Mouse visual cortex

…connectivity of a column of the mouse visual cortex with a generative model that predicts connectivity based on explicit biological and physical features: excitatory or inhibitory cell type, the distance between cell bodies, and cell's depth, which is c

Cortical neurons exhibit a high degree of trial-to-trial variability in response to repeated presentations of the same stimulus. We propose that this variability serves a useful computational purpose: allowing the brain to generalize from a small number of examples. To formalize this theory, we consider a Gaussian model of few-shot classification, wherein we observe a few samples of each class, and each observation has trial-to-trial variability. Our model leads to three predictions about the optimal neural response variability that minimizes generalization error: (i) the optimal variability m

Microcircuits are the fundamental building blocks of the neocortex [1]. Single instances have been reconstructed experimentally (e.g., [2]), and their general dynamics and information processing capabilities have been investigated theoretically (e.g., [3, 4, 5, 6, 7]). Their architecture is usually represented in connectivity maps consisting of probabilities that neurons establish connections. These maps reduce the complicated circuitry to simple relations between cell types, allowing for efficient instantiations of neural network models in parallel computers [8]. While this approach neglects

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. We expect neural responses to be functionally highly similar within a species (since we expect findings to generalize across animals). What kind of transform will make such similarity most evident? That is, under what kind of transform are conspecifics’ neural responses highly similar to each other? Researchers often default to linear regression as a reasonable transform class for measuring neural

Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse's visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributin

Recent studies on representational drift in the visual cortex have produced an intriguing puzzle, demonstrating that neuronal responses to the same external stimuli change over time while population-level activity remains stable (Deitch et al., 2021; Marks & Gourd, 2021). The purpose of drift, and how the brain either compensates for or takes advantage of this phenomenon to maintain robust perception, remains largely unknown. A recent study found that the geometric properties of drift in the primary visual cortex resemble that of continual learning in the presence of a large amount of drop

Biological visual systems have evolved around the efficient coding of natural image statistics in order to support recognition of complex visual patterns. Recent work has shown that deep neural networks are able to learn similar representations to those measured in visual areas in animals, suggesting they may serve as models for the brain. Varying network architectures and loss functions has been shown to modulate the biological similarity learned representations, however the extent to which this results from exposure to natural image statistics during training has not been fully characterized

The ability to record large neural populations—hundreds to thousands of cells simultaneously—is a defining feature of modern systems neuroscience. Aside from improved experimental efficiency, what do these technologies fundamentally buy us? I'll argue that they provide an exciting opportunity to move beyond studying the "average" neural response. That is, by providing dense neural circuit measurements in individual subjects and moments in time, these recordings enable us to track changes across repeated behavioral trials and across experimental subjects. These two forms of variability are stil

Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributin

Understanding neural information processing is extremely challenging. This is because often, it is not even clear what information neurons are really processing. However, we can access the dynamic fingerprint of the processing with information-theoretic and statistical methods. Recent work has thus focused on the autocorrelation time of neural activity, also termed intrinsic timescale, which serves as a proxy for how long information is stored in the activity of the network. This work revealed a hierarchy of intrinsic timescales, which suggests that along the processing hierarchy, the brain

…perceptual inference is encoded in the mouse visual cortex. In illusory contour perception, the perceived whole is greater than the sum of its parts. As such, we hypothesized that brain regions encoding perceptual inference would show enhanced neu

…whether reactivations in sensory cortex depend on local activation of neurons during prior experience is not well understood. We hypothesize that such reactivations in sensory association cortex could be important for learnin

…laborious expert analysis through visual inspection or by computing a set of predefined features that could be quantitatively measured. Both approaches are prone to bias and subjectivity. The classification of neurons by experts was shown to have a high variance, and becomes infeasible with larger datasets. Similarly, the definition of fixed morphological features for classification

The statistical structure of neural population responses is a topic of intense interest in systems neuroscience. The signal eigenspectrum (eigenvalues ordered from high to low) is a fundamental summary statistic that characterizes the dimensionality and efficiency or redundancy of population tuning curves. However, the problem of characterizing signal eigenvalue spectra from limited, noisy neural response data is complicated by two factors. First, noise in the neural responses (aka ``noise correlations'') can interfere with the estimation of the eigenvalues of the signal distribution. Second,

…between thousands of neurons in the mouse visual cortex have heavy-tailed distributions spanning three decades in strength. Moreover, we find that these distributions are robust to variations in the visual stimuli, even persistin

…fractions of neural activity in the mouse brain. However, the specific behavioral features encoded and their consistency across mice remain unknown. To study the neural representation of interpretable behavioral features, we recorded the activity of ~50,000 neurons in the mouse visual cortex during spontaneous behaviors. We used simultaneous five-camera video recordings to show that orofacial behaviors contain