Spatial summation in macaque parietal area 7a follows a winner-take-all rule

A neural model for vector decomposition and relative-motion perception

The perception of motion not only depends on the detection of motion signals but also on choosing and applying reference-frames according to which motion is interpreted. Here we propose a neural model that implements the common-fate principle for reference-frame selection. The model starts with a retinotopic layer of directionally-tuned motion detectors. The Gestalt common-fate principle is applied to the activities of these detectors to implement in two neural populations the direction and the magnitude (speed) of the reference-frame. The output activities of retinotopic motion-detectors are decomposed using the direction of the reference-frame. The direction and magnitude of the reference-frame are then applied to these decomposed motion-vectors to generate activities that reflect relative-motion perception, i.e., the perception of motion with respect to the prevailing reference-frame. We simulated this model for classical relative motion stimuli, viz., the three-dot, rotating-wheel, and point-walker (biological motion) paradigms and found the model performance to be close to theoretical vector decomposition values. In the three-dot paradigm, the model made the prediction of perceived curved-trajectories for the target dot when its horizontal velocity was slower or faster than the flanking dots. We tested this prediction in two psychophysical experiments and found a good qualitative and quantitative agreement between the model and the data. Our results show that a simple neural network using solely motion information can account for the perception of group and relative motion.

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake nonhuman primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF models based on the fMRI blood-oxygen-level-dependent (BOLD) signal, multi-unit spiking activity (MUA), and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. fMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF size with increasing eccentricity, as well as a retinotopically specific deactivation of default mode network nodes similar to previous observations in humans.

A simple vector-like law for perceptual information combination is also followed by a class of cortical multisensory bimodal neurons

An interdisciplinary approach to sensory information combination shows a correspondence between perceptual and neural measures of nonlinear multisensory integration. In psychophysics, sensory information combinations are often characterized by the Minkowski formula, but the neural substrates of many psychophysical multisensory interactions are unknown. We show that audiovisual interactions - for both psychophysical detection threshold data and cortical bimodal neurons - obey similar vector-like Minkowski models, suggesting that cortical bimodal neurons could underlie multisensory perceptual sensitivity. An alternative Bayesian model is not a good predictor of cortical bimodal response. In contrast to cortex, audiovisual data from superior colliculus resembles the 'City-Block' combination rule used in perceptual similarity metrics. Previous work found a simple power law amplification rule is followed for perceptual appearance measures and by cortical subthreshold multisensory neurons. The two most studied neural cell classes in cortical multisensory interactions may provide neural substrates for two important perceptual modes: appearance-based and performance-based perception.

Visual and Vestibular Selectivity for Self-Motion in Macaque Posterior Parietal Area 7a

We examined the responses of neurons in posterior parietal area 7a to passive rotational and translational self-motion stimuli, while systematically varying the speed of visually simulated (optic flow cues) or actual (vestibular cues) self-motion. Contrary to a general belief that responses in area 7a are predominantly visual, we found evidence for a vestibular dominance in self-motion processing. Only a small fraction of neurons showed multisensory convergence of visual/vestibular and linear/angular self-motion cues. These findings suggest possibly independent neuronal population codes for visual versus vestibular and linear versus angular self-motion. Neural responses scaled with self-motion magnitude (i.e., speed) but temporal dynamics were diverse across the population. Analyses of laminar recordings showed a strong distance-dependent decrease for correlations in stimulus-induced (signal correlation) and stimulus-independent (noise correlation) components of spike-count variability, supporting the notion that neurons are spatially clustered with respect to their sensory representation of motion. Single-unit and multiunit response patterns were also correlated, but no other systematic dependencies on cortical layers or columns were observed. These findings describe a likely independent multimodal neural code for linear and angular self-motion in a posterior parietal area of the macaque brain that is connected to the hippocampal formation.

A Normalization Circuit Underlying Coding of Spatial Attention in Primate Lateral Prefrontal Cortex

Lateral prefrontal cortex (LPFC) neurons signal the allocation of voluntary attention; however, the neural computations underlying this function remain unknown. To investigate this, we recorded from neuronal ensembles in the LPFC of two Macaca fascicularis performing a visuospatial attention task. LPFC neural responses to a single stimulus were normalized when additional stimuli/distracters appeared across the visual field and were well-characterized by an averaging computation. Deploying attention toward an individual stimulus surrounded by distracters shifted neural activity from an averaging regime toward a regime similar to that when the attended stimulus was presented in isolation (winner-take-all; WTA). However, attentional modulation is both qualitatively and quantitatively dependent on a neuron's visuospatial tuning. Our results show that during attentive vision, LPFC neuronal ensemble activity can be robustly read out by downstream areas to generate motor commands, and/or fed back into sensory areas to filter out distracter signals in favor of target signals.

Unconscious Biases in Neural Populations Coding Multiple Stimuli

Throughout the nervous system, information is commonly coded in activity distributed over populations of neurons. In idealized situations where a single, continuous stimulus is encoded in a homogeneous population code, the value of the encoded stimulus can be read out without bias. However, in many situations, multiple stimuli are simultaneously present; for example, multiple motion patterns might overlap. Here we find that when multiple stimuli that overlap in their neural representation are simultaneously encoded in the population, biases in the read-out emerge. Although the bias disappears in the absence of noise, the bias is remarkably persistent at low noise levels. The bias can be reduced by competitive encoding schemes or by employing complex decoders. To study the origin of the bias, we develop a novel general framework based on gaussian processes that allows an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli. The results have implications for neural coding and behavioral experiments on, for instance, overlapping motion patterns.

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Spatial summation revealed in the earliest visual evoked component C1 and the effect of attention on its linearity

In natural scenes, multiple objects are usually presented simultaneously. How do specific areas of the brain respond to multiple objects based on their responses to each individual object? Previous functional magnetic resonance imaging (fMRI) studies have shown that the activity induced by a multiobject stimulus in the primary visual cortex (V1) can be predicted by the linear or nonlinear sum of the activities induced by its component objects. However, there has been little evidence from electroencephelogram (EEG) studies so far. Here we explored how V1 responded to multiple objects by comparing the EEG signals evoked by a three-grating stimulus with those evoked by its two components (the central grating and 2 flanking gratings). We focused on the earliest visual component C1 (onset latency of ∼50 ms) because it has been shown to reflect the feedforward responses of neurons in V1. We found that when the stimulus was unattended, the amplitude of the C1 evoked by the three-grating stimulus roughly equaled the sum of the amplitudes of the C1s evoked by its two components, regardless of the distances between these gratings. When the stimulus was attended, this linear spatial summation existed only when the three gratings were far apart from each other. When the three gratings were close to each other, the spatial summation became compressed. These results suggest that the earliest visual responses in V1 follow a linear summation rule when attention is not involved and that attention can affect the earliest interactions between multiple objects.

The responses of V1 cortical neurons to flashed presentations of orthogonal single lines and edges

How cortical neurons process multiple inputs is a fundamental issue in modern neuroscience. Neurons in visual cortical area V1 have been shown to exhibit cross-orientation suppression, where the response to an optimally oriented visual stimulus is reduced by the simultaneous presence of an orthogonally oriented stimulus. This is consistent with the view that cortical neurons respond to multiple inputs with a weighted average (or normalization) of the responses to the inputs presented separately. However, most of these studies have used drifting or counterphase-modulated grating stimuli, potentially confounding orientation effects with non-orientation-specific gain control mechanisms. Additionally, primate vision depends to a great extent on transient stimulus presentations during fixations between saccades. Therefore this study examined the responses of primate V1 neurons to orthogonal flashed-onset single edges and lines, and to their combinations. Single edges or lines do not typically cause strong suppression of the responses to an orthogonal stimulus, even though a grating does. This appears to hold true regardless of the relative contrasts of the orthogonal single lines or edges. This is consistent with response suppression from an orthogonal grating being due to non-orientation-specific contrast gain control (Koeling M, Shapley R, Shelley M. J Comp Neurosci 25: 390-400, 2008; Priebe NJ, Ferster D. Nat Neurosci 9: 552-561, 2006; Walker GA, Ohzawa I, Freeman RD. J.Neurophysiol 79: 227-239, 1998). While normalization mechanisms are clearly important for the cerebral cortex, under many conditions the responses of V1 cortical neurons to an optimally oriented stimulus can be unaffected by the presence of orthogonal stimuli, which may be important to avoid confounding the interpretation of a neural response.

Single neuron activity and theta modulation in postrhinal cortex during visual object discrimination

Postrhinal cortex, rodent homolog of the primate parahippocampal cortex, processes spatial and contextual information. Our hypothesis of postrhinal function is that it serves to encode context, in part, by forming representations that link objects to places. To test this hypothesis, we recorded postrhinal neurons and local field potentials (LFPs) in rats trained on a two-choice, visual discrimination task. As predicted, many postrhinal neurons signaled object-location conjunctions. Another large proportion encoded egocentric motor responses. In addition, postrhinal LFPs exhibited strong oscillatory rhythms in the theta band, and many postrhinal neurons were phase locked to theta. Although correlated with running speed, theta power was lower than predicted by speed alone immediately before and after choice. However, theta power was significantly increased following incorrect decisions, suggesting a role in signaling error. These findings provide evidence that postrhinal cortex encodes representations that link objects to places and suggest postrhinal theta modulation extends to cognitive as well as spatial functions.

Divisive normalization and neuronal oscillations in a single hierarchical framework of selective visual attention

Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in gamma-band frequencies (25–100 Hz). Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to integrate these concepts into a single framework of attention. Here, we aim to provide such a unified framework by expanding the normalization model of attention with a multi-level hierarchical structure and a time dimension; allowing the simulation of a recently reported backward progression of attentional effects along the visual cortical hierarchy. A simple cascade of normalization models simulating different cortical areas is shown to cause signal degradation and a loss of stimulus discriminability over time. To negate this degradation and ensure stable neuronal stimulus representations, we incorporate a kind of oscillatory phase entrainment into our model that has previously been proposed as the “communication-through-coherence” (CTC) hypothesis. Our analysis shows that divisive normalization and oscillation models can complement each other in a unified account of the neural mechanisms of selective visual attention. The resulting hierarchical normalization and oscillation (HNO) model reproduces several additional spatial and temporal aspects of attentional modulation and predicts a latency effect on neuronal responses as a result of cued attention.

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.

Short-time scale dynamics in the responses to multiple stimuli in visual cortex

Many previous studies have used the presentation of multiple stimuli in the receptive fields (RFs) of visual cortical neurons to explore how neurons might operate on multiple inputs. Most of these experiments have used two fixed stimulus locations within the RF of each neuron. Here the effects of using different positions within the RF of a neuron were explored. The stimuli were presented singly at one of six locations, and also at 15 pair-wise combinations, for 24 V2 cortical neurons in two macaque monkeys. There was considerable variability in how pairs of stimuli interacted within the receptive field of any given neuron: changing the position of the stimuli could result in enhancement, winner-take-all, or suppression relative to the strongest response to a stimulus presented by itself. Across the population of neurons there was no correlation between response strength and response latency. However, for many stimulus pairs the response latency was tightly locked to the shortest response latency of any single stimulus presented by itself independent of changes in response magnitude. In other words, a stimulus that by itself elicited a relatively long latency response, would often affect the magnitude of the response to a pair of stimuli, but not change the latency. These results may provide constraints on the development of models of cortical information processing.