Faculty of Computing

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Author: search.filters.author.Kasthurirathna, DStart date: 2025

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    Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence
    (Institute of Electrical and Electronics Engineers, 2025-03-31) Perera, J; Balasubramaniam, S; Somathilaka, S; Wen, Q; Li, X; Kasthurirathna, D; Roohi, A; Nelson, M. T
    As we delve into a life governed by artificial intelligence (AI), ongoing research continues to discover new forms of intelligence that are efficient and closely mimic an organism’s brain in terms of performance. This article presents a new concept termed wet-neuromorphic computing, in which biological cells or organisms are leveraged to perform computational tasks using their natural molecular functions. We map key neuromorphic properties to natural biological computing observed in bacteria, 3-D organoids, and Caenorhabditis elegans. To expand beyond the inspiration of the brain to create conventional neuromorphic computing, the study presents a case study that demonstrates bacterial AI computing using the gene regulatory neural network derived from Escherichia coli’s gene regulatory network for pattern recognition, validated through wet lab experiments. Finally, challenges and future directions are discussed.
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    Spatio-temporal graph neural network based child action recognition using data-efficient methods: A systematic analysis
    (Elsevier Inc, 2025-06-03) Mohottala, S; Gawesha, A; Kasthurirathna, D; Samarasinghe, P; Abhayaratne, C
    This paper presents implementations on child activity recognition (CAR) using spatial–temporal graph neural network (ST-GNN)-based deep learning models with the skeleton modality. Prior implementations in this domain have predominantly utilized CNN, LSTM, and other methods, despite the superior performance potential of graph neural networks. To the best of our knowledge, this study is the first to use an ST-GNN model for child activity recognition employing both in-the-lab, in-the-wild, and in-the-deployment skeleton data. To overcome the challenges posed by small publicly available child action datasets, transfer learning methods such as feature extraction and fine-tuning were applied to enhance model performance. As a principal contribution, we developed an ST-GNN-based skeleton modality model that, despite using a relatively small child action dataset, achieved superior performance (94.81%) compared to implementations trained on a significantly larger (x10) adult action dataset (90.6%) for a similar subset of actions. With ST-GCN-based feature extraction and fine-tuning methods, accuracy improved by 10%–40% compared to vanilla implementations, achieving a maximum accuracy of 94.81%. Additionally, implementations with other ST-GNN models demonstrated further accuracy improvements of 15%–45% over the ST-GCN baseline. The results on activity datasets empirically demonstrate that class diversity, dataset size, and careful selection of pre-training datasets significantly enhance accuracy. In-the-wild and in-the-deployment implementations confirm the real-world applicability of above approaches, with the ST-GNN model achieving 11 FPS on streaming data. Finally, preliminary evidence on the impact of graph expressivity and graph rewiring on accuracy of small dataset-based models is provided, outlining potential directions for future research. The codes are available at https://github.com/sankamohotttala/ST_GNN_HAR_DEML.
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    Exploring emergent topological properties in socio-economic networks through learning heterogeneity
    (2025-12-10) Karavita, C; Lyu, Z; Kasthurirathna, D; Piraveenan, M
    Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socio-economic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates–both at the agent and network levels–remains underexplored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner’s Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics–including scale-free exponents, Estrada heterogeneity, and assortativity–reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socio-economic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.