This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.
This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.
In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
In this lesson, users can follow along as a spaghetti script written in MATLAB is turned into understandable and reusable code living happily in a powerful GitHub repository.
This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.
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.