Hemifield-Specific Motion Extrapolation Reveals Limits of Interhemispheric Integration

An understanding of visual function at the cerebral cortical level requires detailed knowledge of anatomical connectivity. Cortical association pathways and terminations of preoccipital visual areas were investigated in rhesus monkeys by using the autoradiographic tracing technique. Medial and adjacent dorsomedial preoccipital regions project via the occipitofrontal fascicle to the frontal lobe (dorsal area 6, and areas 8Ad, 8B, and 46); via the dorsal portion of the superior longitudinal fascicle (SLF) to dorsal area 6, area 9, and the supplementary motor area; and via the cingulate fascicle to area 24. In addition, medial and dorsomedial preoccipital areas send projections to parietal (areas PGm, PEa, PG-Opt, and POa) and superior temporal (areas MST and MT) regions. In contrast, connections from the dorsolateral, annectant, and ventral preoccipital regions are conveyed via the inferior longitudinal fascicle (ILF) to the parietal lobe (areas POa and IPd), superior temporal sulcus (areas MT, MST, FST, V4t, and IPa), inferotemporal region (areas TEO and TE1-TE3), and parahippocampal gyrus (areas TF, TH, and TL). The central-lateral preoccipital region projects via an ILF-SLF pathway to frontal area 8Av. The preoccipital areas also have caudal connections to occipital areas V1, V2, and V3. Finally, preoccipital regions are interconnected via different intrinsic pathways. These findings provide further insight into the nature of preoccipital fiber pathways and the connectional organization of the visual system.

Electrophysiological correlates of motion extrapolation: An investigation on the CNV

Event Abstract Back to Event Area MT pattern motion selectivity by integrating 1D and 2D motion features from V1 - a neural model Problem The neural mechanisms of detecting, integrating, and disambiguating visual motion still remain largely a puzzle. While many neurons in area V1 show coarse direction and speed selectivity signaling component motion direction, evidence suggests that end-stop cells in V1 compute the true motion direction for 2D image features (Pack et al., Neuron 39, 2003). In area MT, neurons have been found that are selective to speed and respond to pattern motion direction in the case of plaids from combined differently oriented gratings (Movshon et al., Pattern Recognition Mechanisms, 1985). For elongated bars, MT neurons resolve the so-called aperture problem to signal the correct object motion after a temporal course of disambiguation (Pack&Born, Nature 409, 2001). The construction of selectivity to pattern motion from component selective input is a topic of ongoing research. Different experiments suggest that the visual system might use computational strategies of integrating different motion directions or selecting localized features (Pack&Born, The Senses: A Comprehensive Reference, 2008). Methods We propose a neurodynamical model of motion integration in areas V1, MT, and MSTv which unifies the integrationist and selectionist concepts. Normal flow responses are initially detected by model V1 simple/complex cells while inhibitory interactions between cells at different V1 laminae generate local end-stop responses after a temporal delay. These responses are integrated for a short temporal episode and enhanced to generate strong direction signals. MT neurons then receive input from V1 complex and end-stop neurons (size ratio 1:5, direction tuning +/-40°). The integration of component selective responses leads to weak pattern direction selectivity in MT, further sharpened by the input from localized end-stop neurons. Feedback from MT to V1 enhances neurons with feature selectivity and slightly reduces normal flow responses near localized features. Feedforward and feedback connections between MT and MSTv (size ratio 1:1.25) disambiguate MT responses along extended outline boundaries. Results and Conclusion To investigate the aperture problem, we tested the model with a vertically aligned bar moving in diagonal direction. Consistent with neurophysiological findings (Pack&Born, Nature 409, 2001), after few iterations MT neurons showed sharp speed and direction tuning for the correct velocity, while the tuning of V1 neurons only slightly changed. Originating from 2D signals at corners, the correct flow was further propagated in MT along the stimulus by each iteration while propagation time increased with bar length (Born et al., Prog. Brain Res. 140, 2002). For a plaid stimulus, model MT neurons were tuned to pattern motion after some iterations, while V1 neurons indicated normal flow responses along the plaid components, except for end-stop neurons located at crossings. Hence, V1 neurons already provided some of the correct 2D cues, their overall tuning nevertheless remained coarse in speed and direction. To conclude, the integration of 1D and 2D V1 motion features in MT as proposed by our model suggests the generation of MT cell pattern selectivity based on component integration and feature selection. The model explains findings of various neurophysiological experiments and unifies different modeling approaches into one framework. Supported by EU-project 027198 (Decisions-in-Motion) Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Area MT pattern motion selectivity by integrating 1D and 2D motion features from V1 - a neural model. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.163 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Feb 2009; Published Online: 04 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

We sought to better understand the neural representation of visual motion acceleration. A straightforward estimation of acceleration would involve calculating the rate of change of velocity, which itself would be calculated from change in position over time. As it is well-established that neurons in area MT encode velocity, the brain could thus indirectly estimate acceleration by calculating the change in MT’s velocity representations. An alternate mechanism, however, could operate more rapidly and directly. In this case, the brain could exploit interactions between MT’s standard motion encoding and idiosyncratic temporal dynamics of neural responses. Such direct decoding would thus exploit nonlinearities usually ignored in studies of MT coding. We tested between these two theories by measuring from ensembles of MT neurons while two male awake fixating macaques viewed linearly accelerating motion stimuli. Direct decoding of acceleration from MT was possible on faster time scales, and could be done with higher fidelity, than indirect decoding. Distinct motion acceleration information could thus be efficiently read out from rich and heterogeneous MT ensemble responses, regardless of the mechanisms that give rise to various forms of motion tuning that it exhibits. A similar analysis of activity in the medial superior temporal area (MST) did not suggest this later stage of motion processing has a more refined acceleration representation. Together, these results suggest that the brain may opportunistically exploit nonlinear idiosyncrasies of neural responses to efficiently extract behaviorally relevant information on fast time scales, instead of performing explicit calculation of some variables.

A sudden change in the direction of motion is a particularly salient and relevant feature of visual information. Extensive research has identified cortical areas responsive to visual motion and characterized their sensitivity to different features of motion, such as directional specificity. However, relatively little is known about responses to sudden changes in direction. Electrophysiological data from animals and functional imaging data from humans suggest a number of brain areas responsive to motion, presumably working as a network. Temporal patterns of activity allow the same network to process information in different ways. The present study in humans sought to determine which motion-sensitive areas are involved in processing changes in the direction of motion and to characterize the temporal patterns of processing within this network of brain regions. To accomplish this, we used both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The fMRI data were used as supplementary information in the localization of MEG sources. The change in the direction of visual motion was found to activate a number of areas, each displaying a different temporal behavior. The fMRI revealed motion-related activity in areas MT+ (the human homologue of monkey middle temporal area and possibly also other motion sensitive areas next to MT), a region near the posterior end of the superior temporal sulcus (pSTS), V3A, and V1/V2. The MEG data suggested additional frontal sources. An equivalent dipole model for the generators of MEG signals indicated activity in MT+, starting at 130 ms and peaking at 170 ms after the reversal of the direction of motion, and then again at approximately 260 ms. Frontal activity began 0-20 ms later than in MT+, and peaked approximately 180 ms. Both pSTS and FEF+ showed long-duration activity continuing over the latency range of 200-400 ms. MEG responses in the region of V3A and V1/V2 were relatively small, and peaked at longer latencies than the initial peak in MT+. These data revealed characteristic patterns of activity in this cortical network for processing sudden changes in the direction of visual motion.

Auditory and visual attention modulate motion processing in area MT+

Nonspatially selective attention is based on the notion that specific features or objects in the visual environment are effectively prioritized in cortical visual processing. Feature-based attention (FBA), in particular, is a well-studied process that dynamically and selectively addresses neurons preferentially processing the attended feature attribute (e.g., leftward motion). In everyday life, however, behavior may require high sensitivity for an entire feature dimension (e.g., motion), but experimental evidence for a feature dimension-specific attentional modulation on a cellular level is lacking. Therefore, we investigated neuronal activity in macaque motion-selective mediotemporal area (MT) in an experimental setting requiring the monkeys to detect either a motion change or a color change. We hypothesized that neural activity in MT is enhanced when the task requires perceptual sensitivity to motion. In line with this, we found that mean firing rates were higher in the motion task and that response variability and latency were lower compared with values in the color task, despite identical visual stimulation. This task-specific, dimension-based modulation of motion processing emerged already in the absence of visual input, was independent of the relation between the attended and stimulating motion direction, and was accompanied by a spatially global reduction of neuronal variability. The results provide single-cell support for the hypothesis of a feature dimension-specific top-down signal emphasizing the processing of an entire feature class.NEW & NOTEWORTHY Cortical processing serving visual perception prioritizes information according to current task requirements. We provide evidence in favor of a dimension-based attentional mechanism addressing all neurons that process visual information in the task-relevant feature domain. Behavioral tasks required monkeys to attend either color or motion, causing modulations of response strength, variability, latency, and baseline activity of motion-selective monkey area MT neurons irrespective of the attended motion direction but specific to the attended feature dimension.

We presented a mechanical target moving at constant velocity to awake, nontrained, head-restrained cats, in order to study how naive animals pursue objects moving at a high speed with their gaze. Eye movements were recorded while the target was moving in different directions at a constant velocity (20-80 degrees/s) through the center of the visual field. We observed two oculomotor strategies: cats either made an interception saccade (IS) toward the target but opposite to its motion, or tracked it in the direction of motion. They used the interception strategy more frequently when the gaze position error at the onset of target motion was large, and the tracking strategy when it was small. Interception was always achieved by single saccades, which were faster than tracking saccades (TS). During tracking, cats generated sequences of two to six saccades separated by "smooth" eye movements. Tracking quality varied considerably from trial to trial. When the level of motivation was high, cats would track the target at 80 degrees/s over up to 75% of the oculomotor range, with relatively small position errors. We compared ISs and TSs with respect to their metric properties and timing. The amplitudes of ISs positively correlated with position error existing 100 ms before saccade onset, but saccade vectors were directed to a point ahead of the target along the target's track. We conclude that, in programming the ISs, target motion is used to predict the future target position so as to assure a spatial lead of the gaze at the saccade end, instead of attempting a precise capture of the target. The amplitude of TSs did not depend on preceding position errors. TSs were usually small at the onset of the first saccade, as if cats would wait till the target arrived near the line of sight. A majority of primary TSs were initiated before the target arrived near the direction of gaze. Thus they had a direction, opposite to the position error sampled 100 ms before the saccade, but the same as the direction of target motion. Prediction of the future target position from its velocity vector should therefore contribute to the programming of TSs. In addition, we observed that TSs were faster when they were initiated with a spatial lag relative to the target and they were slower if there was a spatial lead or target velocity was reduced. Such a modulation appears to be analogous to the predictive correction of the saccade amplitude during smooth pursuit in primates. Considering strong visual motion sensitivity and motor properties of output neurons of the superior colliculus, it is likely that, in cats, the colliculus makes a major contribution to the integration of eye movement-related and target motion-related signals.

A neurallike model (a Boltzmann machine) was built to extract the true 2-D motion of an entire pattern from ambiguous local motion information available at the pattern’s component contours (i.e., it solves the aperture problem). The model has an input and output layer representing visual cortical areas V1 and MT, respectively. Area MT, an area involved in motion analysis, receives a direct topographic projection from V1. V1 neurons act as local motion detectors in that they can only measure the component of motion perpendicular to the orientation of a moving contour. In contrast, ~20% of MT neurons possess pattern direction selectivity—selectivity for the motion of a pattern as a whole. In the model, each unit is selective for a specific speed and direction of motion. Connectivity is restricted so that each V1 unit projects only to the MT units with which it is consistent (i.e., that could describe its true motion). The input to the system is the set of velocity vectors perpendicular to the contours of a moving pattern. Input units are clamped to represent the input, and the system is allowed to relax to find the state which represents the minimum energy configuration given the input. The network relaxes quickly and the MT units describing the motion of the entire pattern are maximally activated.

Motion stimuli in one visual hemifield activate human primary visual areas of the contralateral side, but suppress activity of the corresponding ipsilateral regions. While hemifield motion is rare in everyday life, motion in both hemifields occurs regularly whenever we move. Consequently, during motion primary visual regions should simultaneously receive excitatory and inhibitory inputs. A comparison of primary and higher visual cortex activations induced by bilateral and unilateral motion stimuli is missing up to now. Many motion studies focused on the MT+ complex in the parieto-occipito-temporal cortex. In single human subjects MT+ has been subdivided in area MT, which was activated by motion stimuli in the contralateral visual field, and area MST, which responded to motion in both the contra- and ipsilateral field. In this study we investigated the cortical activation when excitatory and inhibitory inputs interfere with each other in primary visual regions and we present for the first time group results of the MT+ subregions, allowing for comparisons with the group results of other motion processing studies. Using functional magnetic resonance imaging (fMRI), we investigated whole brain activations in a large group of healthy humans by applying optic flow stimuli in and near the visual field centre and performed a second level analysis. Primary visual areas were activated exclusively by motion in the contralateral field but to our surprise not by central flow fields. Inhibitory inputs to primary visual regions appear to cancel simultaneously occurring excitatory inputs during central flow field stimulation. Within MT+ we identified two subregions. Putative area MST (pMST) was activated by ipsi- and contralateral stimulation and located in the anterior part of MT+. The second subregion was located in the more posterior part of MT+ (putative area MT, pMT).

As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories compensating for delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response as recorded in a neural populations to an approaching stimulus. We validate this using an abstract probabilistic framework and a spiking neural network (SNN) model. Inspired by a mechanism proposed by Nijhawan [1], we first use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to a delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used this pattern of anisotropic, recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [2], we have simulated different trajectory lengths and have explored how anticipatory responses may be dependent on the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN model can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the connectivity pattern which leads to the motion extrapolation mechanism.

Both adaptation and perceptual learning can change how we perceive the visual environment, reflecting the plasticity of the visual system. Our previous work has investigated the interaction between the two aspects of visual plasticity. One of the main findings is that multiple days of repeated motion adaptation attenuates motion aftereffect, which is explained by habituation of motion adaptation. Interestingly, there was almost no transfer of the effect to the untrained adapter, which differed from the trained adapter in the features including retinotopic location, spatiotopic location, and motion direction. Given that the reference frame of motion aftereffect is proposed to be retinotopic, it remains unclear whether the effect we refer to as habituation effect of motion adaptation is more like a special type of motion adaptation or not. Therefore, in three experiments, we examined the role of retinotopic location, spatiotopic location, and motion direction on the transfer of habituation, respectively. In each experiment, only one of the features was kept the same for the trained and untrained conditions. We found that the habituation effect transferred across both the retinotopic and spatiotopic locations as long as the adapting direction remained the same. The findings indicate that the effect we refer to as habituation of motion adaptation is anchored neither in eye-centered (retinotopic) nor world-centered (spatiotopic) coordinates. Rather, it is specific to the direction of the adapter. Therefore, the habituation effect of motion adaptation cannot be ascribed to a variant of motion adaptation.

Many neurons in visual cortical area MT signal the direction of motion of complex visual patterns, such as plaids composed of two superimposed drifting gratings. To compute the direction of pattern motion, MT neurons combine component motion signals over time and space. To determine the spatial and temporal limits of signal integration, we measured the responses of single MT neurons to a novel set of "pseudoplaid" stimuli in which the component gratings were alternated in time or space. As the temporal or spatial separation of the component gratings increased, neuronal selectivity for the direction of pattern motion decreased. Using descriptive models of signal integration, we inferred the temporal and spatial structure of the mechanisms that compute pattern direction selectivity. The median time constant for integration was roughly 10 ms, a timescale characteristic of integration by single cortical pyramidal neurons. The median spatial integration field was roughly one-third of the MT receptive field diameter, suggesting that the spatial limits are set by stages of processing in earlier areas of visual cortex where receptive fields are smaller than in MT. Interestingly, pattern direction-selective neurons had shorter temporal integration times than component direction-selective neurons but similar spatial integration windows. We conclude that pattern motion can only be signaled by MT neurons when the component motion signals co-occur within relatively narrow spatial and temporal limits. We interpret these results in the framework of recent hierarchical models of MT.

Pursuit eye movements correlate with perceived motion in both velocity and direction, even without retinal motion. Cortical cells in the monkey medial temporal region generate signals for initiating pursuit eye movements and respond to retinal motion for perception. However, recent studies suggest multiple motion processes, fast and slow, even for low-level motion. Here we investigated whether the relationship with pursuit eye movements is different for fast and slow motion processes, using a motion aftereffect technique with superimposed low- and high-spatial-frequency gratings. A previous study showed that the low- and high-spatial-frequency gratings adapt the fast and slow motion processes, respectively, and that a static test probes the slow motion process and a flicker test probes the fast motion process (Shioiri & Matsumiya, 2009). In the present study, an adaptation stimulus was composed of two gratings with different spatial frequencies and orientations but the same temporal frequency, moving in the orthogonal direction of ±45° from the vertical. We measured the directions of perceived motion and pursuit eye movements to a test stimulus presented after motion adaptation with changing relative contrasts of the two adapting gratings. Pursuit eye movements were observed in the same direction as that of the motion aftereffects, independent of the relative contrasts of the two adapting gratings, for both the static and flicker tests. These results suggest that pursuit eye movements and perception share motion signals in both slow and fast motion processes.

1. We investigated cells in the middle temporal visual area (MT) and the medial superior temporal area (MST) that discharged during smooth pursuit of a dim target in an otherwise dark room. For each of these pursuit cells we determined whether the response during pursuit originated from visual stimulation of the retina by the pursuit target or from an extraretinal input related to the pursuit movement itself. We distinguished between these alternatives by removing the visual motion stimulus during pursuit either by blinking off the visual target briefly or by stabilizing the target on the retina. 2. In the foveal representation of MT (MTf), we found that pursuit cells usually decreased their rate of discharge during a blink or during stabilization of the visual target. The pursuit response of these cells depends on visual stimulation of the retina by the pursuit target. 3. In a dorsal-medial region of MST (MSTd), cells continued to respond during pursuit despite a blink or stabilization of the pursuit target. The pursuit response of these cells is dependent on an extraretinal input. 4. In a lateral-anterior region of MST (MST1), we found both types of pursuit cells; some, like those in MTf, were dependent on visual inputs whereas others, like those in MSTd, received an extraretinal input. 5. We observed a relationship between pursuit responses and passive visual responses. MST cells whose pursuit responses were attributable to extraretinal inputs tended to respond preferentially to large-field random-dot patterns. Some cells that preferred small spots also had an extraretinal input. 6. For 92% of the pursuit cells we studied, the pursuit response began after onset of the pursuit eye movement. A visual response preceding onset of the eye movement could be observed in many of these cells if the initial motion of the target occurred within the visual receptive field of the cell and in its preferred direction. In contrast to the pursuit response, however, this visual response was not dependent on execution of the pursuit movement. 7. For the remaining 8% of the pursuit cells, the pursuit discharge began before initiation of the pursuit eye movement. This occurred even though the initial motion of the target was outside the receptive field as mapped during fixation trials. Our data suggest, however, that such responses may be attributable to an expansion of the receptive field that accompanies enhanced visual responses.(ABSTRACT TRUNCATED AT 400 WORDS)