ArticleFrontiers in computational neuroscience2011
Development of maps of simple and complex cells in the primary visual cortex.
Article in Frontiers in computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 50 citations in OpenAlex.
- Latent encoding of movement in primary visual cortex.bioRxiv : the preprint server for biology · 2025Article
- The developmental emergence of reliable cortical representations.Nature neuroscience · 2025Article
- A comprehensive data-driven model of cat primary visual cortex.PLoS computational biology · 2024Article
- Spatial Attention Modulates Neuronal Interactions between Simple and Complex Cells in V1.International journal of molecular sciences · 2023Article
- Pooling strategies in V1 can account for the functional and structural diversity across species.PLoS computational biology · 2022Article
- A theory of cortical map formation in the visual brain.Nature communications · 2022Article
- Artificial Visual System for Orientation Detection Based on Hubel-Wiesel Model.Brain sciences · 2022Article
- Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex.Frontiers in cellular neuroscience · 2022Article
- An Anatomically Constrained Model of V1 Simple Cells Predicts the Coexistence of Push-Pull and Broad Inhibition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Learning receptive field properties of complex cells in V1.PLoS computational biology · 2021Article
- A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics.Frontiers in neural circuits · 2020Article
- Describing complex cells in primary visual cortex: a comparison of context and multifilter LN models.Journal of neurophysiology · 2018Article
- The influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspective.PLoS computational biology · 2017Article
- Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps.Frontiers in neural circuits · 2017Article
- A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?Frontiers in neural circuits · 2016Article
- Neurons in cat V1 show significant clustering by degree of tuning.Journal of neurophysiology · 2015Article
- Comprehensive chronic laminar single-unit, multi-unit, and local field potential recording performance with planar single shank electrode arrays.Journal of neuroscience methods · 2015Article
- Multimap formation in visual cortex.Journal of vision · 2015Article
- Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
- Developmental regulation of spatio-temporal patterns of cortical circuit activation.Frontiers in cellular neuroscience · 2012Article
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hubel and Wiesel (1962) classified primary visual cortex (V1) neurons as either simple, with responses modulated by the spatial phase of a sine grating, or complex, i.e., largely phase invariant. Much progress has been made in understanding how simple-cells develop, and there are now detailed computational models establishing how they can form topographic maps ordered by orientation preference. There are also models of how complex cells can develop using outputs from simple cells with different phase preferences, but no model of how a topographic orientation map of complex cells could be formed based on the actual connectivity patterns found in V1. Addressing this question is important, because the majority of existing developmental models of simple-cell maps group neurons selective to similar spatial phases together, which is contrary to experimental evidence, and makes it difficult to construct complex cells. Overcoming this limitation is not trivial, because mechanisms responsible for map development drive receptive fields (RF) of nearby neurons to be highly correlated, while co-oriented RFs of opposite phases are anti-correlated. In this work, we model V1 as two topographically organized sheets representing cortical layer 4 and 2/3. Only layer 4 receives direct thalamic input. Both sheets are connected with narrow feed-forward and feedback connectivity. Only layer 2/3 contains strong long-range lateral connectivity, in line with current anatomical findings. Initially all weights in the model are random, and each is modified via a Hebbian learning rule. The model develops smooth, matching, orientation preference maps in both sheets. Layer 4 units become simple cells, with phase preference arranged randomly, while those in layer 2/3 are primarily complex cells. To our knowledge this model is the first explaining how simple cells can develop with random phase preference, and how maps of complex cells can develop, using only realistic patterns of connectivity.
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