Visual Crowding.

Experimentalists tend to classify models of visual perception as being either local or global, and involving either feedforward or feedback processing. We argue that these distinctions are not as helpful as they might appear, and we illustrate these issues by analyzing models of visual crowding as an example. Recent studies have argued that crowding cannot be explained by purely local processing, but that instead, global factors such as perceptual grouping are crucial. Theories of perceptual grouping, in turn, often invoke feedback connections as a way to account for their global properties. We examined three types of crowding models that are representative of global processing models, and two of which employ feedback processing: a model based on Fourier filtering, a feedback neural network, and a specific feedback neural architecture that explicitly models perceptual grouping. Simulations demonstrate that crucial empirical findings are not accounted for by any of the models. We conclude that empirical investigations that reject a local or feedforward architecture offer almost no constraints for model construction, as there are an uncountable number of global and feedback systems. We propose that the identification of a system as being local or global and feedforward or feedback is less important than the identification of a system's computational details. Only the latter information can provide constraints on model development and promote quantitative explanations of complex phenomena.

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.

Older adults with degenerative declines in sensory systems depend strongly on visual input for postural control. By connecting advanced neural imaging and a postural control model, this study investigated the visual effect on the brain functional network that regulates feedback and feedforward processes of the postural system in older adults under somatosensory perturbations. Thirty-six older adults conducted bilateral stance on a foam surface in the eyes-open (EO) and eyes-closed (EC) conditions while their center of pressure (COP) and scalp EEG were recorded. The stochastic COP trajectory was modeled with non-linear stabilogram diffusion analysis (SDA) to characterize shifts in postural control in a continuum of feedback and feedforward processes. The EEG network was analyzed with the phase-lag index (PLI) and minimum spanning tree (MST). The results indicated that visual input rebalanced feedforward and feedback processes for postural sway, resulting in a greater critical point of displacement (CD), short-term effective diffusion coefficients (Ds) and short-term exponent (Hs), but the smaller critical point of time (CT) and long-term exponent (Hl) for the EC state. The EC network demonstrated stronger frontoparietal-occipital connectivity but weaker fronto-tempo-motor connectivity of the theta (4–7 Hz), alpha (8–12 Hz), and beta (13–35 Hz) bands than did the EO network. MST analysis revealed generally greater leaf fraction and maximal betweenness centrality (BCmax) and kappa of the EC network, as compared with those of the EO network. In contrast, the EC network exhibited a smaller diameter and average eccentricity than those of the EO network. The modulation of long-term negative feedback gain of the aged postural system with visual occlusion was positively correlated with leaf fraction, BCmax, and kappa, but negatively correlated with the diameter and average eccentricity for all EEG sub-bands. In conclusion, the aged brain functional network in older adults is tuned to visual information for modulating long-term negative feedback of the postural system under somatosensory perturbations.

Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether low-frequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ- and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.

Decision letter: Causal neural mechanisms of context-based object recognition

It is well known that simple visual tasks, such as object detection or categorization, can be performed within a short period of time, suggesting the sufficiency of feed-forward visual processing. However, more complex visual tasks, such as fine-grained localization may require high-resolution information available at the early processing levels in the visual hierarchy. To access this information using a top-down approach, feedback processing would need to traverse several stages in the visual hierarchy and each step in this traversal takes processing time. In the present study, we compared the processing time required to complete object categorization and localization by varying presentation duration and complexity of natural scene stimuli. We hypothesized that performance would be asymptotic at shorter presentation durations when feed-forward processing suffices for visual tasks, whereas performance would gradually improve as images are presented longer if the tasks rely on feedback processing. In Experiment 1, where simple images were presented, both object categorization and localization performance sharply improved until 100 ms of presentation then it leveled off. These results are a replication of previously reported rapid categorization effects but they do not support the role of feedback processing in localization tasks, indicating that feed-forward processing enables coarse localization in relatively simple visual scenes. In Experiment 2, the same tasks were performed but more attention-demanding and ecologically valid images were used as stimuli. Unlike in Experiment 1, both object categorization performance and localization precision gradually improved as stimulus presentation duration became longer. This finding suggests that complex visual tasks that require visual scrutiny call for top-down feedback processing.

A cybernetic view on wind-up

The internal model was proposed by Kawato, in order to express physical motion by associativity of feedback and feedforward control processes. In this paper, we test the internal model in an experiment in which participants push a button on en equipment to match a number displayed on a computer screen. The experimental results can be interpreted as a change in the relative contributions of feedback and feedforward control processes in the internal model.

The high-frequency activity (HFA; 80-150 Hz) in human intracranial recordings shows a differential modulation to different degrees in contrast when stimuli are behaviorally relevant, indicating a feedforward process. However, the HFA is also significantly dominated by superficial layers and exhibits a peak before 200 ms, suggesting that it is more likely a feedback signal. Magnetoencephalographic (MEG) recordings are suited to reveal an HFA modulation similar to its modulation in intracranial recordings. This allows for noninvasive, direct comparison of HFA with the C1, an established measure for feedforward input to V1, to test whether HFA represents feedforward or rather feedback. In simultaneous recordings, we used the EEG-C1 event-related potential (ERP) component and MEG-HFA to define feedforward processing in visual cortices. C1 latency preceded the HFA peak modulation, which had a more sustained response. Furthermore, modulation parameters like onset, peak time, and peak amplitude were uncorrelated. Most importantly, the C1 but not HFA distinguished small task-irrelevant contrast differences in visual stimulation. These results highlight the differential roles for the C1 and HFA in visual processing with the C1 measuring feedforward discrimination ability and HFA indexing feedforward and feedback processing.NEW & NOTEWORTHY Whether the broadband high-frequency activity (HFA) represents exclusively feedforward or feedback processing remains unclear. In this study, we compared the response characteristics of the HFA-magnetoencephalographic (MEG) and the C1-EEG component to systematic contrast modulations of task-irrelevant visual stimulation. Our findings reveal that the more sustained HFA follows the C1 component and, unlike the C1, is not modulated by task-irrelevant contrast differences. This timing of the HFA modulation suggests that HFA encompasses both feedforward and feedback processing.

Feedforward control is a process adjusting behaviour in a continuative way. Feedforward takes place when an equilibrium state is disrupted and the system has to automatically retrieve the homeostatic stable state. It also occurs when a perturbation is previewed and must be eliminated in order to achieve a desired goal. According to the most general definition, a feedforward process operates by fixing the future representation of the desired state, the achieving of which stops the process. Then, feedforward works by means of the refinement determined by successive comparisons between the actual and target products. In its applications, a feedforward process is thought to be modulated by the subject's purpose and the environmental state. Over the years, the feedforward process has assumed different connotations in several contests of cognitive psychology. An overview of the research fields in psychology that significantly progressed with the introduction of a feedforward paradigm is provided by: (a) reviewing models in which the feedforward concept plays a fundamental role in the system control; (b) examining critical experiments related to the interaction of feedforward and feedback processes; (c) evidencing practical applications for some of the presented feedforward-based architectures.

Human brain is a layered structure, and performs not only a feedforward process from a lower layer to an upper layer but also a feedback process from an upper layer to a lower layer. The layer is a collection of neurons, and neural network is a mathematical model of the function of neurons. Although neural network imitates the human brain, everyone uses only feedforward process from the lower layer to the upper layer, and feedback process from the upper layer to the lower layer is not used. Therefore, in this paper, we propose Feedback U-Net using Convolutional LSTM which is the segmentation method using Convolutional LSTM and feedback process. The output of U-net gave feedback to the input, and the second round is performed. By using Convolutional LSTM, the features in the second round are extracted based on the features acquired in the first round. On both of the Drosophila cell image and Mouse cell image datasets, our method outperformed conventional U-Net which uses only feedforward process.

A detailed review of many temporal stability and spatial coherence experiments is used to develop a reasonable model for the temporal and spatial limitations on mid-frequency synthetic aperture sonar (SAS) in shallow water. A comparison of deep- versus shallow-water coherence experiments is made based on an extensive review of the literature. This comparison reveals that the spatial limits for arrays often dominate over temporal limits. The upper bounds on real or synthesized array lengths in shallow water that result from this analysis are presented. The feasibility of mid-frequency SAS imaging in shallow water is shown, and the predicted resolution in range, azimuth, and depth is compared to the theoretical values for a perfectly stable ocean and precise motion compensation. As a first step to validate the spatial and temporal limitation analysis, a mid-frequency surface sonar is modeled to create artificial data for SAS imaging in shallow water. The modeled environment includes a time varying ocean surface and sound-speed profiles, bottom roughness, penetration, and scattering. Useful SAS imaging is shown. [Work supported by NUWC.]

Chapter 26 Extensive cortical reorganization following sciatic nerve injury in adult rats versus restricted reorganization after neonatal injury: implications for spatial and temporal limits on somatosensory plasticity

High-Level Motion Processing, edited by Takeo Watanabe

Towards a unified theory of neocortex: laminar cortical circuits for vision and cognition