ArticleFrontiers in computational neuroscience2021
Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding.PLoS computational biology · 2025Article
- Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model.PLoS computational biology · 2025Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Predictive coding with spiking neurons and feedforward gist signaling.Frontiers in computational neuroscience · 2024Article
- How deep is the brain? The shallow brain hypothesis.Nature reviews. Neuroscience · 2023Review
- How 'visual' is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception.Frontiers in computational neuroscience · 2023Article
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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.
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