Topic: V4

The proliferation of deep artificial neural network models has given rise to widespread interest in comparing such models to biological sensory systems. Model metamers – stimuli that produce the same responses in a model as a natural stimulus – have been proposed as a powerful test of the extent to which a model replicates the invariances of a biological sensory system. However, it has remained unclear whether discrepancies evident from metamers are the same as those identified by other model comparison benchmarks, and whether modifications that have been introduced to address other well-known

The size of a neuron’s receptive field increases along the visual hierarchy. Neurons in higher-order visual areas integrate information through a canonical mechanism called normalization, where neurons respond sublinearly to multiple stimuli in the receptive field (Carandini & Heeger, 2012). Neurons in visual cortex exhibit highly heterogeneous degrees of normalization, which can be measured by normalization index, defined as the sum of a neuron’s firing rate to each one of two stimuli divided by its firing rate when both stimuli are presented. Interestingly, it has been demonstrated that

Primates exhibit a remarkable ability to rapidly and accurately recognize visual objects in their central field of view. Based on accurate estimates of object identity from neural population activity and the object-specific behavioral deficits observed after cortical perturbation, previous work has identified the primate ventral visual cortex as a critical brain circuit for core object recognition. In addition, our recent work has demonstrated that the ventrolateral prefrontal cortex (vlPFC; recurrently connected to the ventral stream) is also critical for robust object recognition. In this st

How the brain separates information about multiple objects despite overlap in the neurons responsive to each item is not well understood. It has recently been proposed that when more than one stimulus is present, single neurons can fluctuate between coding one vs. the other(s) across some time period, i.e. a form of neural multiplexing in the time domain (Caruso et al., 2018). However, it is not known (a) whether such fluctuations occur specifically when two distinct objects are present and not in other cases, and (b) how fluctuations in individual neurons might be coordinated with one anot

Neurons in intermediate visual cortical areas such as V2 and V4, unlike those in V1, respond preferentially to texture images containing naturalistic image statistics common to natural images. We wondered whether and how representations of these image statistics, which we term “naturalness”, changed during development. We made longitudinal behavioral and neural measurements from 2 infant macaque monkeys at the ages of 6 and 12 mo, using texture stimuli that were parametrically varied in naturalness. We used a novel visual search task to efficiently measure behavioral naturalness sensitivity. W

Cortical circuits receive multiple inputs from upstream populations with non-overlapping stimulus tuning preferences. Both the feedforward and recurrent architectures of the receiving cortical layer will reflect this diverse input tuning. We study how population-wide neuronal variability propagates through a hierarchical cortical network receiving multiple, independent, tuned inputs. We present new analysis of in vivo neural data from the primate visual system showing that the number of latent variables (dimension) needed to describe population shared variability is smaller in V4 populations c

Brain activity depends on ongoing behavioural and arousal states, such as attention, eye closure, or sleep. These states display measurable differences in population activity [1], dimensionality [2], noise correlations [3], and autocorrelation timescales [4]. State-dependent changes in the primary visual cortex (V1) are potentially induced by cortico-cortical signals, such as those from V4 to V1, known to mediate visual attention in macaques [5]. However, relationships between such top-down signals and the aforementioned state-dependent changes have not been demonstrated directly. To investig

Primates visually identify objects primarily by shape and also by texture. Models of primate vision based on convolutional neural networks (CNNs) achieve high object recognition performance but, unlike primates, emphasize texture over shape. Overcoming this texture bias and making models more brain-like requires data sets that distinguish how shape and texture cues interact in mid-level visual processing. To this end, we created sets of images that parametrically varied between objects and textures, and recorded responses to these images from populations of single units in macaque mid-level vi

Current approaches to cortical prosthetic vision rely on electrically evoked phosphenes, whose dynamic appearance in the visual field could enable functional sight in acquired blindness. Akin to an input/output dynamic-range problem, the spatiotemporal resolution of phosphene-based vision is constrained by the accuracy with which activity can be modulated in V1 and simultaneously, by the discriminability of evoked activity in higher visual areas (i.e. V2 and V4), for different stimulation parameters. However, the high-dimensionality of the stimulation parameter space makes impractical to exten

Flexible routing of visual input signals has been linked to changing patterns of synchrony between gamma rhythms of distant neural groups. It has been proposed that attention increases distant gamma-band synchrony by increasing the gamma frequency of the sender group processing the attended stimulus. This stimulus is subsequently preferentially processed. However, the frequency of gamma activity fluctuates over time, varies between areas, and depends on stimulus characteristics, which may complicate entrainment between oscillators. Here, we investigate the relationship between the frequency of

Ripples (strong, short-lived oscillations in the local field potential associated with memory consolidation) have been previously reported in several brain areas including the hippocampus and neocortex. Compared to hippocampal ripples, relatively little is known about cortical ripples in terms of their spread and function. We inspected the ripple activity in the frequency range of 70-150 Hz in V1 and V4 of two macaque monkeys implanted with 16 Utah electrode arrays each [1]. The activity was recorded during resting state with occasional periods of drowsing. We observed prominent events with hi