Published online by Cambridge University Press: 30 November 2019
An attractor network is used in computational neuroscience to model the neuronal processes important for cognitive functions such as memory as well as motor behaviors. These networks are composed of neurons with excitatory interconnections that can settle into a stable pattern of firing. This chapter describes how attractor networks in the cerebral cortex are important for short- and long-term memory, attention, and decision-making. It then discusses how the random firing of neurons can influence the stability of these networks by introducing stochastic noise, and how these effects are involved in probabilistic decision-making and are implicated in some disorders of cortical function, such as poor short-term memory, attention, and alterations of cognitive functions with aging. Further, this chapter describes how alterations in transmitters that occur with aging, including acetylcholine, dopamine, and norepinephrine, can impair the stability of these memory networks, resulting in poor memory and attention. This computational neuroscience approach has implications for treatment.