Feedforward mechanisms of cross-orientation interactions in mouse V1

Barbera, Dylan; Priebe, Nicholas J.; Glickfeld, Lindsey L.

doi:10.1016/j.neuron.2021.10.017

Cited by 12 publications

(10 citation statements)

References 80 publications

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“…For example, the firing rate response of neurons in the visual cortex to multiple overlayed oriented gratings is normalized in a non-linear fashion (71,72). While this form of normalization is mostly of thalamic origin (73)(74)(75), there is most likely also a cortical component (72,76). A recently introduced E-I network model with static, hand-crafted connectivity can explain these modulations (18,77).…”

Section: Inhibitory Neurons Balance Excitatory Attraction and Enable ...mentioning

confidence: 99%

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Eckmann

Gjorgjieva

2022

Preprint

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Cortical networks exhibit complex stimulus-response patterns. Previous work has identified the balance between excitatory and inhibitory currents as a central component of cortical computations, but has not considered how the required synaptic connectivity emerges from biologically plausible plasticity rules. Using theory and modeling, we demonstrate how a wide range of cortical response properties can arise from Hebbian learning that is stabilized by the synapse-type-specific competition for synaptic resources. In fully plastic recurrent circuits, this competition enables the development and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger connections between similarly tuned neurons and exhibit response normalization and surround suppression. These results demonstrate how neurons can self-organize into functional circuits and provide a foundational understanding of plasticity in recurrent networks.

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Section: Inhibitory Neurons Balance Excitatory Attraction and Enable ...mentioning

confidence: 99%

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Eckmann

Gjorgjieva

2022

Preprint

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Section: Discussionmentioning

confidence: 94%

“…However, facilitated responses have also been reported in monkey and mouse [32][33][34][35][45][46][47] . The large variety in cortical responses to multiple superimposed gratings could also be explained by orientationspecific horizontal interactions within V1 32,33 or by a thalamocortical feedforward mechanism 35 . Both mechanisms would also explain the particular tuning of V1 neurons to specific orientation and spatial frequency bandwidths that we observed in our study.…”

Section: Discussionmentioning

confidence: 94%

“…1g, see also example cells in 1d). Instead of an overall recruitment of neurons with narrow tuning, this suggests that V1 neurons exhibit a more complex orientation tuning that is selective for specific orientation bandwidths that match naturalistic stimulus patterns [32][33][34][35] . This also included a large fraction of neurons (186 neurons, 12.33% of all responsive cells) that responded most strongly to the mid-range orientation bandwidth, and therefore neither preferred the narrowest nor broad bandwidth stimuli (Fig.…”

Section: Orientation Bandwidth Of Visual Stimuli Recruits Additional ...mentioning

confidence: 99%

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Broadband visual stimuli improve neuronal representation and sensory perception

Balla

Wiesbrock

Linde

et al. 2023

Preprint

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Natural scenes are composed of complex distributions of visual features that drive neural response patterns and shape visual perception. However, most stimuli that are commonly used in vision research only reveal neural responses to single features, such as a specific stimulus orientation. How larger feature distributions affect neural responses and visual perception is therefore poorly understood. To address this question, we presented broadband visual stimuli with para-metrically-controlled bandwidth of stimulus orientations and spatial frequencies to awake mice while recording the activity of neural populations in the primary visual cortex with two-photon imaging. Matching the orientation bandwidth of broadband stimuli to naturalistic images strongly increased neural responses and improved feature discrimination performance. Correspondingly, increasing orientation bandwidth also improved the performance of mice in a visual discrimination task. Our results strongly suggest that the visual system is tuned to the feature distributions of naturalistic visual inputs, with broader feature distributions driving more robust neural responses and enhanced visual perception.

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“…These other sources of input may provide stronger sources of correlation than those present in the feedforward input. Indeed, for many V1 cells, the feedforward current from the LGN comprises less than 50% of the total current to a V1 cell (Finn et al, 2007; Li et al, 2013; Lien and Scanziani, 2013; Barbera et al, 2022). Thus, it is possible that any change in the feedforward component of the noise is masked by variations in other inputs that more strongly drive a V1 cell.…”

Section: Discussionmentioning

confidence: 99%

Luminance invariant encoding in primary visual cortex

O’Shea,

Nauhaus,

Wei

et al. 2024

Preprint

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The retina maintains sensitivity over a large range of luminance intensities by switching between rod and cone photoreceptors. This luminance adaptation has been shown to alter the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to encode visual information across environmental conditions, they present a challenge to downstream processing areas for which it is important that representations are invariant to light level. We measured the effects of scotopic versus photopic luminance adaptation on thalamic and cortical activity by tracking neuronal populations across light levels. While changes in the output of the retina are evident in the lateral geniculate nucleus (LGN), the representation in primary visual cortex (V1) is largely invariant to the changes in luminance. We show that an invariant V1 code can emerge through the integration of parallel functional pathways at the geniculocortical synapse.

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Feedforward mechanisms of cross-orientation interactions in mouse V1

Cited by 12 publications

References 80 publications

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Broadband visual stimuli improve neuronal representation and sensory perception

Luminance invariant encoding in primary visual cortex

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