Topic: hierarchical predictive coding

Understanding neural information processing is extremely challenging. This is because often, it is not even clear what information neurons are really processing. However, we can access the dynamic fingerprint of the processing with information-theoretic and statistical methods. Recent work has thus focused on the autocorrelation time of neural activity, also termed intrinsic timescale, which serves as a proxy for how long information is stored in the activity of the network. This work revealed a hierarchy of intrinsic timescales, which suggests that along the processing hierarchy, the brain

Hierarchical predictive coding (hPC) is one of the major theories of sensory processing and cortical computation. It provides a comprehensive explanation of the role of top-down connections in cortex, by proposing how they can guide sensory processing, and has inspired many experimental studies, ranging from electrophysiological studies of individual neurons to high-level studies of cognitive computation. Still, after twenty years of research, important questions remain open: Experimental evidence for error units, which are central to the theory, is inconclusive, and little theoretical work ex