Topic: mouse visual cortex

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

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

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