Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Explicit processing of verbal and spatial features during letter-location binding modulates oscillatory activity of a fronto-parietal network.

The present study investigated the binding of verbal and spatial features in immediate memory. In a recent study, we demonstrated incidental and asymmetrical letter-location binding effects when participants attended to letter features (but not when they attended to location features) that were associated with greater oscillatory activity over prefrontal and posterior regions during the retention period. We were interested to investigate whether the patterns of brain activity associated with the incidental binding of letters and locations observed when only the verbal feature is attended differ from those reflecting the binding resulting from the controlled/explicit processing of both verbal and spatial features. To achieve this, neural activity was recorded using magnetoencephalography (MEG) while participants performed two working memory tasks. Both tasks were identical in terms of their perceptual characteristics and only differed with respect to the task instructions. One of the tasks required participants to process both letters and locations. In the other, participants were instructed to memorize only the letters, regardless of their location. Time–frequency representation of MEG data based on the wavelet transform of the signals was calculated on a single trial basis during the maintenance period of both tasks. Critically, despite equivalent behavioural binding effects in both tasks, single and dual feature encoding relied on different neuroanatomical and neural oscillatory correlates. We propose that enhanced activation of an anterior–posterior dorsal network observed in the task requiring the processing of both features reflects the necessity for allocating greater resources to intentionally process verbal and spatial features in this task

The neural circuits of number and letter copying: an fNIRS study

In our daily lives, we are constantly exposed to numbers and letters. However, it is still under debate how letters and numbers are processed in the brain, while information on this topic would allow for a more comprehensive understanding of, for example, known influences of language on numerical cognition or neural circuits shared by numerical cognition and language processing. Some findings provide evidence for a double dissociation between numbers and letters, with numbers being represented in the right and letters in the left hemisphere, while the opposing view suggests a shared neural network. Since processing may depend on the task, we address the reported inconsistencies in a very basic symbol copying task using functional near-infrared spectroscopy (fNIRS). fNIRS data revealed that both number and letter copying rely on the bilateral middle and left inferior frontal gyri. Only numbers elicited additional activation in the bilateral parietal cortex and in the left superior temporal gyrus. However, no cortical activation difference was observed between copying numbers and letters, and there was Bayesian evidence for common activation in the middle frontal gyri and superior parietal lobules. Therefore, we conclude that basic number and letter processing are based on a largely shared cortical network, at least in a simple task such as copying symbols. This suggests that copying can be used as a control condition for more complex tasks in neuroimaging studies without subtracting stimuli-specific activation

The Inferior Temporal Numeral Area distinguishes numerals from other character categories during passive viewing: A representational similarity analysis

A region in the posterior inferior temporal gyrus (pITG) is thought to be specialized for processing Arabic numerals, but fMRI studies that compared passive viewing of numerals to other character types (e.g., letters and novel characters) have not found evidence of numeral preference in the pITG. However, recent studies showed that the engagement of the pITG is modulated by attention and task contexts, suggesting that passive viewing paradigms may be ill-suited for examining numeral specialization in the pITG. It is possible, however, that even if the strengths of responses to different category types are similar, the distributed response patterns (i.e., neural representations) in a candidate numeral-preferring pITG region ( pITG-numerals ) may reveal categorical distinctions, even during passive viewing. Using representational similarity analyses with three datasets that share the same task paradigm and stimulus sets (total N = 88), we tested whether the neural representations of digits, letters, and novel characters in pITG-numerals were organized according to visual form and/or conceptual categories (e.g., familiar versus novel, numbers versus others). Small-scale frequentist and Bayesian meta-analyses of our dataset-specific findings revealed that the organization of neural representations in pITG-numerals is unlikely to be described by differences in abstract shape, but can be described by a categorical digits versus letters distinction, or even a digits versus others distinction (suggesting greater numeral sensitivity). Evidence of greater numeral sensitivity during passive viewing suggest that pITG-numerals is likely part of a neural pathway that has been developed for automatic processing of objects with potential numerical relevance. Given that numerals and letters do not differ categorically in terms of shape, categorical distinction in pITG-numerals during passive viewing must reflect ontogenetic differentiation of symbol set representations based on repeated usage of numbers and letters in differing task contexts

Symbolizing Number: fMRI investigations of the semantic, auditory, and visual correlates of Hindu-Arabic numerals

Humans are born with a sensitivity to numerical magnitude. In literate cultures, these numerical intuitions are associated with a symbolic notation (e.g..Hindu-Arabic numerals). While a growing body of neuroscientific research has been conducted to elucidate commonalities between symbolic (e.g. Hinud-Arabic numerals) and non-symbolic (e.g. arrays of objects) representations, relatively little is known about the neural correlates specific to the symbolic processing of numerical magnitude. To address this, I conducted the three fMRI experiments contained within this thesis to characterize the neuroanatomical correlates of the auditory, visual, audiovisual, and semantic processing of numerical symbols. In Experiment 1, the neural correlates of symbolic and non-symbolic number were contrasted to reveal that the left angular and superior temporal gyri responded specifically to numerals, while the right posterior superior parietal lobe only responded to non-symbolic arrays. Moreover, the right intraparietal sulcus (IPS) was activated by both formats. The results reflect divergent encoding pathways that converge upon a common representation across formats. In Experiment 2, the neural response to Hindu-Arabic numerals and Chinese numerical ideographs was recorded in individuals who could read both notations and a control group who could read only the numerals. A between-groups contrast revealed semantic processing of ideographs in the right IPS, while asemantic visual processing was found in the left fusiform gyrus. In contrast to the ideographs, the semantic processing of numerals was associated with left IPS activity. The role of these brain regions in the semantic and asemantic representation of numerals is discussed. In Experiment 3, the neural response of the visual, auditory, and audiovisual processing of numerals and letters was measured. The regions associated with visual and auditory responses to letters and numerals were highly similar. In contrast, the audiovisual response to numerals recruited a region of the right supramarginal gyrus, while the audiovisual letters activated left visual regions. In addition, an effect of congruency in the audiovisual pairs was comparable across numeral-number name pairs and letter-letter name pairs, but absent in letter-speech sound pairs. Taken together, these three experiments provide new insights into how the brain processes numerical symbols at different levels of description

The Dissociation between Early and Late Selection in Older Adults

Older adults exhibit a reduced ability to ignore task-irrelevant stimuli; however, it remains to be determined where along the information processing stream the most salient age-associated changes occur. In the current study, ERPs provided an opportunity to determine whether age-related differences in processing task-irrelevant stimuli were uniform across information processing stages or disproportionately affected either early or late selection. ERPs were measured in young and old adults during a color-selective attention task in which participants responded to target letters in a specified color (attend condition) while ignoring letters in a different color (ignore condition). Old participants were matched to two groups of young participants on the basis of neuropsychological test performance: one using age-appropriate norms and the other using test scores not adjusted for age. There were no age-associated differences in the magnitude of early selection (attend–ignore), as indexed by the size of the anterior selection positivity and posterior selection negativity. During late selection, as indexed by P3b amplitude, both groups of young participants generated neural responses to target letters under the attend versus ignore conditions that were highly differentiated. In striking contrast, old participants generated a P3b to target letters with no reliable differences between conditions. Individuals who were slow to initiate early selection appeared to be less successful at executing late selection. Despite relative preservation of the operations of early selection, processing delays may lead older participants to allocate excessive resources to task-irrelevant stimuli during late selection