Topic: natural images

Natural visual environments are full of complex and diverse patterns. Recent neurophysiological experiments based on artificial patterns have suggested that macaque V1 neurons’ receptive fields are more complex and diverse in feature selectivity (Tang et al 2017) rather than just oriented Gabor filters. However, that finding was based on extensive parametric artificial stimuli used and might be biased. Hence, the nature of the neural codes of macaque V1 neurons remains controversial. To resolve this question, we performed 2-photon calcium imaging on three macaque monkeys to obtain 1689 V1 neur

Working memory (WM) allows us to hold information temporarily and make complex decisions beyond reflexive response to stimuli. One prominent feature of WM is its capacity limit. Despite decades of study, the root of this limit is still not well-understood. Most previous accounts for this limit assume various forms of memory constraints and make strong, often oversimplified, assumptions about sensory representations of stimuli. In this work, we built visual-cognitive neural network models of WM that process raw sensory stimuli. In contrast to intuitions that capacity limit results from memory c

An appealing hypothesis states that visual neurons in the brain are sensitive to the statistical properties of natural images. Neurophysiology and fMRI studies have tested neural sensitivity to images altered to exhibit either typical or atypical statistics. A general finding is that visual cortical neurons respond more strongly to images with natural statistics, and that this can vary across the visual hierarchy. Convolutional neural networks (CNNs) have achieved human-level performance in vision tasks and have been used as visual cortex models. However, CNN responses to images with altered s

The human visual cortex processes visual stimuli hierarchically. Early visual areas (V1, V2) of the ventral visual stream feed crude visual features (like orientation and edges) into later visual areas (V4, lateral occipital (Lat Occ), inferior temporal (IT)) that then encode complex visual features (like object form). Previous studies have reported a difference in fMRI responses between natural and urban landscapes in certain parts of the brain. Here we asked if this distinction in the representation of complex natural and man-made visual stimuli extends to the ventral visual stream and if st

An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes th

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will disc

The brain computes internal representations by applying highly recurrent dynamics to feed-forward input. Such dynamics may be viewed as analogous to the inference step in latent variables models, where one usually follows the gradient of the latent posterior distribution until a stable state is reached. At the stable state, when this gradient has approximately zero norm, a parameter optimization step can be applied to slightly increase the likelihood of the current sample under the model. Here we suggest this procedure, which closely matches the expectation-maximization (EM) algorithm, can be