Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice.

Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates.

A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF.

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos.

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions.

The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility.

Pattern completion and disruption characterize contextual modulation in the visual cortex.

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli - where the center and surround share the same orientation - are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing.

Bipartite invariance in mouse primary visual cortex.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli.

State-dependent pupil dilation rapidly shifts visual feature selectivity.

To increase computational flexibility, the processing of sensory inputs changes with behavioural context. In the visual system, active behavioural states characterized by motor activity and pupil dilation enhance sensory responses, but typically leave the preferred stimuli of neurons unchanged. Here we find that behavioural state also modulates stimulus selectivity in the mouse visual cortex in the context of coloured natural scenes.

Novel microwire-based biosensor probe for simultaneous real-time measurement of glutamate and GABA dynamics in vitro and in vivo.

Glutamate (GLU) and γ-aminobutyric acid (GABA) are the major excitatory (E) and inhibitory (I) neurotransmitters in the brain, respectively. Dysregulation of the E/I ratio is associated with numerous neurological disorders. Enzyme-based microelectrode array biosensors present the potential for improved biocompatibility, localized sample volumes, and much faster sampling rates over existing measurement methods.

TRIO Platform: A Novel Low Profile In vivo Imaging Support and Restraint System for Mice.

High resolution, in vivo optical imaging of the mouse brain over time often requires anesthesia, which necessitates maintaining the animal's body temperature and level of anesthesia, as well as securing the head in an optimal, stable position. Controlling each parameter usually requires using multiple systems. Assembling multiple components into the small space on a standard microscope stage can be difficult and some commercially available parts simply do not fit.