ArticleFrontiers in neural circuits2017
A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.
Article in Frontiers in neural circuits, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 159 citations in OpenAlex.
- An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity.Neuroinformatics · 2021Pooled it
- Functional Clusters for Shape, Texture, and Motion Encoding in Macaque V2.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- TEVSER: a theory of evolving self-representations.Frontiers in human neuroscience · 2026Article
- Functional clusters for shape, texture, and motion encoding in macaque V2.bioRxiv : the preprint server for biology · 2025Article
- Visuomotor Control Accuracy of Circular Tracking Movement According to Visual Information in Virtual Space.Sensors (Basel, Switzerland) · 2025Article
- Simulation of evoked responses to transcranial magnetic stimulation using a multiscale cortical circuit model.bioRxiv : the preprint server for biology · 2025Article
- Perceptual and semantic maps in individual humans share structural features that predict creative abilities.Communications psychology · 2025Article
- A non-Hebbian code for episodic memory.Science advances · 2025Article
- FOCUS: object-centric world models for robotic manipulation.Frontiers in neurorobotics · 2025Article
- Bayesian Mechanics of Synaptic Learning Under the Free-Energy Principle.Entropy (Basel, Switzerland) · 2024Article
- Predictive learning shapes the representational geometry of the human brain.Nature communications · 2024Article
- Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.Entropy (Basel, Switzerland) · 2024Article
- Need for cross-level iterative re-entry in models of visual processing.Psychonomic bulletin & review · 2024Article
- NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.Sensors (Basel, Switzerland) · 2024Article
- Sensorimotor synchronization with visual, auditory, and tactile modalities.Psychological research · 2023Article
- Coherent mapping of position and head direction across auditory and visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Free energy and inference in living systems.Interface focus · 2023Article
- IoT and Deep Learning-Based Farmer Safety System.Sensors (Basel, Switzerland) · 2023Article
- Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM.Frontiers in psychiatry · 2023Article
- Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments.Brain informatics · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.