Efficient codes and balanced networks

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SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (2017, background): “the idea that individual spike timing may carry additional information which could be crucial for efficient coding (Thalmeier et al., 2015; Denève and Machens, 2016; Abbott et al., 2016; Brendel et al., 2017) and fast computation (Thorpe et al.”
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks (2019, background): “The latter are typically defined as linear combinations of low-pass filtered spike trains [Gilra and Gerstner, 2017; Nicola and Clopath, 2017; Eliasmith and Anderson, 2004; Denève and Machens, 2016; Abbott et al., 2016].”
Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex (2018, unknown): “R2 = 1− Var( fi, j(t)− f̄i(t)) Var( fi, j(t)) (2)”
Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study (2022, background): “Auditory verbal hallucinations has been found to be related to the alternation of neural activities (2), such as excitatory–inhibitory (E/I) balance (3).”