Low-level mediation of directionally specific motion after-effects: motion perception is not necessary

Previous psychophysical experiments with normal human observers have shown that adaptation to a moving dot stream causes directionally specific repulsion in the perceived angle of a subsequently viewed, moving probe. In this paper, we used a 2AFC task with roving pedestals to determine the conditions necessary and sufficient for producing directionally specific repulsion with compound adaptors, each ofwhich contains two oppositely moving, differently colored, component streams. Experiment 1 provides a demonstration of repulsion between single-component adaptors and probes moving at approximately 90° or 270°. In Experiment 2 oppositely moving dots in the adaptor were paired to preclude the appearance of motion. Nonetheless, repulsion remained strong when the angle betweeneach probe stream and one component was approximately 30°. In Experiment 3 adapting dot-pairs were kept stationary during their limited lifetimes. Their orientation content alone proved insufficient for producing repulsion. In Experiments 4-6 the angle between probe and both adapting components was approximately 90°or 270°. Directional repulsion was found when observers were asked to visually track one of the adapting components (Experiment 6), but not when observers were asked to attentionally track it (Experiment 5), nor while passively viewing the adaptor (Experiment 4). Our results are consistent with a low-level mechanism for motion adaptation. It is not selective for stimulus color and it is not susceptible to attentional modulation.The most likely cortical locus of adaptation is area V1

Spatial contrast sensitivity in adolescents with autism spectrum disorders

Adolescents with autism spectrum disorders (ASD) and typically developing (TD) controls underwent a rigorous psychophysical assessment that measured contrast sensitivity to seven spatial frequencies (0.5-20 cycles/degree). A contrast sensitivity function (CSF) was then fitted for each participant, from which four measures were obtained: visual acuity, peak spatial frequency, peak contrast sensitivity, and contrast sensitivity at a low spatial frequency. There were no group differences on any of the four CSF measures, indicating no differential spatial frequency processing in ASD. Although it has been suggested that detail-oriented visual perception in individuals with ASD may be a result of differential sensitivities to low versus high spatial frequencies, the current study finds no evidence to support this hypothesis

Adaptive Filtering Enhances Information Transmission in Visual Cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio

Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity

Background: The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings: Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance: We propose a new neural model for MT pattern computation and motion disambiguation that i

Using the properties of Primate Motion Sensitive Neurons to extract camera motion and depth from brief 2-D Monocular Image Sequences

Humans and most animals can run/fly and navigate efficiently through cluttered environments while avoiding obstacles in their way. Replicating this advanced skill in autonomous robotic vehicles currently requires a vast array of sensors coupled with computers that are bulky, heavy and power hungry. The human eye and brain have had millions of years to develop an efficient solution to the problem of visual navigation and we believe that it is the best system to reverse engineer. Our brain and visual system appear to use a very different solution to the visual odometry problem compared to most computer vision approaches. We show how a neural-based architecture is able to extract self-motion information and depth from monocular 2-D video sequences and highlight how this approach differs from standard CV techniques. We previously demonstrated how our system works during pure translation of a camera. Here, we extend this approach to the case of combined translation and rotation

A Multi-Stage Model for Fundamental Functional Properties in Primary Visual Cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. We here define a model that accounts for many of the empirical observations concerning direction selectivity. The model describes a single column of cat primary visual cortex and comprises a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small (∼4 ms) interval, and it is this difference that confers direction selectivity on model neurons. We show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex

Object Segmentation from Motion Discontinuities and Temporal Occlusions–A Biologically Inspired Model

BACKGROUND: Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it. METHODOLOGY/PRINCIPAL FINDINGS: From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection. CONCLUSIONS/SIGNIFICANCE: A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion

A Postnatal Critical Period for Orientation Plasticity in the Cat Visual Cortex

Orientation selectivity of primary visual cortical neurons is an important requisite for shape perception. Although numerous studies have been previously devoted to a question of how orientation selectivity is established and elaborated in early life, how the susceptibility of orientation plasticity to visual experience changes in time remains unclear. In the present study, we showed a postnatal sensitive period profile for the modifiability of orientation selectivity in the visual cortex of kittens reared with head-mounted goggles for stable single-orientation exposure. When goggle rearing (GR) started at P16-P30, 2 weeks of GR induced a marked over-representation of the exposed orientation, and 2 more weeks of GR consolidated the altered orientation maps. GR that started later than P50, in turn, induced the under-representation of the exposed orientation. Orientation plasticity in the most sensitive period was markedly suppressed by cortical infusion of NMDAR antagonist. The present study reveals that the plasticity and consolidation of orientation selectivity in an early life are dynamically regulated in an experience-dependent manner

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli