The proposal focuses on prospective ideas that can be employed for enhancement of the existing INCF Impact Visualization Portal. It goes into depth, explaining every idea's importance and possible way of implementation. Additionally, it proposes novel ideas, as well as ideas for upgrading the existing features.

This proposal aims to improve the capability of markdown exporter package inorder to cover multicompartment model, and also to support generation of p.d.f, h.t.m.l , latex file types not only this but also to follow a standard template generation using jinja2 template generation tool , Inorder to streamline the process for user to select the output according to their requirements based on their template selection.

The Active Segmentation Platform for ImageJ (ASP/IJ) aims to provide a general-purpose workbench that allows biologists and other domain experts to access state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. This project focuses on enhancing the functionality and user experience of ASP/IJ by updating its user interfaces and developing a new visualization and reporting panel using the Weka (Waikato Environment for Knowledge Analysis) library. This panel will enable users to easily utilize Weka's advanced visualization and analysis tools, thereby facilitating image segmentation and classification.

This project studies the development of a tiny worm called Caenorhabditis elegans, focusing on how its cells grow and interact from its early stages. It uses two special tools called Spatial Hypergraphs and Lineage Hypergraphs to map out and understand these interactions. Spatial Hypergraphs look at how cells are arranged and interact in space, which helps us understand how they communicate and influence each other. Lineage Hypergraphs track the family tree of each cell, showing how cells evolve and form different parts of the worm's body. By combining these tools, the project aims to get a complete picture of the worm's development, from a single cell to a fully formed organism. This work could help us learn more about basic life processes and improve medical and tissue engineering fields. The project is supported by international organizations interested in neuroscience and the use of advanced computer models to study brain and nervous system development.

Gesture-based music generation has existed for some years now, thanks to software such as Wekinator. However, they use machine learning methods that have been overtaken by recent Deep Learning innovations, and therefore limit the studies that can be carried out on musical creativity and sound/movement interaction. The aim of this project is to use these modern Deep Learning methods to generate music driven by motion capture. The framework consists of 3 distinct modules. Firstly, user gestures are detected using landmarks generated by MediaPipe by Google. Secondly, these landmarks are used to recognize the gesture performed by the user, and generate Open Sound Control (OSC) signals. Finally, the OSC signals are converted into music using Max/MSP. In addition, new gestures can be dynamically added to the framework, with just a few examples, and mapped to new sounds. Deliverables: - Experiment to estimate the appropriate delay between a gesture and the resulting sound/effect. - Framework based on LivePose or Pose2Art using only the gestures predifined in MediaPipe, able to interface the chosen camera and the sound-card - Model based on MediaPipe holistic model to classifiy dynamic gestures - Famework to map gestures to sounds through OSC signals. - Few-Shot Learning-based model to dynamically add new gestures.

We aim to further enhance model development with NetPyNE’s “batch” subpackage by refactoring the code base for ease of use and scalability, then use the improved batch subpackage to explore and typify the effectiveness of various search algorithms (random, population based, various posterior based…) on a diverse model repository including: rodent motor(M1), rodent somatosensory(S1), and macaque auditory (A1) thalamocortical circuits to make NetPyNE’s capabilities more efficient for computational neuroscience research.