Temporal precision in the neural code and the timescales of natural vision

By Daniel A. Butts, C. Weng, Jianzhong Jin, Chun-I Yeh, N. Lesica, J. Alonso, Garrett B. Stanley. Nature, 2007

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Deep Learning in Spiking Neural Networks (2018, background): “This precise temporal pattern in spiking activity is considered as a crucial coding strategy in sensory information processing areas (Butts et al., 2007; Gollisch &Meister, 2008; Reinagel & Reid, 2000; Sinha et al., 2017; Victor, 2005) and neural motor control areas in the brain (Srivastava et al.”
Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, background): “, 2004), single spike generation (Butts et al., 2007; Carandini et al., 2007; Keat et al., 2001; Pillow et al., 2005), and burst generation (Lesica and Stanley, 2004; Sherman, 2001).”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, background): “Such an organization scheme for ocular dominance in the primary visual cortex can be related to spontaneous retinal waves (Wong 1999; Butts et al. 2007), which implies a correlation input structure for some timescale (Gjorgjieva et al. 2009).”