scholarly journals A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

2018 ◽  
Author(s):  
Saskia E. J. de Vries ◽  
Jerome Lecoq ◽  
Michael A. Buice ◽  
Peter A. Groblewski ◽  
Gabriel K. Ocker ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sensory Information ◽  
Natural Scenes ◽  
Visual Coding ◽  
Adult Mice ◽  
Cortical Responses ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Hierarchical Computation

SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

scholarly journals Spontaneous behaviors drive multidimensional, brain-wide activity

2018 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Charu Bai Reddy ◽  
Matteo Carandini ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spontaneous Activity ◽  
Random Noise ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Cortical Responses ◽  
Ongoing Behavior ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Behavioral Information

Cortical responses to sensory stimuli are highly variable, and sensory cortex exhibits intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Here, by recording over 10,000 neurons in mouse visual cortex, we show that spontaneous activity reliably encodes a high-dimensional latent state, which is partially related to the mouse’s ongoing behavior and is represented not just in visual cortex but across the forebrain. Sensory inputs do not interrupt this ongoing signal, but add onto it a representation of visual stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

scholarly journals Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex

2021 ◽  
Author(s):  
Colin Conwell ◽  
David Mayo ◽  
Boris Katz ◽  
Michael A. Buice ◽  
George A. Alvarez ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Visual Cortex ◽  
Cortical Neurons ◽  
Large Scale ◽  
Deep Neural Network ◽  
Neural Network Modeling ◽  
Natural Scenes ◽  
Neural Network Models ◽  
Mouse Visual Cortex

How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a large-scale benchmarking of dozens of deep neural network models in mouse visual cortex with multiple methods of comparison and multiple modes of verification. Using the Allen Brain Observatory's 2-photon calcium-imaging dataset of activity in over 59,000 rodent visual cortical neurons recorded in response to natural scenes, we replicate previous findings and resolve previous discrepancies, ultimately demonstrating that modern neural networks can in fact be used to explain activity in the mouse visual cortex to a more reasonable degree than previously suggested. Using our benchmark as an atlas, we offer preliminary answers to overarching questions about levels of analysis (e.g. do models that better predict the representations of individual neurons also predict representational geometry across neural populations?); questions about the properties of models that best predict the visual system overall (e.g. does training task or architecture matter more for augmenting predictive power?); and questions about the mapping between biological and artificial representations (e.g. are there differences in the kinds of deep feature spaces that predict neurons from primary versus posteromedial visual cortex?). Along the way, we introduce a novel, highly optimized neural regression method that achieves SOTA scores (with gains of up to 34%) on the publicly available benchmarks of primate BrainScore. Simultaneously, we benchmark a number of models (including vision transformers, MLP-Mixers, normalization free networks and Taskonomy encoders) outside the traditional circuit of convolutional object recognition. Taken together, our results provide a reference point for future ventures in the deep neural network modeling of mouse visual cortex, hinting at novel combinations of method, architecture, and task to more fully characterize the computational motifs of visual representation in a species so indispensable to neuroscience.

scholarly journals Representational drift in the mouse visual cortex

2020 ◽  
Author(s):  
Daniel Deitch ◽  
Alon Rubin ◽  
Yaniv Ziv
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Population Level ◽  
Population Activity ◽  
Cortical Areas ◽  
Electrophysiological Recordings ◽  
The Face ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Over Time

AbstractNeuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such ‘representational drift’ occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells’ activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

scholarly journals Neuronal adaptation reveals a suboptimal decoding of orientation tuned populations in the mouse visual cortex

2018 ◽  
Author(s):  
Miaomiao Jin ◽  
Jeffrey M. Beck ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Cortical Neurons ◽  
Signal To Noise Ratio ◽  
Sensory Information ◽  
Orientation Discrimination ◽  
Signal To Noise ◽  
Neuronal Adaptation ◽  
Related Information ◽  
Mouse Visual Cortex

AbstractSensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust adaptation in the mouse visual system. We find that adaptation increases animals’ thresholds for orientation discrimination. This was unexpected since optimal computations that take advantage of all available sensory information predict that the shift in tuning and increase in signal-to-noise ratio in the adapted condition should improve discrimination. Instead, we find that the effects of adaptation on behavior can be explained by the appropriate reliance of the perceptual choice circuits on target preferring neurons, but the failure to discount neurons that prefer the distractor. This suggests that to solve this task the circuit has adopted a suboptimal strategy that discards important task-related information to implement a feed-forward visual computation.

scholarly journals Unsupervised Models of Mouse Visual Cortex

2021 ◽  
Author(s):  
Aran Nayebi ◽  
Nathan C. L. Kong ◽  
Chengxu Zhuang ◽  
Justin L. Gardner ◽  
Anthony M. Norcia ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Network Architectures ◽  
Deep Convolutional Neural Networks ◽  
Visual Stream ◽  
Computational Theory ◽  
Visual Coding ◽  
Ventral Visual Stream ◽  
Mouse Visual Cortex ◽  
Image Transformations

Task-optimized deep convolutional neural networks are the most quantitatively accurate models of the primate ventral visual stream. However, such networks are implausible as a model of the mouse visual system because mouse visual cortex has a known shallower hierarchy and the supervised objectives these networks are typically trained with are likely neither ethologically relevant in content nor in quantity. Here we develop shallow network architectures that are more consistent with anatomical and physiological studies of mouse visual cortex than current models. We demonstrate that hierarchically shallow architectures trained using contrastive objective functions applied to visual-acuity-adapted images achieve neural prediction performance that exceed those of the same architectures trained in a supervised manner and result in the most quantitatively accurate models of the mouse visual system. Moreover, these models' neural predictivity significantly surpasses those of supervised, deep architectures that are known to correspond well to the primate ventral visual stream. Finally, we derive a novel measure of inter-animal consistency, and show that the best models closely match this quantity across visual areas. Taken together, our results suggest that contrastive objectives operating on shallow architectures with ethologically-motivated image transformations may be a biologically-plausible computational theory of visual coding in mice.

scholarly journals Orientation tuning depends on spatial frequency in mouse visual cortex

2016 ◽  
Author(s):  
Inbal Ayzenshtat ◽  
Jesse Jackson ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Natural Scenes ◽  
Response Properties ◽  
Layer 2 ◽  
Mouse Visual Cortex

AbstractThe response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.Significance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.Highlights–Orientation selectivity is dependent on spatial frequency.–Asymmetric Gabor model can explain this dependence.

scholarly journals Dark Rearing Promotes the Recovery of Visual Cortical Responses but Not the Morphology of Geniculocortical Axons in Amblyopic Cat

2021 ◽  
Vol 15 ◽  
Author(s):  
Takahiro Gotou ◽  
Katsuro Kameyama ◽  
Ayane Kobayashi ◽  
Kayoko Okamura ◽  
Takahiko Ando ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Cortical Neurons ◽  
Visual Pathway ◽  
Monocular Deprivation ◽  
Postnatal Life ◽  
Visual Neurons ◽  
Cortical Responses ◽  
Soma Size ◽  
Visual Cortical ◽  
Dark Rearing

Monocular deprivation (MD) of vision during early postnatal life induces amblyopia, and most neurons in the primary visual cortex lose their responses to the closed eye. Anatomically, the somata of neurons in the closed-eye recipient layer of the lateral geniculate nucleus (LGN) shrink and their axons projecting to the visual cortex retract. Although it has been difficult to restore visual acuity after maturation, recent studies in rodents and cats showed that a period of exposure to complete darkness could promote recovery from amblyopia induced by prior MD. However, in cats, which have an organization of central visual pathways similar to humans, the effect of dark rearing only improves monocular vision and does not restore binocular depth perception. To determine whether dark rearing can completely restore the visual pathway, we examined its effect on the three major concomitants of MD in individual visual neurons, eye preference of visual cortical neurons and soma size and axon morphology of LGN neurons. Dark rearing improved the recovery of visual cortical responses to the closed eye compared with the recovery under binocular conditions. However, geniculocortical axons serving the closed eye remained retracted after dark rearing, whereas reopening the closed eye restored the soma size of LGN neurons. These results indicate that dark rearing incompletely restores the visual pathway, and thus exerts a limited restorative effect on visual function.

scholarly journals A Scalable and Modular Automated Pipeline for Stitching of Large Electron Microscopy Datasets

2021 ◽  
Author(s):  
Gayathri Mahalingam ◽  
Russel Torres ◽  
Daniel Kapner ◽  
Eric T Trautman ◽  
Tim Fliss ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Visual Cortex ◽  
High Throughput ◽  
Serial Section ◽  
Large Scale ◽  
Software Pipeline ◽  
Array Tomography ◽  
Automated Quality Control ◽  
Mouse Visual Cortex ◽  
The Brain

Serial section Electron Microscopy can produce high throughput imaging of large biological specimen volumes. The high-resolution images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. A high fidelity volume assembly is required to correctly reconstruct neural anatomy and synaptic connections. It involves seamless 2D stitching of the images within a serial section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP(Assembly Stitching and Alignment Pipeline) that is scalable and parallelized to work with distributed systems. The pipeline is built on top of the Render [18] services used in the volume assembly of the brain of adult Drosophila melanogaster [2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. The modularity of ASAP allows for easy adaptation to new algorithms without significant changes to the workflow. The software pipeline includes a complete set of tools to do stitching, automated quality control, 3D section alignment, and rendering of the assembled volume to disk. We also implemented a workflow engine that executes the volume assembly workflow in an automated fashion triggered following the transfer of raw data. ASAP has been successfully utilized for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25]. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

scholarly journals Canonical and noncanonical features of the mouse visual cortical hierarchy

2020 ◽  
Author(s):  
Rinaldo D. D’Souza ◽  
Quanxin Wang ◽  
Weiqing Ji ◽  
Andrew M. Meier ◽  
Henry Kennedy ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Active Vision ◽  
Structural Features ◽  
Hierarchical Organization ◽  
Hierarchical Level ◽  
Continuous Measure ◽  
Axonal Projections ◽  
Visual Cortical ◽  
Mouse Visual Cortex

ABSTRACTNeocortical circuit computations underlying active vision are performed by a distributed network of reciprocally connected, functionally specialized areas. Mouse visual cortex is a dense, hierarchically organized network, comprising subnetworks that form preferentially interconnected processing streams. To determine the detailed layout of the mouse visual hierarchy, laminar patterns formed by interareal axonal projections, originating in each of ten visual areas were analyzed. Reciprocally connected pairs of areas, and shared targets of pairs of source areas, exhibited structural features consistent with a hierarchical organization. Beta regression analyses, which estimated a continuous measure of hierarchical distance, indicated that the network comprises multiple hierarchies embedded within overlapping processing levels. Single unit recordings showed that within each processing stream, receptive field sizes typically increased with increasing hierarchical level; however, ventral stream areas showed overall larger receptive field diameters. Together, the results reveal canonical and noncanonical hierarchical network motifs in mouse visual cortex.

scholarly journals Multimodal Imaging Evidence for a Frontoparietal Modulation of Visual Cortex during the Selective Processing of Conditioned Threat

2017 ◽  
Vol 29 (6) ◽  
pp. 953-967 ◽  
Author(s):  
Nathan M. Petro ◽  
L. Forest Gruss ◽  
Siyang Yin ◽  
Haiqing Huang ◽  
Vladimir Miskovic ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Anterior Insula ◽  
Causal Modeling ◽  
Middle Frontal Gyrus ◽  
Large Scale Network ◽  
Inferior Parietal Cortex ◽  
Scale Network ◽  
Cortical Regions ◽  
Visual Cortical

Emotionally salient cues are detected more readily, remembered better, and evoke greater visual cortical responses compared with neutral stimuli. The current study used concurrent EEG-fMRI recordings to identify large-scale network interactions involved in the amplification of visual cortical activity when viewing aversively conditioned cues. To generate a continuous neural signal from pericalcarine visual cortex, we presented rhythmic (10/sec) phase-reversing gratings, the orientation of which predicted the presence (CS+) or absence (CS−) of a cutaneous electric shock (i.e., the unconditioned stimulus). The resulting single trial steady-state visual evoked potential (ssVEP) amplitude was regressed against the whole-brain BOLD signal, resulting in a measure of ssVEP-BOLD coupling. Across all trial types, ssVEP-BOLD coupling was observed in both primary and extended visual cortical regions, the rolandic operculum, as well as the thalamus and bilateral hippocampus. For CS+ relative to CS− trials during the conditioning phase, BOLD-alone analyses showed CS+ enhancement at the occipital pole, superior temporal sulci, and the anterior insula bilaterally, whereas ssVEP-BOLD coupling was greater in the pericalcarine cortex, inferior parietal cortex, and middle frontal gyrus. Dynamic causal modeling analyses supported connectivity models in which heightened activity in pericalcarine cortex for threat (CS+) arises from cortico-cortical top–down modulation, specifically from the middle frontal gyrus. No evidence was observed for selective pericalcarine modulation by deep cortical structures such as the amygdala or anterior insula, suggesting that the heightened engagement of pericalcarine cortex for threat stimuli is mediated by cortical structures that constitute key nodes of canonical attention networks.

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