AYAOSHIMA.MPYCPP

Machine Learning / Computational Neuroscience

Tensor Component Analysis (TCA) for Neural Plasticity in Maternal Learning

  • Developed and implemented statistical modeling method TCA to decompose high-dimensional neuron-firing data from the paraventricular nucleus (PVN) of mice, identifying structured neural patterns underlying maternal behavior learning
  • Discovered that active maternal learning strengthens neural synchrony and precise temporal firing, demonstrating that oxytocin-related PVN circuits drive adaptive plasticity in social learning

Statistical ModelingPCATCAComputational NeuroscienceData AnalysisDimension ReductionMatlabPython
Tensor Component Analysis (TCA) for Neural Plasticity in Maternal Learning Image 1Tensor Component Analysis (TCA) for Neural Plasticity in Maternal Learning Image 2

Neural-Symbolic VQA: Multi-Modal AI with CNN, NLP, and Symbolic Reasoning

  • Developed a Neural-Symbolic VQA system, integrating computer vision (CNN), NLP, and Symbolic Reasoning, trained and evaluated on the Sort-of-CLEVR dataset, which includes 10k images and 200k questions.
  • Achieved accuracy of 88% in relational and 99% in non-relational questions.

Multi-ModalNLPComputer VisionCNNPyTorchSymbolic ReasoningPython
Neural-Symbolic VQA: Multi-Modal AI with CNN, NLP, and Symbolic Reasoning Image 1

Object Tracking Under Occlusions: CNN Detection with Kalman Prediction

Leveraged a pre-trained CNN (ResNet), combined with the MS COCO dataset resulting in accurate tracking of obscured ball trajectories, integrating object detection, classification, and motion prediction using Kalman filter

Mathematical ModelingKalman FilterObject TrackingCNNResNetPython