Dynamic population codes of multiplexed stimulus features in primate area MT

Magnetoencephalography contrast adaptation reflects perceptual adaptation

Contrast adaptation is a fundamental visual process that has been extensively investigated and used to infer the selectivity of visual cortex. We recently reported an apparent disconnect between the effects of contrast adaptation on perception and functional magnetic resonance imaging BOLD response adaptation, in which adaptation between chromatic and achromatic stimuli measured psychophysically showed greater selectivity than adaptation measured using BOLD signals. Here we used magnetoencephalography (MEG) recordings of neural responses to the same chromatic and achromatic adaptation conditions to characterize the neural effects of contrast adaptation and to determine whether BOLD adaptation or MEG better reflect the measured perceptual effects. Participants viewed achromatic, L-M isolating, or S-cone isolating radial sinusoids before adaptation and after adaptation to each of the three contrast directions. We measured adaptation-related changes in the neural response to a range of stimulus contrast amplitudes using two measures of the MEG response: the overall response amplitude, and a novel time-resolved measure of the contrast response function, derived from a classification analysis combined with multidimensional scaling. Within-stimulus adaptation effects on the contrast response functions in each case showed a pattern of contrast-gain or a combination of contrast-gain and response-gain effects. Cross-stimulus adaptation conditions showed that adaptation effects were highly stimulus selective across early, ventral, and dorsal visual cortical areas, consistent with the perceptual effects.

Neural mechanism of priming in visual search

Selective attention affords scrutinizing items in our environment. However, attentional selection changes over time and across space. Empirically, repetition of visual search conditions changes attentional processing. Priming of pop-out is a vivid example. Repeatedly searching for the same pop-out search feature is accomplished with faster response times and fewer errors. We review the psychophysical background of priming of pop-out, focusing on the hypothesis that it arises through changes in visual selective attention. We also describe research done with macaque monkeys to understand the neural mechanisms supporting visual selective attention and priming of pop-out, and survey research on priming of pop-out using noninvasive brain measures with humans. We conclude by hypothesizing three alternative neural mechanisms and highlighting open questions.

Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

fMRI representational similarity analysis reveals graded preferences for chromatic and achromatic stimulus contrast across human visual cortex

Human visual cortex is partitioned into different functional areas that, from lower to higher, become increasingly selective and responsive to complex feature dimensions. Here we use a Representational Similarity Analysis (RSA) of fMRI-BOLD signals to make quantitative comparisons across LGN and multiple visual areas of the low-level stimulus information encoded in the patterns of voxel responses. Our stimulus set was picked to target the four functionally distinct subcortical channels that input visual cortex from the LGN: two achromatic sinewave stimuli that favor the responses of the high-temporal magnocellular and high-spatial parvocellular pathways, respectively, and two chromatic stimuli isolating the L/M-cone opponent and S-cone opponent pathways, respectively. Each stimulus type had three spatial extents to sample both foveal and para-central visual field. With the RSA, we compare quantitatively the response specializations for individual stimuli and combinations of stimuli in each area and how these change across visual cortex. First, our results replicate the known response preferences for motion/flicker in the dorsal visual areas. In addition, we identify two distinct gradients along the ventral visual stream. In the early visual areas (V1-V3), the strongest differential representation is for the achromatic high spatial frequency stimuli, suitable for form vision, and a very weak differentiation of chromatic versus achromatic contrast. Emerging in ventral occipital areas (V4, VO1 and VO2), however, is an increasingly strong separation of the responses to chromatic versus achromatic contrast and a decline in the high spatial frequency representation. These gradients provide new insight into how visual information is transformed across the visual cortex.

Weighting neurons by selectivity produces near-optimal population codes

Perception is produced by "reading out" the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons' spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets (Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron's tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron's preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron's tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher's linear discriminant) led to only marginally better performance than decoders based purely on a neuron's preferred direction and selectivity.

Interpreting the dimensions of neural feature representations revealed by dimensionality reduction

Recent progress in understanding the structure of neural representations in the cerebral cortex has centred around the application of multivariate classification analyses to measurements of brain activity. These analyses have proved a sensitive test of whether given brain regions provide information about specific perceptual or cognitive processes. An exciting extension of this approach is to infer the structure of this information, thereby drawing conclusions about the underlying neural representational space. These approaches rely on exploratory data-driven dimensionality reduction to extract the natural dimensions of neural spaces, including natural visual object and scene representations, semantic and conceptual knowledge, and working memory. However, the efficacy of these exploratory methods is unknown, because they have only been applied to representations in brain areas for which we have little or no secondary knowledge. One of the best-understood areas of the cerebral cortex is area MT of primate visual cortex, which is known to be important in motion analysis. To assess the effectiveness of dimensionality reduction for recovering neural representational space we applied several dimensionality reduction methods to multielectrode measurements of spiking activity obtained from area MT of marmoset monkeys, made while systematically varying the motion direction and speed of moving stimuli. Despite robust tuning at individual electrodes, and high classifier performance, dimensionality reduction rarely revealed dimensions for direction and speed. We use this example to illustrate important limitations of these analyses, and suggest a framework for how to best apply such methods to data where the structure of the neural representation is unknown.