This article presents a review of computational methods for connectivity inference from
neural activity data derived from multi-electrode recordings or fluorescence imaging. We first …

Resting-state large-scale brain models vary in the amount of biological elements they
incorporate and in the way they are being tested. One might expect that the more realistic …

This paper explores the idea of using deep neural network architecture with dynamically
programmed layers for brain connectome prediction problem. Understanding the brain …

The inference of neuronal connectome from large-scale neuronal activity recordings, such
as two-photon Calcium imaging, represents an active area of research in computational …

Despite advancements in the development of cell-based in-vitro neuronal network models,
the lack of appropriate computational tools limits their analyses. Methods aimed at …

Within machine learning, the supervised learning field aims at modeling the input-output
relationship of a system, from past observations of its behavior. Decision trees characterize …

We organized a Challenge to unravel the connectivity of simulated neuronal networks. The
provided data was solely based on fluorescence time series of spontaneous activity in a net …

Knowledge of functional grou**s of neurons can shed light on structures of neural circuits
and is valuable in many types of neuroimaging studies. However, accurately determining …

Probabilistic graphical models have become an important unsupervised learning tool for
detecting network structures for a variety of problems, including the estimation of functional …

Many of the methods available for the study of cortical influences on striatal neurons have
serious problems. In vivo the connectivity is so complex that the study of input from an …