This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.
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.
In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.
This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted.
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 explores how researchers try to understand neural networks, particularly in the case of observing neural activity.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This is the Introductory Module to the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers concepts associated with neural nets, including rotation and squashing, and is a part of the Deep Learning Course at New York University's Center for Data Science (CDS).
This lecture covers the concept of neural nets training (tools, classification with neural nets, and PyTorch implementation) and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture discusses the concept of natural signals properties and the convolutional nets in practice and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture covers the concept of recurrent neural networks: vanilla and gated (LSTM) and is a part of the Deep Learning Course at NYU's Center for Data Science.
This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy-based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.