Technical Reports - Query ResultsYour query term was 'number = 2012-08'
1 report found
- OFAI-TR-2012-08 ( 1056kB PDF file)
The Hippocampal-Entorhinal Complex performs Bayesian Localization and Error Correction
- T Madl, S Franklin, K Chen, D Montaldi, R Trappl
- The mammalian brain updates representations of spatial location with self-motion cues, a process referred to as path integration. Since self-motion information is inherently inexact and subject to neuronal noise, this process leads to errors, which would accumulate over time if not corrected by sensory information about the environment. In this paper, we propose that the hippocampal-entorhinal complex, the major neuronal correlate representing spatial information, corrects such errors by integrating self-motion information and sensory information about the environment in a Bayes-optimal manner. Based on theoretical arguments as well as empirical data, we propose that hippocampal place cells are able to encode probability distributions and uncertainties of allocentric spatial location, and to use them for Bayesian inference to improve the accuracy of the location representation using different sources of information. We hypothesize about possible neuronal correlates of the components and processes required for such inference. Unlike most previously suggested error correction and spatial cue integration mechanisms, we not only provide a plausible neuronal basis for these mechanisms but also generate concrete predictions from our hypotheses and substantiate them with empirical data. We describe a computational model performing Bayesian localization in arbitrary two-dimensional environments in a biologically plausible way, and use it to replicate neuronal recording data as well as behaviour data in published studies in order to strengthen our claims. Our ideas tie in with a growing body of research suggesting that the brain might behave like a Bayesian machine (the Bayesian brain hypothesis ), and provides empirical evidence suggesting that it might employ Bayesian processes on the level of neuronal implementation.