CNS 2007 Workshop Proposal

 

Title:

 

Neuro-Machine Interfaces: Integrating Biology and Technology to Develop Functionally Relevant Devices

 

 

Invited Speakers:

• Don H. Johnson, Department of Electrical & Computer Engineering, Rice University (Email , Webpage)

• Astrid A. Prinz, Department of Biology, Emory University (Email, Webpage)

• Dongchul C. Lee, Advanced Bionics (Email, Bio)

 

 

Schedule:

 

The workshop will take place on July 12^th morning in the Bahen Building on the downtown campus of the University of Toronto (Map). It will run for half a day. Attendance is open to all CNS attendees. Those interested in presenting are invited to contact the workshop organizer.

 

 8:30  -  8:45   :  Introduction / ANS Presentation

 8:45  -  9:35   :  Prof. Don H. Johnson & Ilan N. Goodman (Presentation)

                          Information theoretic analysis of the effectiveness of neural prosthetics

 9:40 - 10:00   :  Dr. Dongchul C. Lee

                          Field Sculpting to correct electric field distortion by percutaneous lead migration

10:00 - 10:20  :  Coffee Break

10:20 - 11:10  :  Prof. Astrid Prinz

                          Neuronal network plasticity and homeostasis - implications for neuroprosthetics

11:15 - 12:30  :  Panel Discussion

 

Instructions for the speakers:

The workshop will consist of:

• Invited Presentations: Speakers will give 40 minute presentations of their work followed by 10 minutes of questions.
• Short Presentations: Speakers will give 10-15 minute presentations of their work followed by 5 minutes of questions.
• Panel Discussion: Our invited speakers and the audience will engage each other on the various issues challenges concerning neuro- machine interfaces.

 

 

Description:

 

Neuroprostheses are medical devices that replace the function of an impaired nervous system.  Some examples of neuroprosthetic devices include: cochlear implants, retinal implants, cortical implants, and functional neuromuscular stimulation (FNS) electrodes.  Neuro-machine interfaces (NMI) use neuroprostheses to read signals from neurons and then computers and algorithms are used to translate those signals into desired actions. 

 

Successful development of functional neuroprostheses requires an interdisciplinary approach, involving experimentalists to understand the physiology and behavior of the nervous system, engineers to develop adaptive biocompatible devices, clinicians to implement and study the interaction between the device and the patient, and computational modelers to integrate the diverse approaches.

 

There are still many important issues that must be addressed for NMI development such as a need for fully-implantable biocompatible devices, real-time computational algorithms, efficient neural signal acquisition and processing, and improved sensory feedback with links to motor output.  Perhaps the most important issue in NMI development is optimizing the behavior of the combined system (biological and technological) by fully utilizing the plasticity of the nervous system.

 

How can computational neuroscience help address these issues?  This workshop will explore some of the major challenges in interfacing biological adaptive systems with adaptive NMI devices: 

 

·        Given that the human nervous system is more complex than in vitro preparations and different from in vivo animal models how do we transform an experimental device from a laboratory setting to a clinically relevant device? 

 

·        How can computational neuroscientists help in improving the design of experimental devices?  How biologically accurate do models have to be, and on what scales, in order to positively contribute to technological development?

 

·        There is a problem in NMI of both too little and too much data.  The number of channels available to interact with the nervous system is limited, while the amount of raw voltage vs. time data acquired from probes can be overwhelming.  How can computational neuroscientists help to maximize use of limited channel data, while extracting only useful information?

 

·        How do we incorporate and take advantage of the properties of the musculoskeletal system in order to maximize the utility and effectiveness of NMI devices?

 

·        The nervous system is adaptive, so NMI control algorithms have to be versatile enough to accommodate this plasticity.  How can we design NMI control algorithms that promote adaptive plasticity in the nervous system throughout the time course of that adaptation?