Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Attention induces dynamic changes in coherence between monkey area V1 and V4

Neurons receive a vast number of synaptic inputs at the same time. To successfully process a relevant piece of information, a neuron needs to select relevant, while suppressing irrelevant signals. In order to dynamically route a meaningful set of signals through processing pathways, neurons need to have a fast and flexible mechanism to change their effective connectivity. Here, we hypothesize that attention changes effective connectivity by modulating the synchrony between neuronal populations.
We investigated whether attention dynamically changes effective connectivity between area V4 neurons and their V1 input. Single unit activity, multi unit activity and local field potentials (LFP) were recorded simultaneously from macaque monkeys’ areas V1 and V4. While maintaining their gaze on a fixation spot, the monkeys had to attend to one of two continuously morphing shapes and respond to reoccurrence of the initial sample shape in the attended stream. Size and position of the shapes were adjusted such that both fitted into a single V4 receptive field (RF) while covering two separate, non-overlapping V1 RFs.
We found strong phase-coherence of gamma-band LFPs between the V1 populations responding to the attended stimulus and the V4 population. At the same time, V1 populations responding to the unattended stimulus showed only weak LFP phase-coherence with the V4 population. Furthermore, spike-triggered averages (STAs) of at least 40% of the recording pairs revealed that spikes recorded from area V4 were precisely timed with respect to the phase of the gamma oscillation in the upstream area V1. More importantly, almost all these cases showed an increased effect with attention. These results support the hypothesis that attention induces dynamic changes in the effective connectivity between two anatomically hard-wired neuronal populations by selectively modulating synchrony

A Multi-Channel, Flex-Rigid ECoG Microelectrode Array for Visual Cortical Interfacing

High-density electrocortical (ECoG) microelectrode arrays are promising signal-acquisition platforms for brain-computer interfaces envisioned, e.g., as high-performance communication solutions for paralyzed persons. We propose a multi-channel microelectrode array capable of recording ECoG field potentials with high spatial resolution. The proposed array is of a 150 mm2 total recording area; it has 124 circular electrodes (100, 300 and 500 µm in diameter) situated on the edges of concentric hexagons (min. 0.8 mm interdistance) and a skull-facing reference electrode (2.5 mm2 surface area). The array is processed as a free-standing device to enable monolithic integration of a rigid interposer, designed for soldering of fine-pitch SMD-connectors on a minimal assembly area. Electrochemical characterization revealed distinct impedance spectral bands for the 100, 300 and 500 µm-type electrodes, and for the array’s own reference. Epidural recordings from the primary visual cortex (V1) of an awake Rhesus macaque showed natural electrophysiological signals and clear responses to standard visual stimulation. The ECoG electrodes of larger surface area recorded signals with greater spectral power in the gamma band, while the skull-facing reference electrode provided higher average gamma power spectral density (γPSD) than the common average referencing technique

Predictions for different association field symmetries.

<p>Average likelihoods to be the starting element of a contour of length , shown for all edge elements belonging to –element contours (left columns), and for background edges (). Vertical axis denotes iterations in eqn. (1), and the color scale is normalized to minimum/maximum likelihoods in each graph. (A) shows the corresponding dynamics for the optimal model which uses uni–directional AFs. For obtaining (B), we symmetrized the AF of the optimal model such that it became invariant to the directions of arbitrary edge pairs. For this bi–directional AF, simulations on the same stimuli as used for (A) were performed. If neuronal populations would encode these likelihoods , uni–directional interactions would cause highest activities at the end of a contour, while bi–directional interactions predict highest activities at center elements of a contour.</p

Comparing the model to data from independent experiments.

<p>(A) Comparison of association field parameters to electrophysiological data. Red, modulation index of firing rate of a cortical neuron to a preferred stimulus in dependence on the angular position of a second, flanking stimulus of same orientation (cross-section extracted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002520#pcbi-1002520-g002" target="_blank">Fig. 2C</a> in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002520#pcbi.1002520-Kapadia1" target="_blank">[23]</a>). Blue, cross-section through optimal association field, scaled to the maxima of the red curve. (B) Comparison of attenuation of edge likelihood with eccentricity with psychophysical data. Red, sensitivity modulation required to explain psychophysical edge detection thresholds in dependence on eccentricity , (from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002520#pcbi.1002520-Foley1" target="_blank">[24]</a>). Blue, optimal likelihood modulation . Parameters and equations see main text.</p

Framework for combining theory, modeling and psychophysics to study contour integration.

<p>Upper row, contour creation: A contour is created either on the left or right hemifield of a computer screen by a Markov random process using a suitably defined association field (AF, in brackets) for specifying the transition probabilities. Adding randomly oriented, similarly spaced background elements effectively hides the contour and completes a stimulus. Lower left column, contour integration: The ideal algorithm for contour integration uses knowledge about the generating process (i.e. the same AF as used in generating the contours, in brackets), to perform inference on a stimulus. For each edge, it computes the probability of being the first (or last) element of a contour created by the generating Markov process. The likely position of a contour is finally determined by maximum-likelihood estimation on the sum of these probabilities for each hemifield. Lower middle and right column, comparison to humans and probabilistic models: In our paradigm, the ideal contour observer serves as a benchmark for human contour detection, which is probed using the same stimuli under time constraints. At the same time, the inference algorithm of the ideal observer suggests a class of probabilistic contour integration models in which we search for the optimal model which best explains human behavior and performance. Note that ‘optimal’ does not mean that the contour integration model strives for an optimal contour detection performance: it should also make the same errors as human observers, as in this illustrative example, where a shorter ‘chance’ contour in the background is judged more salient by the human subject.</p

Temporal aspects of contour detection.

<p>Psychophysical contour detection performances in dependence on SOA in the upper row are compared to performance of the optimal model, which best matches human behaviour, in the lower row. Iterations performed in the optimal model were rescaled to time by assuming a constant propagation speed mediated by the AF interactions (corresponding to 13.9 DVA per 200 ms, which was the average length of all contours in the stimulus ensembles). (A) and (C) show performances for different AF alignment jitters, for contours of length (color legend as inset to (C)). (B) and (D) show performances for different inter–element distances which are inversely proportional to the total number of edges in a contour, for an AF jitter of (color legend as inset to (D)). Detection performance for the optimal model was averaged over 5000 samples from each contour ensemble, instead of using only 48 samples as in the experiment, to yield a better statistics and smoother curves.</p