Topic: linear readouts

Neural responses are often highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry of the neural representations. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects using their whiskers. High-speed videos of the whisker movements revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects a process of non-linear integration of spatio-temporal features of the sensory input

Many engineered and biological systems must trade off performance and energy use, and the brain is no exception. Over long time-scales, evidence suggests that energy use is kept approximately stable by homeostatic firing-rate set-points, resulting in stable average performance levels. On shorter timescales, however, this stability can and should be deviated from, such as during attention when both neural activity and performance often increase (e.g., in predator situations, or when attending to small sensory details). It remains unclear how this fundamental performance-energy trade-off can be

Constructing well-motivated methodologies for quantifying similarity between high-dimensional neural representations is an active research in computational neuroscience. Such similarity measures have implications for understanding variability in neural computations across individuals and species [1], as well as for comparisons between biological systems and computational models [2]. One common approach to understand these systems is to build regression models or “decoders” that reconstruct features of the stimulus from neural responses. Here, we leverage this idea to quantify the similarity of