ArticleFrontiers in neural circuits2018
A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex.
Article in Frontiers in neural circuits, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 147 citations in OpenAlex.
- Simultaneous detection and estimation in olfactory sensing.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
- Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories.eLife · 2025Article
- Robust variability of grid cell properties within individual grid modules enhances encoding of local space.eLife · 2025Article
- Average miniature post-synaptic potential size is inversely proportional to membrane capacitance across neocortical pyramidal neurons of different sizes.Frontiers in cellular neuroscience · 2025Article
- Robust variability of grid cell properties within individual grid modules enhances encoding of local space.bioRxiv : the preprint server for biology · 2024Article
- A mechanism for deviance detection and contextual routing in the thalamus: a review and theoretical proposal.Frontiers in neuroscience · 2024Article
- The emergence of enhanced intelligence in a brain-inspired cognitive architecture.Frontiers in computational neuroscience · 2024Article
- An operating principle of the cerebral cortex, and a cellular mechanism for attentional trial-and-error pattern learning and useful classification extraction.Frontiers in neural circuits · 2024Article
- Empirical comparison of deep learning models for fNIRS pain decoding.Frontiers in neuroinformatics · 2024Article
- Four attributes of intelligence, a thousand questions.Biological cybernetics · 2023Article
- Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars.PNAS nexus · 2023Article
- The Quality and Utility of Artificial Intelligence in Patient Care.Deutsches Arzteblatt international · 2023Review
- Symmetry and complexity in object-centric deep active inference models.Interface focus · 2023Article
- IoT and Deep Learning-Based Farmer Safety System.Sensors (Basel, Switzerland) · 2023Article
- Empirically validated theoretical analysis of visual-spatial perception under change of nervous system arousal.Frontiers in computational neuroscience · 2023Article
- Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM.Frontiers in psychiatry · 2023Article
- How to build a cognitive map.Nature neuroscience · 2022Review
- Topographically organized representation of space and context in the medial prefrontal cortex.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Are Grid-Like Representations a Component of All Perception and Cognition?Frontiers in neural circuits · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.
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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.