The spatial profile of macaque MT neurons is consistent with Gaussian sampling of logarithmically coordinated visual representation

Representation of Motion Direction in Visual Area MT Accounts for High Sensitivity to Centripetal Motion, Aligning with Efficient Coding of Retinal Motion Statistics

The overrepresentation of centrifugal motion in the middle temporal visual area (area MT) has long been thought to provide an efficient coding strategy for optic flow processing. However, this overrepresentation compromises the detection of approaching objects, which is essential for survival. In the present study, we revisited this long-held notion by reanalyzing motion selectivity in area MT of three macaque monkeys (two males, one female) using random-dot stimuli instead of spot stimuli. We found no differences in the number of neurons tuned to centrifugal versus centripetal motion; however, centrifugally tuned neurons showed stronger tuning than centripetally tuned neurons. This was attributed to the heightened suppression of responses in centrifugal neurons to centripetal motion compared with that of centripetal neurons to centrifugal motion. Our modeling implies that this intensified suppression accounts for superior detection performance for weak centripetal motion stimuli. Moreover, through Fisher information analysis, we establish that the population sensitivity to motion direction in peripheral vision corresponds well with retinal motion statistics during forward locomotion. While these results challenge established concepts, considering the interplay of logarithmic Gaussian receptive fields and spot stimuli can shed light on the previously documented overrepresentation of centrifugal motion. Significantly, our findings reconcile a previously found discrepancy between MT activity and human behavior, highlighting the proficiency of peripheral MT neurons in encoding motion direction efficiently.SIGNIFICANCE STATEMENT The efficient coding hypothesis states that sensory neurons are tuned to specific, frequently experienced stimuli. Whereas previous work has found that neurons in the middle temporal (MT) area favor centrifugal motion, which results from forward locomotion, we show here that there is no such bias. Moreover, we found that the response of centrifugal neurons for centripetal motion was more suppressed than that of centripetal neurons for centrifugal motion. Combined with modeling, this provides a solution to a previously known discrepancy between reported centrifugal bias in MT and better detection of centripetal motion by human observers. Additionally, we show that population sensitivity in peripheral MT neurons conforms to an efficient code of retinal motion statistics during forward locomotion.

Bayesian connective field modeling using a Markov Chain Monte Carlo approach

The majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modelling is a biologically-grounded method to describe this essential aspect of the brain's circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modelling translates the concept of the receptive field (RF) into the domain of connectivity by assessing, at the voxel level, the spatial dependency between signals in distinct cortical visual field areas. Thus, the approach enables to characterize the functional cortical circuitry of the human cortex. While already very useful, the present CF modelling approach has some intrinsic limitations due to the fact that it only estimates the model's explained variance and not the probability distribution associated with the estimated parameters. If we could resolve this, CF modelling would lend itself much better for statistical comparisons at the level of single voxels and individuals. This is important when trying to gain a detailed understanding of the neurobiology and pathophysiology of the visual cortex, notably in rare cases. To enable this, we present a Bayesian approach to CF modeling (bCF). Using a Markov Chain Monte Carlo (MCMC) procedure, it estimates the posterior probability distribution underlying the CF parameters. Based on this, bCF quantifies, at the voxel level, the uncertainty associated with each parameter estimate. This information can be used in various ways to increase confidence in the CF model predictions. We applied bCF to BOLD responses recorded in the early human visual cortex using 3T fMRI. We estimated both the CF parameters and their associated uncertainties and show they are only weakly correlated. Moreover, we show how bCF facilitates the use of effect size (beta) as a data-driven parameter that can be used to select the most reliable voxels for further analysis. Finally, to further illustrate the functionality gained by bCF, we apply it to perform a voxel-level comparison of a single, circular symmetric, Gaussian versus a Difference-of-Gaussian model. We conclude that our bCF framework provides a comprehensive tool to study human functional cortical circuitry in health and disease.

Neural-like computing with populations of superparamagnetic basis functions

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.

Population coding, where populations of artificial neurons process information collectively can facilitate robust data processing, but require high circuit overheads. Here, the authors realize this approach with reduced circuit area and power consumption, by utilizing superparamagnetic tunnel junction based neurons.

Frequency Selectivity of Voxel-by-Voxel Functional Connectivity in Human Auditory Cortex

While functional connectivity in the human cortex has been increasingly studied, its relationship to cortical representation of sensory features has not been documented as much. We used functional magnetic resonance imaging to demonstrate that voxel-by-voxel intrinsic functional connectivity (FC) is selective to frequency preference of voxels in the human auditory cortex. Thus, FC was significantly higher for voxels with similar frequency tuning than for voxels with dissimilar tuning functions. Frequency-selective FC, measured via the correlation of residual hemodynamic activity, was not explained by generic FC that is dependent on spatial distance over the cortex. This pattern remained even when FC was computed using residual activity taken from resting epochs. Further analysis showed that voxels in the core fields in the right hemisphere have a higher frequency selectivity in within-area FC than their counterpart in the left hemisphere, or than in the noncore-fields in the same hemisphere. Frequency-selective FC is consistent with previous findings of topographically organized FC in the human visual and motor cortices. The high degree of frequency selectivity in the right core area is in line with findings and theoretical proposals regarding the asymmetry of human auditory cortex for spectral processing.

Visual impairment by surrounding noise is due to interactions among stimuli in the higher-order visual cortex

Observers have difficulty identifying a target in their peripheral vision in the presence of surrounding stimuli. Although hypotheses addressing this phenomenon have been proposed, such as the integration of stimuli and surround suppression in the higher-order visual cortex, no direct comparisons of the psychophysical and neuronal sensitivities have been performed. Here we measured the performance of monkeys with a variant of the direction discrimination task using a center/surround bipartite random-dot stimulus while simultaneously recording from isolated neurons from the middle temporal visual area (MT). The psychophysical threshold increased with the addition of a task-irrelevant noise annulus that surrounded the task-relevant motion stimuli. The neuronal threshold of MT neurons also increased at a spatial scale similar to the psychophysical threshold. This suggests that the impaired ability in our task resulted from impairment in the MT area. Importantly, reduced neuronal performance was due to both a reduced response to preferred motion and an enhanced response to nonpreferred motion. These observations suggest that impairment caused by surrounding noise results from interactions between stimuli and noise and not from a reduction in the response of visual neurons.

Responses to random dot motion reveal prevalence of pattern-motion selectivity in area MT

How the visual system reconstructs global patterns of motion from components is an important issue in vision. Conventional studies using plaids have shown that approximately one-third of neurons in cortical area MT respond to one-dimensional (1D) components of a moving pattern (component cells), whereas another third responds to the global two-dimensional (2D) motion of a pattern (pattern cells). Conversely, studies using spots of light or random dots that contain multiple orientations have seldom reported directional tuning that is consistent with 1D motion preference. To bridge the gap between these studies, we recorded from isolated neurons in macaque area MT and measured tuning for velocity (direction and speed) using random dot stimuli. We used the "intersection of constraints" principle to classify our population into pattern-direction-selective (PDS) neurons and component-direction-selective (CDS) neurons. We found a larger proportion of PDS cells (68%) and a smaller proportion of CDS cells (8%) compared with prior studies using plaids. We further compared velocity tuning, measured using random dot stimuli, with direction tuning, measured using plaids. Although there was a correlation between the degree of preference for 2D over 1D motion of the two measurements, tuning seemed to prefer 2D motion using random dot stimuli. Modeling analyses suggest that integration across orientations contributes to the 2D motion preference of both dots and plaids, but opponent inhibition mainly contributes to the 2D motion preference of plaids. We conclude that MT neurons become more capable of identifying a particular 2D velocity when stimuli contain multiple orientations.

Compressive spatial summation in human visual cortex

Neurons within a small (a few cubic millimeters) region of visual cortex respond to stimuli within a restricted region of the visual field. Previous studies have characterized the population response of such neurons using a model that sums contrast linearly across the visual field. In this study, we tested linear spatial summation of population responses using blood oxygenation level-dependent (BOLD) functional MRI. We measured BOLD responses to a systematic set of contrast patterns and discovered systematic deviation from linearity: the data are more accurately explained by a model in which a compressive static nonlinearity is applied after linear spatial summation. We found that the nonlinearity is present in early visual areas (e.g., V1, V2) and grows more pronounced in relatively anterior extrastriate areas (e.g., LO-2, VO-2). We then analyzed the effect of compressive spatial summation in terms of changes in the position and size of a viewed object. Compressive spatial summation is consistent with tolerance to changes in position and size, an important characteristic of object representation.

Connective field modeling

The traditional way to study the properties of visual neurons is to measure their responses to visually presented stimuli. A second way to understand visual neurons is to characterize their responses in terms of activity elsewhere in the brain. Understanding the relationships between responses in distinct locations in the visual system is essential to clarify this network of cortical signaling pathways. Here, we describe and validate connective field modeling, a model-based analysis for estimating the dependence between signals in distinct cortical regions using functional magnetic resonance imaging (fMRI). Just as the receptive field of a visual neuron predicts its response as a function of stimulus position, the connective field of a neuron predicts its response as a function of activity in another part of the brain. Connective field modeling opens up a wide range of research opportunities to study information processing in the visual system and other topographically organized cortices.

Reduction in receptive field size of macaque MT neurons in the presence of visual noise

The visual system faces a trade-off between increased spatial integration of disparate local signals and improved spatial resolution to filter out irrelevant noise. Increased spatial integration is beneficial when signals are weak, whereas increased spatial resolution is particularly beneficial when focusing on a small object in a cluttered natural scene. The receptive field (RF) size of visual cortical neurons can be modulated depending on various factors such as sensory context, allowing adaptive integration of sensory signals. In this study, we explored the spatial integration properties of neurons in macaque middle temporal visual area (MT). We hypothesized that spatial resolution would increase when high-contrast noise was presented simultaneously with a visual stimulus, enabling focus on a small object in a cluttered scene. To test this hypothesis, we mapped the RFs of MT neurons of two fixating monkeys in a 5 × 5 grid manner using a small patch of random-dot motion. To examine the effects of noise on RF profile, a dynamic noise (0% coherence dots) of varying diameter was concurrently presented at the RF center. We found that RF size decreased when noise diameter increased. Analyses based on the response normalization model and area summation provided evidence for the potential contribution of spatial summation properties within the RF and surround suppression to the apparent contraction of RF size. Our results suggest that MT neurons integrate smaller regions of motion signals when signals are embedded in noise, an efficient strategy to filter out surrounding noise.