Local Information Dynamics at the Retinogeniculate Synapse of the Cat

In neuroscience, predictive coding theory (PCT) arguably has become the most comprehensive theory of brain functioning, action and perception. PCT proposes that the brain exploits statistical regularities in its input to facilitate perception. Exploiting regularities is thought to happen (i) either by passing on sensory evidence matching internal predictions (reliability coding), (ii) or by passing on surprising sensory evidence not matching predictions (error coding). It is typically not known when and where in the brain a mismatch between sensory evidence and prediction occurs. Accordingly, when experimentally testing the two strategies, we can not be certain if an observed neural signal reflects a matching prediction or a prediction error. The interpretation of the neural signal thus often depends on the experimenter's a-priori belief which strategy the system uses. What is needed, therefore, is a way of testing the type of coding without relying on the semantics of the neural signals.

In this talk, I will present the framework of local information dynamics as one way to investigate neural coding in a semantics-free fashion. In this framework, local active information storage (LAIS) quantifies the predictable information in a time series, while local transfer entropy (LTE) quantifies the transferred information, for every sample. We applied these measures to existing spike train recordings from 17 pairs of retinal ganglion cells (RGC) and lateral geniculate nucleus (LGN) cells in the anesthetized cat. By evaluating the correlation between LAIS and LTE, we tested whether the synapse preferentially transferred predictable or surprising information, which allowed us to distinguish reliability coding from error coding. For computing the information theoretic measures we used discrete estimators implemented in the JIDT toolbox together with a localized bias correction. We found a positive correlation of LAIS and LTE in all cell pairs, which was stronger for pairs with a strong connection strength (contribution). This suggests that retinal inputs to LGN cells are preferentially passed on when reliable. We believe that our framework will be useful to improve the understanding of PCT and of neural information processing in general.