61 citations · 63 across the 13 of their papers we have counts for
13 papers
Neuronal Spike Trains as Functional-Analytic Distributions: Representation, Analysis, and Significance
Gabriel A. Silva
The action potential constitutes the digital component of the signaling dynamics of neurons. But the biophysical nature of the full-time course of the action potential associated w…
Leveraging Quantum Superposition to Infer the Dynamic Behavior of a Spatial-Temporal Neural Network Signaling Model
Gabriel A. Silva
The exploration of new problem classes for quantum computation is an active area of research. In this paper, we introduce and solve a novel problem class related to dynamics on lar…
Semisupervised regression in latent structure networks on unknown manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset +2
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is assoc…
Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Tianyi Chen, Youngser Park, Ali Saad-Eldin +10
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids…
Learning without gradient descent encoded by the dynamics of a neurobiological model
Vivek Kurien George, Vikash Morar, Weiwei Yang +6
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss functi…
Generalizable Machine Learning in Neuroscience using Graph Neural Networks
Paul Y. Wang, Sandalika Sapra, Vivek Kurien George +1
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to…