This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties. 

In this lesson, you will learn about some typical neuronal models employed by machine learners and computational neuroscientists, meant to imitate the biophysical properties of real neurons. 

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

In this lesson, you will learn more about some of the issues inherent in modeling neural spikes, approaches to ameliorate these problems, and the pros and cons of these approaches. 

 In this lesson, you will learn about some of the many methods to train spiking neural networks (SNNs) with either no attempt to use gradients, or only use gradients in a limited or constrained way. 

In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method. 

This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity. 

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs. 

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.

This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons. 

In this lesson you will learn about fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks. 

This lesson continues discussing properties of neural oscillations and networks. 

In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

This lesson provides a continued discussion and characterization of coupled oscillators. 

This lesson gives an overview of modeling neurons based on firing rate.