Workshop 1: Small scale brain initiatives
|Thursday, August 20 10:20-12:10|
|Chair: Romain Brette|
|Ann-Shyn Chiang||FlyDriver: A connectomics tool for manipulating neural circuits
in the Drosophila brain at single cell resolution
Making the connectome work: Lessons from simple systems
|Paul S. Katz||
Comparative neural circuitry in sea slugs; a multiplicity of mechanisms
In recent years, considerable efforts have been devoted to the development of large scale measurements of entire mammalian brains, in the hope that they would provide decisive insights into brain function. Yet connectomes have already been obtained in smaller circuits, including the entire nervous system of C. Elegans, and their relation to circuit function has turned out to be not straightforward. In this workshop, we will discuss some of these attempts to understand the function of small nervous systems, and what we have learned about the relation between structure and function.
Workshop 2: Large scale brain initiatives
|Thursday, August 20 13:00-15:30|
|Chair: Sean Hill|
|Walter Koroshetz||The NIH Brain Iniative: computing from action potentials to behavior|
|Rickard Frackowiak||What can modern informatics bring to an understanding of diseases of the brain?|
|Yoko Yamaguchi||The role of the J-Node in Brain/MINDS|
Workshop abstract and more speakers will be announced soon!
Workshop 3: Neuromorphic computing and challenges
|Friday, August 21 10:20-12:10|
|Chair: Jeanette Hellgren-Kotaleski|
|Tadashi Yamazaki||Building a full-size artificial cerebellum on a computer|
|Giacomo Indiveri||Learning and Plasticity in Neuromorphic Systems|
|Romain Brette||Brian for neuromorphic computing|
|Runchun Mark Wang||Neuromorphic Engineering: New computational paradigms inspired by the brain|
Future computing systems will capitalize on our increased understanding of the brain through the use of similar architectures and computational principles. During this workshop, we bring together recent developments in this rapidly developing field of neuromorphic computing systems, and also discuss challenges ahead.
In the neuromorphic systems field, emulation of neural systems is done using the implementation of neural elements in silicon. Typically, parallel analog and/or digital VLSI circuits are used; and the stochastic behavior of event driven communication between simple devices resembling neurons embedded in massively parallel and recursive network architectures is exploited. Such hardware systems, whose design is inspired by the brain, have the potential to create a paradigm shift in terms of energy efficiency, fault tolerance, adaptability as well as information processing capabilities.
For example, neuromorphic systems may in the future be able to mimic the capabilities of adaptive pattern recognition and motor control capabilities found in the vertebrate brain. Also, already today neuromorphic systems allow emulation and simulations of computational neural models in real time or faster.