Improving SNR and reducing training time of classifiers in large datasets via kernel averaging

Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data

Perceived and mentally rotated contents are differentially represented in cortical depth of V1

Primary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region

A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement

In real world scenarios, objects are often partially occluded. This requires a robustness for object recognition against these perturbations. Convolutional networks have shown good performances in classification tasks. The learned convolutional filters seem similar to receptive fields of simple cells found in the primary visual cortex. Alternatively, spiking neural networks are more biological plausible. We developed a two layer spiking network, trained on natural scenes with a biologically plausible learning rule. It is compared to two deep convolutional neural networks using a classification task of stepwise pixel erasement on MNIST. In comparison to these networks the spiking approach achieves good accuracy and robustness.Comment: Published in ICANN 2018: Artificial Neural Networks and Machine Learning - ICANN 2018 https://link.springer.com/chapter/10.1007/978-3-030-01418-6_25 The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-01418-6_2

Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

Recurrent connections in the visual cortex are thought to aid object recognition when part of the stimulus is occluded. Here we investigate if and how recurrent connections in artificial neural networks similarly aid object recognition. We systematically test and compare architectures comprised of bottom-up (B), lateral (L) and top-down (T) connections. Performance is evaluated on a novel stereoscopic occluded object recognition dataset. The task consists of recognizing one target digit occluded by multiple occluder digits in a pseudo-3D environment. We find that recurrent models perform significantly better than their feedforward counterparts, which were matched in parametric complexity. Furthermore, we analyze how the network's representation of the stimuli evolves over time due to recurrent connections. We show that the recurrent connections tend to move the network's representation of an occluded digit towards its un-occluded version. Our results suggest that both the brain and artificial neural networks can exploit recurrent connectivity to aid occluded object recognition.Comment: 13 pages, 5 figures, accepted at the 28th International Conference on Artificial Neural Networks, published in Springer Lecture Notes in Computer Science vol 1172

Prefrontal Cortex Lesions Impair Object-Spatial Integration

How and where object and spatial information are perceptually integrated in the brain is a central question in visual cognition. Single-unit physiology, scalp EEG, and fMRI research suggests that the prefrontal cortex (PFC) is a critical locus for object-spatial integration. To test the causal participation of the PFC in an object-spatial integration network, we studied ten patients with unilateral PFC damage performing a lateralized object-spatial integration task. Consistent with single-unit and neuroimaging studies, we found that PFC lesions result in a significant behavioral impairment in object-spatial integration. Furthermore, by manipulating inter-hemispheric transfer of object-spatial information, we found that masking of visual transfer impairs performance in the contralesional visual field in the PFC patients. Our results provide the first evidence that the PFC plays a key, causal role in an object-spatial integration network. Patient performance is also discussed within the context of compensation by the non-lesioned PFC

Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition

Soluble CD44 Interacts with Intermediate Filament Protein Vimentin on Endothelial Cell Surface

CD44 is a cell surface glycoprotein that functions as hyaluronan receptor. Mouse and human serum contain substantial amounts of soluble CD44, generated either by shedding or alternative splicing. During inflammation and in cancer patients serum levels of soluble CD44 are significantly increased. Experimentally, soluble CD44 overexpression blocks cancer cell adhesion to HA. We have previously found that recombinant CD44 hyaluronan binding domain (CD44HABD) and its non-HA-binding mutant inhibited tumor xenograft growth, angiogenesis, and endothelial cell proliferation. These data suggested an additional target other than HA for CD44HABD. By using non-HA-binding CD44HABD Arg41Ala, Arg78Ser, and Tyr79Ser-triple mutant (CD443MUT) we have identified intermediate filament protein vimentin as a novel interaction partner of CD44. We found that vimentin is expressed on the cell surface of human umbilical vein endothelial cells (HUVEC). Endogenous CD44 and vimentin coprecipitate from HUVECs, and when overexpressed in vimentin-negative MCF-7 cells. By using deletion mutants, we found that CD44HABD and CD443MUT bind vimentin N-terminal head domain. CD443MUT binds vimentin in solution with a Kd in range of 12–37 nM, and immobilised vimentin with Kd of 74 nM. CD443MUT binds to HUVEC and recombinant vimentin displaces CD443MUT from its binding sites. CD44HABD and CD443MUT were internalized by wild-type endothelial cells, but not by lung endothelial cells isolated from vimentin knock-out mice. Together, these data suggest that vimentin provides a specific binding site for soluble CD44 on endothelial cells

Visual imagery and false memory for pictures:a functional magnetic resonance imaging study in healthy participants

BACKGROUND: Visual mental imagery might be critical in the ability to discriminate imagined from perceived pictures. Our aim was to investigate the neural bases of this specific type of reality-monitoring process in individuals with high visual imagery abilities. METHODS: A reality-monitoring task was administered to twenty-six healthy participants using functional magnetic resonance imaging. During the encoding phase, 45 words designating common items, and 45 pictures of other common items, were presented in random order. During the recall phase, participants were required to remember whether a picture of the item had been presented, or only a word. Two subgroups of participants with a propensity for high vs. low visual imagery were contrasted. RESULTS: Activation of the amygdala, left inferior occipital gyrus, insula, and precuneus were observed when high visual imagers encoded words later remembered as pictures. At the recall phase, these same participants activated the middle frontal gyrus and inferior and superior parietal lobes when erroneously remembering pictures. CONCLUSIONS: The formation of visual mental images might activate visual brain areas as well as structures involved in emotional processing. High visual imagers demonstrate increased activation of a fronto-parietal source-monitoring network that enables distinction between imagined and perceived pictures