Extreme Image Transformations Affect Humans and Machines Differently

Some recent artificial neural networks (ANNs) have claimed to model important aspects of primate neural and human performance data. Their demonstrated performance in object recognition is still dependent on exploiting low-level features for solving visual tasks in a way that humans do not. Out-of-distribution or adversarial input is challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by certain extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and networks on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on other transforms that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking for our transforms through human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.Comment: Under review. 29 pages, 8 figures, 12 tables. arXiv admin note: text overlap with arXiv:2205.0516

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.publishedVersionPeer reviewe

Electrophysiological Maturation of Cerebral Organoids Correlates with Dynamic Morphological and Cellular Development

Cerebral organoids (COs) are rapidly accelerating the rate of translational neuroscience based on their potential to model complex features of the developing human brain. Several studies have examined the electrophysiological and neural network features of COs; however, no study has comprehensively investigated the developmental trajectory of electrophysiological properties in whole-brain COs and correlated these properties with developmentally linked morphological and cellular features. Here, we profiled the neuroelectrical activities of COs over the span of 5 months with a multi-electrode array platform and observed the emergence and maturation of several electrophysiologic properties, including rapid firing rates and network bursting events. To complement these analyses, we characterized the complex molecular and cellular development that gives rise to these mature neuroelectrical properties with immunohistochemical and single-cell transcriptomic analyses. This integrated approach highlights the value of COs as an emerging model system of human brain development and neurological disease