Creutzig, F and Sprekeler, H (2008) Predictive coding and the slowness principle: an information-theoretic approach. Neural Comput, 20. pp. 1026-1041. ISSN 0899-7667Full text not available from this repository.
Understanding the guiding principles of sensory coding strategies is a main goal in computational neuroscience. Among others, the principles of predictive coding and slowness appear to capture aspects of sensory processing. Predictive coding postulates that sensory systems are adapted to the structure of their input signals such that information about future inputs is encoded. Slow feature analysis (SFA) is a method for extracting slowly varying components from quickly varying input signals, thereby learning temporally invariant features. Here, we use the information bottleneck method to state an information-theoretic objective function for temporally local predictive coding. We then show that the linear case of SFA can be interpreted as a variant of predictive coding that maximizes the mutual information between the current output of the system and the input signal in the next time step. This demonstrates that the slowness principle and predictive coding are intimately related.
|Uncontrolled Keywords:||Action Potentials Afferent Pathways Animals Cerebral Cortex Computer Simulation Humans Linear Models Neural Networks (Computer) Neurons Sensation Signal Processing, Computer-Assisted Time Factors|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:13|
|Last Modified:||26 Mar 2017 03:59|