Hot Topic Speakers

This hot topic discussion will list the steps for translation of circuit neuroscience from basic discovery to clinical applications, starting with behavioral animals models, reviewing best practice of optogenetics interventions and discussing the different types of approaches are currently used. 

The panel will highlight advances in rodents and, particularly, in implementing optogenetics in humans and their impact to develop novel deep brain stimulation (DBS) protocols. 

Christian Lüscher leads the Laboratory of Circuits and Behavior at the University of Geneva. His team has performed seminal work towards the understanding of the neural mechanisms underlying addiction and other behavioral diseases. He is currently interested on the development of novel protocols for deep brain stimulation based on optogenetic circuit interrogation in rodent models of addiction and obsessive compulsive disorders. See: http://www.addictionscience.unige.ch/

Aryn Gittis  leads a Laboratory at Carnegie Mellon University. Her team investigates do neural circuits transform our thoughts into actions, with a particular focus how neural circuits in the basal ganglia are altered by experience and why certain circuits breakdown in movement disorders such as Parkinson’s disease and dystonia. Her lab uses optogenetics, electrophysiology, histology, and behavior to study the function of neural circuits in brain slices and in vivo. See: https://labs.bio.cmu.edu/gittis/

Botond Roska is Group Leader of Central Visual Circuits & Human Retinal Circuit Groups at the Institute of Molecular and Clinical Ophthalmology in Basel. His team investigates how the retina functions at the cellular level. They are using their findings to develop therapeutic approaches for treating vision loss and blindness caused by retinal diseases. Their focus is on gene therapies that restore light sensitivity to the cells of the retina and thus renew the functionality of blind retinas. With his work, Botond Roska has revolutionized ophthalmology. See: https://iob.ch/people/botond-roska  

Machine learning -including deep learning- appears to be transforming our methods and our understanding of brain function, as well as our ability to characterize behavior.

This hot topic discussion will present the state-of-the-art of the topic and highlight relevant work to quantify specific behaviors across different species, as well as how to tackle the links between brain and behavior.

In addition to presentations by the speakers, we are planning an animated discussion about promises and perils, as well as perspectives, of this approach, involving all presenters and the public.

Chaired by Wulfram Gerstner.

Mackenzie Mathis is the Bertarelli Foundation Chair of Integrative Neuroscience at the Brain Mind Institute, Swiss Federal Institute of Technology Lausanne (EPFL), and an ELLIS Scholar. Her lab works on mechanisms underlying adaptive behavior in intelligent systems. They combine machine learning, computer vision, and experimental work in rodents with the goal of understanding the neural basis of adaptive motor control. See: http://www.mackenziemathislab.org/

Pavan Ramdya leads the Neuroengineering Laboratory at the Brain Mind Institute, Swiss Federal Institute of Technology Lausanne (EPFL). His lab aims at reverse-engineer living systems to better understand the mind and to design more intelligent robots. They use flies and combine microscopy, machine learning, genetics, and computational models to address systems-level questions. See: https://www.epfl.ch/labs/ramdya-lab/

Nadine Gogolla is a Research Group Leader at the Max Planck Institute of Neurobiology in Germany. Her lab investigates the neural circuits underlying emotion to understand how the brain integrates external cues, feeling states, and emotions to make decisions. Using machine learning and two-photon microscopy, they have been able to classify mouse facial expressions into emotion-like categories and correlate these facial expressions with neural activity in the insular cortex. See: https://www.neuro.mpg.de/gogolla