U-Nets for medical Image-Segmentation  -  Day 13 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Sequence Processing -  Day 15 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

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. 

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

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 focuses on how the immune system can target and attack the nervous system to produce autoimmune responses that may result in diseases such as multiple sclerosis, neuromyelitis, and lupus cerebritis manifested by motor, sensory, and cognitive impairments. Despite the fact that the brain is an immune-privileged site, autoreactive lymphocytes producing proinflammatory cytokines can cause active brain inflammation, leading to myelin and axonal loss.

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.

This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases. 

This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

This lecture focuses on the rationale for employing neuroimaging methods for movement disorders.

This lecture provides an overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the central nervous system. 

This lesson provides an introduction to neurons, synaptic transmission, and ion channels.

This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.