Topic: redundancy reduction

Sensory neurons in many organisms and brain areas exhibit a form of local gain control that has been described as "divisive normalization'' (Carandini & Heeger, 2011) in which the stimulus-driven activity of each neuron is divided by a factor involving the summed activity of a group of neurons. Redundancy reduction has been proposed as normative explanation of divisive normalization, and previous work has used this principle for learning normalization models (Schwartz & Simoncelli, 2000). Specifically, redundancy in filtered representations of images can be reduced by dividing by an es

Biological visual systems learn complex representations of the world that support a wide range of cognitive behaviors without using a large number of labelled examples. The efficient coding hypothesis suggests that this is accomplished by adapting the sensory representation to the statistics of the input signal, i.e. in a way that facilitates redundancy reduction. Visual signals have several clear sources of redundancy. They evolve slowly in time, since temporally adjacent inputs typically correspond to different views of the same scene, which in turn are usually more similar than views of dis

Adaptive motor control requires the integration of proprioceptive and other sensory signals. However, the principles, which govern the processing of proprioception, are poorly understood. Here, we employ a task-driven modeling approach to quantitatively test hypotheses about the functional role of proprioceptive neurons in cuneate nucleus, which is the projection target of ascending spindle neurons in the brain stem. We generated datasets of realistic proprioceptive, muscle spindle inputs for a large, diverse repertoire of movements (following Sandbrink et al., bioRxiv, 2020), and used them to

The sequential analysis of information in a coarse-to-fine (CtF) manner is a fundamental processing strategy of the visual system. Previous studies have shown that neurons in the primary visual cortex (V1) of anesthetized animals can process spatial information in a CtF fashion, shifting their spatial frequency (SF) preference from low (coarse) to high (fine) throughout their response to static grating stimuli. However, many central questions regarding CtF processing, such as whether it occurs in awake behaving mice and potential computational advantages it may provide, remain unexplored. Here