Divisive normalization

…responses is typically modeled using divisive normalization or in purely descriptive terms, none of them can account for the bewildering range of effects documented for CSP of motion alone. Furthermore, a normative model that links these neural effects to function and behavior is still

Divisive normalization (DN), the division of a neuron’s activity by a pool of connected neurons, has been recognized as a general computational motif in the cortex. Arguments support that in the visual cortex, DN acts as a canonical computation in generative models, which delivers contextual effects and contributes to shaping response variability. Deep generative models (DGMs) have recently been adopted by neuroscience from machine vision to embrace nonlinear computations in the visual cortex. Yet, it remains unclear whether DGMs effectively integrate DN as a core computational element. We inv

Natural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals can perform accurate visual behaviors in nature, whereas computer vision devices struggle with changing environments. How do animal eyes achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control in Drosophila, resulting in stable visual processing. We identify the dendrites of specific third order neurons as the site of luminance gain control. The circuitry further involves wide field neurons, matching computational predictions that local spatial p

…E-I balance are important for gating, normalization, and representation of input ensembles. Using in vitro whole-cell recordings in mouse hippocampal slices, we probed E-I balance by optogenetically evok

…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

…Likewise, Divisive Normalization (DN) has been proposed as a canonical circuit motif [Heeger 2011], but there are competing theories about the computations that DN implements (gain control, attention). In this work, we unified both by showing how an extended DN model can account for neural predictions made by a CI model [Shivkumar et al. 2022] in the context of hierarchical motion perception. Specifically, we generated CI predicti

Spatial context plays an important role in visual processing and perception. A rich set of surround effects that involve nonlinear interactions between the center and surround have been found in Primary Visual Cortex (V1). A general summary is that surround suppression is prevalent and the amount of suppression depends on the visual similarity between the center and surround. In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have

…response nonlinearities such as divisive normalization, a canonical brain computation. However, when necessary connectivity co

Perceptual decisions are ubiquitous and essential in life, and even in their simplest forms open a window into the neural mechanisms underlying cognition. However, the properties of decision mechanisms inferred in simple behavioral paradigms are not independent of the properties of employed stimuli and task. One key task property is the type of information provided by a single stimulus, which can be either stationary or dynamic. Stationary paradigms are based on evidence with fixed central tendency, often obscured by noise, whereas in dynamic paradigms the mean of the evidence can vary within

Excitatory and inhibitory inputs to cortical neurons remain balanced across different conditions. The balanced network model (Van Vreeswijk & Sompolinsky 1998) provides a self-consistent account of this observation: under mild inequality conditions on connectivity parameters, population rates dynamically adjust to yield a state in which inputs to all neurons are tightly balanced, keeping all populations active at biological levels. Global tight balance, however, predicts a linear stimulus-dependence for population responses and cannot explain systematic cortical response nonlinearities

…Past work suggests that divisive normalization (DN), a cortical computation responsible for coordinating neural activity, could explain those modulations of neuronal covariability. However, quantifying the relation between DN and NCs remains challenging, as most successful descriptive models of neural variability do not account for DN. We propose a pairwise stochastic DN model that accounts for the effect of DN on covariability. In the model,

…key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimu