Neural substrate of dynamic Bayesian inference in the cerebral cortex

Abstract

Dynamic Bayesian inference allows a system to infer the environmental state under conditions of limited sensory observation. Using a goal-reaching task, we found that posterior parietal cortex (PPC) and adjacent posteromedial cortex (PM) implemented the two fundamental features of dynamic Bayesian inference: prediction of hidden states using an internal state transition model and updating the prediction with new sensory evidence. We optically imaged the activity of neurons in mouse PPC and PM layers 2, 3 and 5 in an acoustic virtual-reality system. As mice approached a reward site, anticipatory licking increased even when sound cues were intermittently presented; this was disturbed by PPC silencing. Probabilistic population decoding revealed that neurons in PPC and PM represented goal distances during sound omission (prediction), particularly in PPC layers 3 and 5, and prediction improved with the observation of cue sounds (updating). Our results illustrate how cerebral cortex realizes mental simulation using an action-dependent dynamic model.

Future state prediction errors guide active avoidance behavior by adult zebrafish

“…Our results are in good agreement with the data from other groups that the zebrafish brain is capable of the predictive coding as those of other higher vertebrates (Attinger et al, 2017;Chao et al, 2018;Funamizu et al, 2016;Makino and Komiyama, 2015;Rao and Ballard, 1999;Schneider et al, 2018;Schwiedrzik and Freiwald, 2017;Sumbre et al, 2008).…”

Bayesian computation through cortical latent dynamics

“…For example, some studies have proposed that the stochastic nature of sensory representations provide the means to implicitly encode sensory likelihoods 81,82 . Others have shown that task-related firing rates of single neurons before the presentation of sensory information may be modulated by prior expectations [83][84][85] , and firing rates after the presentation of sensory information may reflect Bayesian estimate of behaviorally-relevant variables 30,[86][87][88] . There have also been attempts to apply reliability-weighted linear updating schemescommonly used in cue combination studies [89][90][91] -to explain how single-neuron firing rates might combine sensory evidence with prior expectations 92,93 .…”

Development of PTSD-like behavior in adult mice after observing an acute traumatic event

“…This is in agreement with previous finding that the right but not the left ACC controls observational fear learning in mice (Kim et al, 2012). The brain heavily integrates not only external but also internal causes (Donaldson et al, 2015; Funamizu et al, 2016; Kohl et al, 2018; Larkum, 2013; Lee et al, 2014; R. X. Lee et al, 2015; Matias et al, 2017; Murugan et al, 2017; Remedios et al, 2017; Roome and Kuhn, 2018; Tononi et al, 2016; Zelikowsky et al, 2018).…”