This lecture covers advanced concept of energy based models. The lecture is a part of the Advanced energy based models modules of the the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this course include: Energy-Based Models I, Energy-Based Models II, Energy-Based Models III, and an Introduction to Data Science or a Graduate Level Machine Learning course.

This lesson gives an introduction to deep learning, with a perspective via inductive biases and emphasis on correctly matching deep learning to the right research questions.

As a part of NeuroHackademy 2021, Noah Benson gives an introduction to Pytorch, one of the two most common software packages for deep learning applications to the neurosciences.

Learn how to use TensorFlow 2.0 in this full tutorial for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

In this hands-on tutorial, Dr. Robert Guangyu Yang works through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions, with a quick demonstration of how we can train and analyze RNNs on various cognitive neuroscience tasks. Familiarity of Python and basic knowledge of Pytorch are assumed.

This lecture provides an introduction to the study of eye-tracking in humans. 

From the retina to the superior colliculus, the lateral geniculate nucleus into primary visual cortex and beyond, this lecture gives a tour of the mammalian visual system highlighting the Nobel-prize winning discoveries of Hubel & Wiesel.

From Universal Turing Machines to McCulloch-Pitts and Hopfield associative memory networks, this lecture explains what is meant by computation.

In an overview of the structure of the mammalian neocortex, this lecture explains how the mammalian cortex is organized in a hierarchy, describing the columnar principle and canonical microcircuits.

The retina has 60 different types of neurons. What are their functions? This lecture explores the definition of cell types and their functions in the mammalian retina.

Optical imaging offers a look inside the working brain. This lecture takes a look at orientation and ocular dominance columns in the visual cortex, and shows how they can be viewed with calcium imaging.

Functional imaging has led to the discovery of a plethora of visual cortical regions. This lecture introduces functional imaging techniques and their teachings about the visual cortex.

This lecture explains these ideas and explores the task of characterizing neuronal response properties using information theory.

What is color? This lecture explores how color is "made" in the brain and variations of color perception including trichromacy, color blindness in men, tetrachromatic vision in women, and genetic engineering of color perception.

How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.

What is the difference between attention and consciousness? This lecture describes the scientific meaning of consciousness, journeys on the search for neural correlates of visual consciousness, and explores the possibility of consciousness in other beings and even non-biological structures.

This primer on optogenetics primer discusses how to manipulate neuronal populations with light at millisecond resolution and offers possible applications such as curing the blind and "playing the piano" with cortical neurons.