmental model of the top left neuron (circled). Each compartment represents a small segment of the dendrite, and connections are established on the small dendritic spines, shown as line segments. (c) A coupled oscillator model of the neuron, consisting of an excitatory and an inhibitory unit with recurrent coupling, and weighted connections with other neurons in the network. (d) A model where a single variable describes the activation of the neuron, corresponding to either the membrane potential (in the integrate-and-fire model), or the average number of spikes per unit time (in the firing-rate model). (e) A high-level model of a neuronal network. With the more abstract neurons, it is possible to simulate a number of neurons and connections, allowing us to study phenomena at the level of networks and maps.