Virginia R. de Sa

On the nature and scope of featural representations of word meaning.

Behavioral experiments and a connectionist model were used to explore the use of featural representations in the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimers disease (AD) are discussed.

Explaining brightness illusions using spatial filtering and local response normalization

We introduce two new low-level computational models of brightness perception that account for a wide range of brightness illusions, including many variations on Whites Effect [Perception, 8, 1979, 413]. Our models extend Blakeslee and McCourts ODOG model [Vision Research, 39, 1999, 4361], which combines multiscale oriented difference-of-Gaussian filters and response normalization. We extend the response normalization to be more neurally plausible by constraining normalization to nearby receptive fields (models 1 and 2) and spatial frequencies (model 2), and show that both of these changes increase the effectiveness of the models at predicting brightness illusions.

In the Footsteps of Biological Motion and Multisensory Perception Judgments of Audiovisual Temporal Relations Are Enhanced for Upright Walkers

Observers judged whether a periodically moving visual display (point-light walker) had the same temporal frequency as a series of auditory beeps that in some cases coincided with the apparent footsteps of the walker. Performance in this multisensory judgment was consistently better for upright point-light walkers than for inverted point-light walkers or scrambled control stimuli, even though the temporal information was the same in the three types of stimuli. The advantage with upright walkers disappeared when the visual “footsteps” were not phase-locked with the auditory events (and instead offset by 50% of the gait cycle). This finding indicates there was some specificity to the naturally experienced multisensory relation, and that temporal perception was not simply better for upright walkers per se. These experiments indicate that the gestalt of visual stimuli can substantially affect multisensory judgments, even in the context of a temporal task (for which audition is often considered dominant). This effect appears to be constrained by the ecological validity of the particular pairings.

Multi-view kernel construction

In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. These multiple views of the data are often not directly comparable to one another, and thus a principled method for their integration is warranted. In this paper we develop a new algorithm to leverage information from multiple views for unsupervised clustering by constructing a custom kernel. We generate a multipartite graph (with the number of parts given by the number of views) that induces a kernel we then use for spectral clustering. Our algorithm can be seen as a generalization of co-clustering and spectral clustering and a relative of Kernel Canonical Correlation Analysis. We demonstrate the algorithm on four data sets: an illustrative artificial data set, synthetic fMRI data, voxels from an fMRI study, and a collection of web pages. Finally, we compare its performance to common alternatives.

Homeostatic synaptic scaling in self-organizing maps

Various forms of the self-organizing map (SOM) have been proposed as models of cortical development [Choe Y., Miikkulainen R., (2004). Contour integration and segmentation with self-organized lateral connections. Biological Cybernetics, 90, 75-88; Kohonen T., (2001). Self-organizing maps (3rd ed.). Springer; Sirosh J., Miikkulainen R., (1997). Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation, 9(3), 577-594]. Typically, these models use weight normalization to contain the weight growth associated with Hebbian learning. A more plausible mechanism for controlling the Hebbian process has recently emerged. Turrigiano and Nelson [Turrigiano G.G., Nelson S.B., (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 5, 97-107] have shown that neurons in the cortex actively maintain an average firing rate by scaling their incoming weights. In this work, it is shown that this type of homeostatic synaptic scaling can replace the common, but unsupported, standard weight normalization. Organized maps still form and the output neurons are able to maintain an unsaturated firing rate, even in the face of large-scale cell proliferation or die-off. In addition, it is shown that in some cases synaptic scaling leads to networks that more accurately reflect the probability distribution of the input data.

Brief presentations reveal the temporal dynamics of brightness induction and White's illusion

We measured the timecourse of brightness processing by briefly presenting brightness illusions and then masking them. Brightness induction (brightness contrast) was visible when presented for only 58 ms, was stronger at short presentation times, and its visibility did not depend on spatial frequency. We also found that Whites illusion was visible at 82 ms. Together, these results suggest that (1) brightness perception depends on the surrounding context, even at very short presentation times, (2) the initial brightness percept is generated very quickly, but additional exposure can modulate it, and (3) the temporal dynamics are not dependent on a slow filling-in process.

Single-trial classification of gait and point movement preparation from human EEG

Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).

Perceptual Learning From Cross-Modal Feedback

This chapter discusses that many problems are better classified when the labels of the data points are available during training and classifiers can be further improved if the network attempts to minimize the number of misclassifications. It explains how to approach the performance of the minimizing misclassifications classifier without requiring labeled input patterns. The labels are replaced by an assumption that the world present patterns to different modalities in such a way that patterns from Modality 1s Class A occur with patterns from Modality 2s class A distribution. By iterating the algorithm, the labeling algorithm is able to take advantage of the better codebook vector placement and produce better results allowing the M-D algorithm to perform even better. With this two iteration algorithm the performance on the benchmark dataset is within 5% of the analogous supervised algorithm. The results were better when the confusable classes were different for the two modalities as they could provide better labeling where it was needed most. In this case the performance was within 2% of the supervised algorithm. The experiments with the pseudo-modalities indicate that performance with the M-D algorithm is better when the modalities are divided with correlated dimensions kept together and those that are independent separated.

Task-phase-specific dynamics of basal forebrain neuronal ensembles

Cortically projecting basal forebrain neurons play a critical role in learning and attention, and their degeneration accompanies age-related impairments in cognition. Despite the impressive anatomical and cell-type complexity of this system, currently available data suggest that basal forebrain neurons lack complexity in their response fields, with activity primarily reflecting only macro-level brain states such as sleep and wake, onset of relevant stimuli and/or reward obtainment. The current study examined the spiking activity of basal forebrain neuron populations across multiple phases of a selective attention task, addressing, in particular, the issue of complexity in ensemble firing patterns across time. Clustering techniques applied to the full population revealed a large number of distinct categories of task-phase-specific activity patterns. Unique population firing-rate vectors defined each task phase and most categories of task-phase-specific firing had counterparts with opposing firing patterns. An analogous set of task-phase-specific firing patterns was also observed in a population of posterior parietal cortex neurons. Thus, consistent with the known anatomical complexity, basal forebrain population dynamics are capable of differentially modulating their cortical targets according to the unique sets of environmental stimuli, motor requirements, and cognitive processes associated with different task phases.

Effect of Visual Feedback on the Occipital-Parietal-Motor Network in Parkinson’s Disease with Freezing of Gait

Freezing of gait (FOG) is an elusive phenomenon that debilitates a large number of Parkinson’s disease (PD) patients regardless of stage of disease, medication status, or deep brain stimulation implantation. Sensory feedback cues, especially visual feedback cues, have been shown to alleviate FOG episodes or even prevent episodes from occurring. Here, we examine cortical information flow between occipital, parietal, and motor areas during the pre-movement stage of gait in a PD-with-FOG patient that had a strong positive behavioral response to visual cues, one PD-with-FOG patient without any behavioral response to visual cues, and age-matched healthy controls, before and after training with visual feedback. Results for this case study show differences in cortical information flow between the responding PD-with-FOG patient and the other two subject types, notably, an increased information flow in the beta range. Tentatively suggesting the formation of an alternative cortical sensory-motor pathway during training with visual feedback, these results are proposed as subject for further verification employing larger cohorts of patients.