Best practices: the tips and tricks on how to get your Miniscope to work and how to get your experiments off the ground.
"Balancing size & function in compact miniscopes" was presented by Tycho Hoogland at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Computational imaging for miniature miniscopes" was presented by Laura Waller at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Online 1-photon vs 2-photon calcium imaging data analysis: Current developments and future plans" was presented by Andrea Giovannucci at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Ensemble fluidity supports memory flexibility during spatial reversal" was presented by William Mau at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
How to start processing the raw imaging data generated with a Miniscope, including developing a usable pipeline and demoing the Minion pipeline
This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.
This lecture covers the application of diffusion MRI for clinical and preclinical studies.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
The Virtual Brain is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to The Virtual brain in particular. Prof. Ritter will present the newest approaches for clinical applications of The Virtual brain - that is, for stroke, epilepsy, brain tumors and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy and understanding of neurological disease.
The concept of neural masses, an application of mean field theory, is introduced as a possible surrogate for electrophysiological signals in brain simulation. The mathematics of neural mass models and their integration to a coupled network are explained. Bifurcation analysis is presented as an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Finally, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.
The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained. On this basis, the concept of epilepsy simulation is developed. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
This lecture presents the Graphical (GUI) and Command Line (CLI) User Interface of TVB. Alongside with the speakers, explore and interact with all means necessary to generate, manipulate and visualize connectivity and network dynamics. Speakers: Paula Popa & Mihai Andrei
This lecture briefly introduces The Virtual Brain (TVB), a multi-scale, multi-modal neuroinformatics platform for full brain network simulations using biologically realistic connectivity, as well as its potential neuroscience applications: for example with epilepsy.
This lecture introduces the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components.
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.