Biology subjects

Bhaduri, B.

Publications and source records attributed to Bhaduri, B..

2 recordsLinked to original sources

bioRxiv2023

BRAND: A platform for closed-loop experiments with deep network models

Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes, which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

neuroscience↗

bioRxiv2017

Simultaneous cell traction and growth measurements using light

Understanding cell mechanotransduction is important for discerning matrix structure-cell function relationships underlying health and disease. Despite the crucial role of mechanochemical signaling in phenomena such as cell migration, proliferation, and differentiation, measuring the cell-generated forces at the interface with the extracellular matrix during these biological processes remains challenging. An ideal method would provide continuous, non-destructive images of the force field applied by cells, over broad spatial and temporal scales, while simultaneously revealing the cell biological process under investigation. Toward this goal, we present the integration of a new real-time traction stress imaging modality, Hilbert phase dynamometry (HPD), with the technique of spatial light interference microscopy (SLIM) for label free monitoring of cell growth. HPD relies on extracting the displacement field in a deformable substrate, which is chemically patterned with a fluorescent grid. The displacements introduced by the cell are captured by the phase of the periodic signal associated with the grid, borrowing concepts from holography. The displacement field is uniquely converted into forces by solving an elasticity inverse problem. Because the measurement of displacement only uses the epi-fluorescence channel of an inverted microscope, we can simultaneously achieve measurements in transmission. We performed SLIM and extracted cell mass on the same field of view in addition to the measured displacement field. We used this technique to study mesenchymal stem cells and found that cells undergoing osteogenesis and adipogenesis exerted larger and more dynamic stresses than their precursor. Our results indicate that the MSCs develop the smallest forces and growth rates. We anticipate that simultaneous cell growth and traction measurements will improve our understanding of mechanotransduction, particularly during dynamic processes where the matrix properties provide context to guide cells towards a physiological or pathological outcome, e.g., tissue morphogenesis, or cancer metastasis.

cell biology↗