In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.
This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.
This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.
In this talk, you will learn how brainlife.io works, and how it can be applied to neuroscience data.
As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services.
This talk presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance computing facilities across Canada and around the world.
This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page.
This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.
In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.
This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.
This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.
In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.
This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.
This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.
In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).