A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs

Shirin Dora, Cyriel Pennartz, Sander Bohte

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
50 Downloads (Pure)

Abstract

Predictive coding has been argued as a mechanism underlying sensory processing in the brain. In computational models of predictive coding, the brain is described as a machine that constructs and continuously adapts a generative model based on the stimuli received from external environment. It uses this model to infer causes that generated the received stimuli. However, it is not clear how predictive coding can be used to construct deep neural network models of the brain while complying with the architectural constraints imposed by the brain. Here, we describe an algorithm to construct a deep generative model that can be used to infer causes behind the stimuli received from external environment. Specifically, we train a deep neural network on real-world images in an unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the causes inferred using the trained model on images of objects that are not used in training. Despite the novel features of these objects the model is able to infer the causes for them. Furthermore, the reconstructions of the original images obtained from the generative model using these inferred causes preserve important details of these objects.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
EditorsYannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis
PublisherSpringer Cham
Pages457-467
Number of pages11
ISBN (Electronic)978-3-030-01424-7
ISBN (Print)978-3-030-01423-0
DOIs
Publication statusE-pub ahead of print - 27 Sep 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 4 Oct 20187 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11141 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Artificial Neural Networks, ICANN 2018
CountryGreece
CityRhodes
Period4/10/187/10/18

Keywords

  • Deep generative models
  • Predictive coding

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  • Cite this

    Dora, S., Pennartz, C., & Bohte, S. (2018). A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs. In Y. Manolopoulos, B. Hammer, V. Kurkova, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 457-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11141 LNCS). Springer Cham. https://doi.org/10.1007/978-3-030-01424-7_45