#visualcortex — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #visualcortex, aggregated by home.social.
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"Thalamic activation of the visual cortex at the single-synapse level", Chen et al. 2026.
https://www.science.org/doi/full/10.1126/science.aec9923There was earlier work using a much coarser technique which found the same result:
"Specificity of monosynaptic connections from thalamus to visual cortex", Reid and Alonso, 1995.
https://www.nature.com/articles/378281a0Compare figure 3 in Reid & Alonso 1995 with Fig 5E in Chen et al. 2026.
But as Chen et al. 2026 says:
"Earlier experiments that used paired recordings of an orientation-selective L4 neuron in the cat visual cortex and a corresponding dLGN cell (5 [Reid and Alonso, 1995]) suggested, but could not prove, a synaptic connectivity scheme consistent with the H&W model. Our approach enabled the identification and characterization of nearly the complete set of active dLGN synaptic inputs to a single orientation-selective L4 neuron. The spatial alignment of receptive field centers of dLGN inputs matched the preferred orientation of the corresponding neuron. Thus, our findings directly validate core predictions of the connectivity proposed by the H&W [Hubel and Wiesel's] model."
By the way, Clay Reid was a trainee of H&W.
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"Thalamic activation of the visual cortex at the single-synapse level", Chen et al. 2026.
https://www.science.org/doi/full/10.1126/science.aec9923There was earlier work using a much coarser technique which found the same result:
"Specificity of monosynaptic connections from thalamus to visual cortex", Reid and Alonso, 1995.
https://www.nature.com/articles/378281a0Compare figure 3 in Reid & Alonso 1995 with Fig 5E in Chen et al. 2026.
But as Chen et al. 2026 says:
"Earlier experiments that used paired recordings of an orientation-selective L4 neuron in the cat visual cortex and a corresponding dLGN cell (5 [Reid and Alonso, 1995]) suggested, but could not prove, a synaptic connectivity scheme consistent with the H&W model. Our approach enabled the identification and characterization of nearly the complete set of active dLGN synaptic inputs to a single orientation-selective L4 neuron. The spatial alignment of receptive field centers of dLGN inputs matched the preferred orientation of the corresponding neuron. Thus, our findings directly validate core predictions of the connectivity proposed by the H&W [Hubel and Wiesel's] model."
By the way, Clay Reid was a trainee of H&W.
-
"Thalamic activation of the visual cortex at the single-synapse level", Chen et al. 2026.
https://www.science.org/doi/full/10.1126/science.aec9923There was earlier work using a much coarser technique which found the same result:
"Specificity of monosynaptic connections from thalamus to visual cortex", Reid and Alonso, 1995.
https://www.nature.com/articles/378281a0Compare figure 3 in Reid & Alonso 1995 with Fig 5E in Chen et al. 2026.
But as Chen et al. 2026 says:
"Earlier experiments that used paired recordings of an orientation-selective L4 neuron in the cat visual cortex and a corresponding dLGN cell (5 [Reid and Alonso, 1995]) suggested, but could not prove, a synaptic connectivity scheme consistent with the H&W model. Our approach enabled the identification and characterization of nearly the complete set of active dLGN synaptic inputs to a single orientation-selective L4 neuron. The spatial alignment of receptive field centers of dLGN inputs matched the preferred orientation of the corresponding neuron. Thus, our findings directly validate core predictions of the connectivity proposed by the H&W [Hubel and Wiesel's] model."
By the way, Clay Reid was a trainee of H&W.
-
"Thalamic activation of the visual cortex at the single-synapse level", Chen et al. 2026.
https://www.science.org/doi/full/10.1126/science.aec9923There was earlier work using a much coarser technique which found the same result:
"Specificity of monosynaptic connections from thalamus to visual cortex", Reid and Alonso, 1995.
https://www.nature.com/articles/378281a0Compare figure 3 in Reid & Alonso 1995 with Fig 5E in Chen et al. 2026.
But as Chen et al. 2026 says:
"Earlier experiments that used paired recordings of an orientation-selective L4 neuron in the cat visual cortex and a corresponding dLGN cell (5 [Reid and Alonso, 1995]) suggested, but could not prove, a synaptic connectivity scheme consistent with the H&W model. Our approach enabled the identification and characterization of nearly the complete set of active dLGN synaptic inputs to a single orientation-selective L4 neuron. The spatial alignment of receptive field centers of dLGN inputs matched the preferred orientation of the corresponding neuron. Thus, our findings directly validate core predictions of the connectivity proposed by the H&W [Hubel and Wiesel's] model."
By the way, Clay Reid was a trainee of H&W.
-
"Thalamic activation of the visual cortex at the single-synapse level", Chen et al. 2026.
https://www.science.org/doi/full/10.1126/science.aec9923There was earlier work using a much coarser technique which found the same result:
"Specificity of monosynaptic connections from thalamus to visual cortex", Reid and Alonso, 1995.
https://www.nature.com/articles/378281a0Compare figure 3 in Reid & Alonso 1995 with Fig 5E in Chen et al. 2026.
But as Chen et al. 2026 says:
"Earlier experiments that used paired recordings of an orientation-selective L4 neuron in the cat visual cortex and a corresponding dLGN cell (5 [Reid and Alonso, 1995]) suggested, but could not prove, a synaptic connectivity scheme consistent with the H&W model. Our approach enabled the identification and characterization of nearly the complete set of active dLGN synaptic inputs to a single orientation-selective L4 neuron. The spatial alignment of receptive field centers of dLGN inputs matched the preferred orientation of the corresponding neuron. Thus, our findings directly validate core predictions of the connectivity proposed by the H&W [Hubel and Wiesel's] model."
By the way, Clay Reid was a trainee of H&W.
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How does the #brain encode features of environmental structure while viewing a 3D scene? This study shows that the human #VisualCortex hierarchically encodes nearby boundaries, revealing a distance-before-orientation principle of spatial layout processing @PLOSBiology https://plos.io/3NoUjh2
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How does the #brain encode features of environmental structure while viewing a 3D scene? This study shows that the human #VisualCortex hierarchically encodes nearby boundaries, revealing a distance-before-orientation principle of spatial layout processing @PLOSBiology https://plos.io/3NoUjh2
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How does the #brain encode features of environmental structure while viewing a 3D scene? This study shows that the human #VisualCortex hierarchically encodes nearby boundaries, revealing a distance-before-orientation principle of spatial layout processing @PLOSBiology https://plos.io/3NoUjh2
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How does the #brain encode features of environmental structure while viewing a 3D scene? This study shows that the human #VisualCortex hierarchically encodes nearby boundaries, revealing a distance-before-orientation principle of spatial layout processing @PLOSBiology https://plos.io/3NoUjh2
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How does the #brain encode features of environmental structure while viewing a 3D scene? This study shows that the human #VisualCortex hierarchically encodes nearby boundaries, revealing a distance-before-orientation principle of spatial layout processing @PLOSBiology https://plos.io/3NoUjh2
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🧠 New paper by Cowley et al: Compact deep neural network models of the #VisualCortex .
The authors show that neural responses in primate visual cortex can be predicted by much smaller, interpretable #DeepNeuralMetworks than previously assumed. By constraining model architecture and parameters, they achieve strong predictive performance while revealing computational structure in cortical processing.
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🧠 New paper by Cowley et al: Compact deep neural network models of the #VisualCortex .
The authors show that neural responses in primate visual cortex can be predicted by much smaller, interpretable #DeepNeuralMetworks than previously assumed. By constraining model architecture and parameters, they achieve strong predictive performance while revealing computational structure in cortical processing.
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🧠 New paper by Cowley et al: Compact deep neural network models of the #VisualCortex .
The authors show that neural responses in primate visual cortex can be predicted by much smaller, interpretable #DeepNeuralMetworks than previously assumed. By constraining model architecture and parameters, they achieve strong predictive performance while revealing computational structure in cortical processing.
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🧠 New paper by Cowley et al: Compact deep neural network models of the #VisualCortex .
The authors show that neural responses in primate visual cortex can be predicted by much smaller, interpretable #DeepNeuralMetworks than previously assumed. By constraining model architecture and parameters, they achieve strong predictive performance while revealing computational structure in cortical processing.
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🧠 New paper by Cowley et al: Compact deep neural network models of the #VisualCortex .
The authors show that neural responses in primate visual cortex can be predicted by much smaller, interpretable #DeepNeuralMetworks than previously assumed. By constraining model architecture and parameters, they achieve strong predictive performance while revealing computational structure in cortical processing.
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https://www.europesays.com/ie/378952/ AI Models Predict New Vision Mechanisms in Real Brains #AI #ArtificialIntelligence #ArtificialIntelligence #DigitalTwins #Éire #IE #Ireland #MachineLearning #Neuroscience #ReceptiveFields #StanfordUniversity #Technology #VisualCortex #VisualNeuroscience
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https://www.europesays.com/uk/798159/ Many people don’t see mental images. The reason offers clues to consciousness #Health #MentalHealth #MentalImagery #researchers #UK #UnitedKingdom #VisualCortex
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Many people don’t see mental images. The reason offers clues to consciousness
Think about your breakfast this morning. Can you imagine the pattern on your coffee mug? The sheen of…
#NewsBeep #News #Mentalhealth #CA #Canada #Health #mentalimagery #MentalHealth #researchers #visualcortex
https://www.newsbeep.com/ca/504793/ -
https://www.europesays.com/ie/360413/ Many people don’t see mental images. The reason offers clues to consciousness #Éire #Health #IE #Ireland #MentalHealth #MentalImagery #MentalHealth #Researchers #VisualCortex
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Syeda, …, @computingnature et al., bioRxiv (2026) find that #NeuralActivity in #mouse #VisualCortex is dominated by #orofacial movements, not eye movements. Across darkness and visual stimulation, eye movements explain only a small fraction of neural variance and are largely correlated with whisking and sniffing. Movement signals thus strongly shape #V1 activity during free viewing.
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Syeda, …, @computingnature et al., bioRxiv (2026) find that #NeuralActivity in #mouse #VisualCortex is dominated by #orofacial movements, not eye movements. Across darkness and visual stimulation, eye movements explain only a small fraction of neural variance and are largely correlated with whisking and sniffing. Movement signals thus strongly shape #V1 activity during free viewing.
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Syeda, …, @computingnature et al., bioRxiv (2026) find that #NeuralActivity in #mouse #VisualCortex is dominated by #orofacial movements, not eye movements. Across darkness and visual stimulation, eye movements explain only a small fraction of neural variance and are largely correlated with whisking and sniffing. Movement signals thus strongly shape #V1 activity during free viewing.
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Syeda, …, @computingnature et al., bioRxiv (2026) find that #NeuralActivity in #mouse #VisualCortex is dominated by #orofacial movements, not eye movements. Across darkness and visual stimulation, eye movements explain only a small fraction of neural variance and are largely correlated with whisking and sniffing. Movement signals thus strongly shape #V1 activity during free viewing.
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Syeda, …, @computingnature et al., bioRxiv (2026) find that #NeuralActivity in #mouse #VisualCortex is dominated by #orofacial movements, not eye movements. Across darkness and visual stimulation, eye movements explain only a small fraction of neural variance and are largely correlated with whisking and sniffing. Movement signals thus strongly shape #V1 activity during free viewing.
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The #neurons that let us see what isn’t there
A standard #opticalillusion triggers specific neurons in the #visual system of mice.
“Illusions are fun, but they are also a gateway to perception,” says Hyeyoung Shin, assistant professor of neuroscience at Seoul National Universitym first author of a new study in Nature Neuroscience that identified a specific population of neurons in #visualcortex—dubbed IC-encoders—and shows a direct role in representing a visual illusion.
https://arstechnica.com/science/2025/10/the-neurons-that-let-us-see-what-isnt-there/ -
The #neurons that let us see what isn’t there
A standard #opticalillusion triggers specific neurons in the #visual system of mice.
“Illusions are fun, but they are also a gateway to perception,” says Hyeyoung Shin, assistant professor of neuroscience at Seoul National Universitym first author of a new study in Nature Neuroscience that identified a specific population of neurons in #visualcortex—dubbed IC-encoders—and shows a direct role in representing a visual illusion.
https://arstechnica.com/science/2025/10/the-neurons-that-let-us-see-what-isnt-there/ -
The #neurons that let us see what isn’t there
A standard #opticalillusion triggers specific neurons in the #visual system of mice.
“Illusions are fun, but they are also a gateway to perception,” says Hyeyoung Shin, assistant professor of neuroscience at Seoul National Universitym first author of a new study in Nature Neuroscience that identified a specific population of neurons in #visualcortex—dubbed IC-encoders—and shows a direct role in representing a visual illusion.
https://arstechnica.com/science/2025/10/the-neurons-that-let-us-see-what-isnt-there/ -
The #neurons that let us see what isn’t there
A standard #opticalillusion triggers specific neurons in the #visual system of mice.
“Illusions are fun, but they are also a gateway to perception,” says Hyeyoung Shin, assistant professor of neuroscience at Seoul National Universitym first author of a new study in Nature Neuroscience that identified a specific population of neurons in #visualcortex—dubbed IC-encoders—and shows a direct role in representing a visual illusion.
https://arstechnica.com/science/2025/10/the-neurons-that-let-us-see-what-isnt-there/ -
The #neurons that let us see what isn’t there
A standard #opticalillusion triggers specific neurons in the #visual system of mice.
“Illusions are fun, but they are also a gateway to perception,” says Hyeyoung Shin, assistant professor of neuroscience at Seoul National Universitym first author of a new study in Nature Neuroscience that identified a specific population of neurons in #visualcortex—dubbed IC-encoders—and shows a direct role in representing a visual illusion.
https://arstechnica.com/science/2025/10/the-neurons-that-let-us-see-what-isnt-there/ -
🧠 Rao & Ballard’s (1999) spatial #PredictiveCoding theory gets strong support: Zhang et al. (2025) show in mouse #VisualCortex that predictive coding is primarily spatial, not temporal. #2P #imaging of ~20,000 neurons found mismatch responses only when new spatial landmarks appeared, but not when sequences were reordered:
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🧠 Rao & Ballard’s (1999) spatial #PredictiveCoding theory gets strong support: Zhang et al. (2025) show in mouse #VisualCortex that predictive coding is primarily spatial, not temporal. #2P #imaging of ~20,000 neurons found mismatch responses only when new spatial landmarks appeared, but not when sequences were reordered:
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🧠 Rao & Ballard’s (1999) spatial #PredictiveCoding theory gets strong support: Zhang et al. (2025) show in mouse #VisualCortex that predictive coding is primarily spatial, not temporal. #2P #imaging of ~20,000 neurons found mismatch responses only when new spatial landmarks appeared, but not when sequences were reordered:
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🧠 Rao & Ballard’s (1999) spatial #PredictiveCoding theory gets strong support: Zhang et al. (2025) show in mouse #VisualCortex that predictive coding is primarily spatial, not temporal. #2P #imaging of ~20,000 neurons found mismatch responses only when new spatial landmarks appeared, but not when sequences were reordered:
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🧠 Rao & Ballard’s (1999) spatial #PredictiveCoding theory gets strong support: Zhang et al. (2025) show in mouse #VisualCortex that predictive coding is primarily spatial, not temporal. #2P #imaging of ~20,000 neurons found mismatch responses only when new spatial landmarks appeared, but not when sequences were reordered:
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🧠 New study by Lempel et al. (2025): Visual experience after eye opening aligns #feedforward inputs (L4) and #RecurrentNetworks (L2/3) in mouse #VisualCortex. This improved alignment, together with enhanced orientation discrimination, creates coherent, reliable sensory codes, revealing how experience shapes stable #cortical representations:
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🧠 New study by Lempel et al. (2025): Visual experience after eye opening aligns #feedforward inputs (L4) and #RecurrentNetworks (L2/3) in mouse #VisualCortex. This improved alignment, together with enhanced orientation discrimination, creates coherent, reliable sensory codes, revealing how experience shapes stable #cortical representations:
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🧠 New study by Lempel et al. (2025): Visual experience after eye opening aligns #feedforward inputs (L4) and #RecurrentNetworks (L2/3) in mouse #VisualCortex. This improved alignment, together with enhanced orientation discrimination, creates coherent, reliable sensory codes, revealing how experience shapes stable #cortical representations:
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🧠 New study by Lempel et al. (2025): Visual experience after eye opening aligns #feedforward inputs (L4) and #RecurrentNetworks (L2/3) in mouse #VisualCortex. This improved alignment, together with enhanced orientation discrimination, creates coherent, reliable sensory codes, revealing how experience shapes stable #cortical representations:
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🧠 New study by Lempel et al. (2025): Visual experience after eye opening aligns #feedforward inputs (L4) and #RecurrentNetworks (L2/3) in mouse #VisualCortex. This improved alignment, together with enhanced orientation discrimination, creates coherent, reliable sensory codes, revealing how experience shapes stable #cortical representations:
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https://www.europesays.com/uk/311828/ Brain Sorts ‘Stuff’ from ‘Things’ Using Separate Neural Circuits #BrainResearch #MIT #NeuralNetworks #neurobiology #Neuroscience #Science #UK #UnitedKingdom #VisualCortex #VisualNeuroscience
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Brain Sorts ‘Stuff’ from ‘Things’ Using Separate Neural Circuits
Key Questions Answered Q: What did the study uncover about how the brain processes materials?A: It found that…
#NewsBeep #News #Science #brainresearch #GB #MIT #neuralnetworks #neurobiology #Neuroscience #UK #UnitedKingdom #visualcortex #visualneuroscience
https://www.newsbeep.com/uk/39253/ -
In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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In their study, Morales-Gregorio et al. show that #NeuralManifolds in #V1 shift dynamically under top-down influence from #V4. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.
🌍 https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5
#CompNeuro #Neuroscience #VisualCortex #NeuralManifolds #SystemsNeuroscience
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A new study by Timothy Sit, Célian Bimbard, Anna Lebedeva, @MatteoCarandini, Philip Coen, and Kenneth Harris challenges the classic cortical column model. Using in vivo recordings, they show that layers 2/3 and 5 in #V1 encode distinct visual features — functionally dissociated, not redundant. A cool look into laminar specialization.
🌍 https://www.biorxiv.org/content/10.1101/2025.06.30.662366v2
#Neuroscience #VisualCortex #CorticalLayers #SystemsNeuroscience
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A new study by Timothy Sit, Célian Bimbard, Anna Lebedeva, @MatteoCarandini, Philip Coen, and Kenneth Harris challenges the classic cortical column model. Using in vivo recordings, they show that layers 2/3 and 5 in #V1 encode distinct visual features — functionally dissociated, not redundant. A cool look into laminar specialization.
🌍 https://www.biorxiv.org/content/10.1101/2025.06.30.662366v2
#Neuroscience #VisualCortex #CorticalLayers #SystemsNeuroscience
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A new study by Timothy Sit, Célian Bimbard, Anna Lebedeva, @MatteoCarandini, Philip Coen, and Kenneth Harris challenges the classic cortical column model. Using in vivo recordings, they show that layers 2/3 and 5 in #V1 encode distinct visual features — functionally dissociated, not redundant. A cool look into laminar specialization.
🌍 https://www.biorxiv.org/content/10.1101/2025.06.30.662366v2
#Neuroscience #VisualCortex #CorticalLayers #SystemsNeuroscience
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A new study by Timothy Sit, Célian Bimbard, Anna Lebedeva, @MatteoCarandini, Philip Coen, and Kenneth Harris challenges the classic cortical column model. Using in vivo recordings, they show that layers 2/3 and 5 in #V1 encode distinct visual features — functionally dissociated, not redundant. A cool look into laminar specialization.
🌍 https://www.biorxiv.org/content/10.1101/2025.06.30.662366v2
#Neuroscience #VisualCortex #CorticalLayers #SystemsNeuroscience