This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lesson provides an introduction to simple spiking neuron models.
This lesson provides an overview of plasticity on many levels, including short-term, long-term, metaplasticity, and structural plasticity. The lesson also provides xamples related to modelling of biochemical networks.
Note: The sound uptake is a bit noisy the first few minutes, but gets better from about 5 mins in
This lesson gives an introduction to the modelling of chemical computation in the brain.
This lesson provides an introduction to the role of models in theoretical neuroscience.
This lesson introduces different types of models, model complexity, and how to choose an appropriate model.
This lesson gives an overview of balanced excitatory-inhibitory (E-I) networks, stability, and gain modulation.
In this lesson, you will learn about methods for dimensionality reduction of data, with a focus on factor analysis.
This lesson gives an in-depth look into various types of neuronal networks, as well properties, parameters, and phenomena which characterize them.
In this lesson, you will learn about spiking neuron networks and linear response models.
This lesson discusses Bayesian neuron models and parameter estimation.
This lesson gives an overview of Bayesian memory and learning, as well as how to go from observations to latent variables.
In this lesson, you will learn about how constraints can help us understand how the brain works.
This lesson discusses how to approach neural systems from an evolutionary perspective.
This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
This lesson recaps why math, in a number of ways, is extremely useful in data science.
This lesson provides an introduction to the lessons in this course that deal with statistics and why they are useful for data science.
In this lesson, users will learn about the importance of exploratory analysis, as well as how statistics enables one to become familiar with and understand one's data.
This lesson goes over graphical data exploration, including motivations for its use as well as practical examples of visualizing data distributions.
In this lesson, users learn about exploratory statistics, and are introduced to various methods for numerical data exploration.