Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy

Shirin Dora, Sander M. Bohte, Cyriel M. A. Pennartz

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Abstract

Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is trained using a gated Hebbian learning rule and mimics the feedforward and feedback connectivity of the cortex. After training on image datasets, the models formed latent representations in higher areas that allowed reconstruction of the original images. We analyzed low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increased systematically across ascending areas in the model hierarchy. Depending on the strength of regularization factors, sparseness also increased from lower to higher areas. The results suggest a rationale as to why experimental results on sparseness across the cortical hierarchy have been inconsistent. Finally, representations for different object classes became more distinguishable from lower to higher areas. Thus, deep neural networks trained using a gated Hebbian formulation of predictive coding can reproduce several properties associated with neuronal responses along the visual cortical hierarchy.
Original languageEnglish
Article number666131
Pages (from-to)1-20
Number of pages20
JournalFrontiers in computational Neuroscience
Volume15
Early online date28 Jul 2021
DOIs
Publication statusE-pub ahead of print - 28 Jul 2021

Keywords

  • Neuroscience
  • visual processing
  • predictive coding
  • deep biologically plausible learning
  • selectivity
  • sparseness
  • sensory neocortex
  • inference
  • representation learning

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