Neuron banner

Menu:

Computational Neuroscience and Neuroengineering

Seminar Series

 7/29/2008Pradeep ShenoyHuman-aided Computing: Utilizing Implicit
Human Processing to Classify Images
 7/14/2008Eric PohlmeyerA brain-machine interface for regaining control
of a paralyzed arm
 6/9/2008Gabriel A. SilvaMapping the functional connectivity of
cellular neural networks
 5/1/2008Daniel A. ButtsThe Importance of Time in Visual Computation
 4/15/2008Klaus-Robert MuellerToward Brain Computer Interfacing
 4/14/2008Timothy GardnerThe formation of neural circuits and behavior in songbirds
 3/24/2008Sridevi V. SarmaImproving Deep Brain Stimulation in Parkinson's Disease Using Feedback Control
 3/10/2008Marom BiksonRational Design of Electrotherapy Devices: Translational Neural Engineering at CCNY BME
 1/25/2008John G. HarrisBiologically Inspired Sensing and Coding of Signals
 

Related Seminars

 3/31/2008Hernando OmbaoSpectral Analysis of Brain Signals
 3/25/2008Daniel LeeBiologically Inspired Sensorimotor Processing
 3/13/2008Luke TheogarajanBio-ionic Neural Interfaces




Computational Neuroscience and Neuroengineering

Seminar Series

Title:   Human-aided Computing: Utilizing Implicit Human Processing to Classify Images
Speaker:   Pradeep Shenoy
Affiliation:   University of Washington
Date:   Tuesday, July 29, 2008
Time:   2:00 pm
Location:   BME Conference Room, 351 Engineering Terrace
Abstract:   We propose the notion of human-aided computing, where brain responses to image stimuli can be used to categorize images based on their content.
In this talk I will describe experiments that use an electroencephalograph (EEG) device to measure the presence and outcomes of implicit cognitive processing in response to visual stimuli consisting of real-world images. Our EEG classification system can distinguish between images containing faces, animals and inanimate objects, and benefits from multiple image presentations to the same or multiple users. Our system can also leverage computer vision techniques for performing this categorization task, and may potentially help improve the performance of these machine algorithms.


Title:   A brain-machine interface for regaining control of a paralyzed arm: A primate model of cortically controlled functional electrical stimulation
Speaker:   Eric Pohlmeyer
Affiliation:   Biomedical Engineering
Northwestern University
Date:   Monday, July 14, 2008
Time:   11:00 am
Location:   DBME Conference Room, 351 Engineering Terrace
Abstract:   We have developed a system in which neural signals recorded from microelectrodes implanted within the brain can be used to control electrical stimulation of paralyzed hand and forearm muscles. Functional electrical stimulation (FES) has often been used to restore the capacity to grasp and manipulate objects to spinal cord injury patients by activating paralyzed muscles through the direct application of electric current. However, providing the user with the means to control the multiple degrees of freedom needed for dexterous manipulation of objects remains an important limitation. The goal of our lab has been to use signals recorded directly from the brain to allow more complex and varied control of grasping FES systems. We have previously shown that multielectrode recordings from the monkey primary motor cortex can be used to predict arm and hand muscle activity during complex reaching tasks. We have now developed a novel FES system which uses information about intended muscle activation extracted from the motor cortex to control stimulation in intramuscular electrodes. In our experiments, two monkey subjects used this cortically controlled FES system to regain voluntary wrist flexion following limb paralysis by blocks of the median and ulnar nerves. Cortically controlled FES of four forelimb muscles approximately doubled the maximum flexion force that the monkeys could achieve. Furthermore, the monkeys were able to voluntarily grade this force to match several different target levels, at speeds of only one-half to two-thirds normal, and force variations of only about twice normal. These results provide an important proof of concept, demonstrating the feasibility of a cortically controlled FES prosthesis for human spinal injured patients. Such systems would offer a significant advantage to patients with injuries in the mid-cervical spinal cord, and potentially even greater benefits to high-cervical spinal cord injured patients with paralysis of the entire upper limb.


Title:   Mapping the functional connectivity of cellular neural networks in order to investigate how networks represent and store information
Speaker:   Gabriel A. Silva
Affiliation:   Silva Research Group
Department of Bioengineering, Jacobs School of Engineering
Department of Ophthalmology, School of Medicine
Date:   Thursday, June 9, 2008
Time:   11:00 am
Location:   414 Schapiro CEPSR
Abstract:   Memories define who we are and our place relative to the world we live in by providing causality and continuity between the immediate present and the past. Although the molecular and cellular mechanisms of learning and memory are generally well understood, we have a limited understanding of how these stereotyped processes scale to the level of cellular neural networks and bestow upon them the ability to encode, store, and recall information.

One cannot directly extrapolate how the molecular mechanisms give rise to dynamic properties at the network level. To address this, our lab has been developing novel experimental and computational methods for imaging and mapping functional signaling in cellular neural networks with single cell and sub-cellular resolution. These methods identify functional patterns of activation in a way that can be related back to the fundamental molecular and cellular neurobiology, effectively mapping the functional connectivity topology of networks as they store information in response to specific stimuli. Two approaches being developed by our lab will be discussed: The spatial graph connectivity model is designed to map the spatiotemporal evolution of the connectivity topology of experimentally measured functional networks with single cell resolution in large networks of neurons and glia, while a variation of an optical flow algorithm was adapted to derive and track second messenger signaling with sub-cellular single pixel resolution.

These functional methods compliment other work in our lab using quantum dot nanotechnology to image cellular anatomy and structure at high spatial resolutions and high signal to noise ratio using epifluorescence microscopy.

Collectively, we are beginning to use these tools to address specific questions about how cellular neural networks encode information, and how they can be reversed engineered in order to design in silico network structures that are statistically and functionally similar to biological neural networks.
Speaker Bio:   Dr. Gabriel A. Silva is an assistant professor in the Departments of Bioengineering and Ophthalmology and the Neurosciences Program at the University of California, San Diego. He received his undergraduate degrees (1996) in human physiology (Hon. B.Sc.) and biophysics (B.Sc.) and a masters degree (1997) in neuroscience from the University of Toronto, Canada. After completing his PhD in neural engineering and neurophysiology at the University of Illinois, Chicago in 2001, he did a postdoctoral fellowship in applied nanotechnology to neuroscience at Northwestern University, Chicago (2003). He joined the faculty at UCSD in 2004. His research focuses on investigating cell structure, signaling, and information processing in biological neural networks in health and disease.


Title:   The Importance of Time in Visual Computation
Speaker:   Daniel A. Butts
Affiliation:   Institute of Computational Biomedicine
Weill Medical College of Cornell University
Date:   Thursday, May 1, 2008
Time:   1:00 pm
Location:   414 Schapiro CEPSR
Abstract:   Even when we look at a stationary visual scene, the image of the visual world projected on the retina is in constant motion due to ever- present movements of the eye. Dynamically changing visual stimuli, such as those created by eye movements, lead to highly reliable neuronal responses in the visual pathway, where timing can be precise down to the level of milliseconds. Because the visual stimulus driving such precision is significantly slower, investigation of neuronal responses in context of natural vision has lead to several insights: regarding both the role of time in representing sensory information, and how fast neuronal signaling arises from more-slowly changing input through the function of local circuitry.

This work thus provides a picture of the relevant timing relationships across visual neuron populations, and sets the stage for understanding how the cortex uses time in performing visual computation.


Title:   Toward Brain Computer Interfacing
Speaker:   Prof. Dr. Klaus-Robert Mueller
Affiliation:   Technical University of Berlin Institute for Software Engineering and Theoretical Computer Science
Intelligent Data Analysis Group Fraunhofer FIRST, Berlin, Germany
Date:   Tuesday, April 15, 2008
Time:   1:00 pm
Location:   DBME Conference Room, 351 Engineering Terrace
Abstract:   Brain Computer Interfacing (BCI) aims at making use of brain signals for e.g. the control of objects, spelling, gaming and so on. This talk will first provide a brief overview of Brain Computer Interface from a machine learning and signal processing perspective. In particular it shows the wealth, the complexity and the difficulties of the data available, a truely enormous challenge: In real-time a multi-variate very strongly noise contaminated data stream is to be processed and neuroelectric activities are to be accurately differentiated.

Finally, I report in more detail about the Berlin Brain Computer (BBCI) Interface that is based on EEG signals and take the audience all the way from the measured signal, the preprocessing and filtering, the classification to the respective application. BCI as a new channel for man-machine communication is discussed in a clincial setting and for gaming.

This is joint work with Benjamin Blankertz, Michael Tangermann, Matthias Krauledat, Claudia Sanelli, Stefan Hauffe (TU, Berlin) and Gabriel Curio (Charite, Berlin).


Title:   The formation of neural circuits and behavior in songbirds
Speaker:   Timothy Gardner
Affiliation:   Post-doctoral Fellow, Massachusetts Institute of Technology
Date:   Monday, April 14, 2008
Time:   11:00am
Location:   DBME Conference Room, 351 Engineering Terrace
Abstract:   Songbirds form detailed auditory memories of other birds. songs and use these memories to guide vocal imitation. This natural behavior provides excellent material to study the rules that govern the wiring of the nervous system and the principles of circuit dynamics in relation to behavior.

In this talk, I will describe recent efforts to build a quantitative understanding of the vocal learning process. This includes developments in auditory processing theory, a study of the behavioral limits of vocal learning in songbirds, and recent in-vivo imaging of neural changes during song learning.


Title:   Improving Deep Brain Stimulation in Parkinson's Disease Using Feedback Control
Speaker:   Sridevi V. Sarma
Affiliation:   Post-Doc Research Fellow
Harvard Medical School and MIT
Date:   Monday, March 24, 2008
Time:   11:00 am
Location:   414 CEPSR
Abstract:   An estimated 3 to 4 million people in the United States have Parkinson's Disease (PD), a chronic progressive neural disease that occurs when specific neurons in the midbrain degenerate, causing movement disorders such as tremor, rigidity, and bradykinesia. Currently, there is no cure to stop disease progression. However, surgery and medications are available to relieve some of the symptoms in the short term. A highly promising treatment is deep brain stimulation (DBS). DBS is a surgical procedure in which an electrode is inserted through a small opening in the skull and implanted in a targeted area of the brain. The electrode is connected to a neurostimulator (sits inferior to the collar bone), which injects current back into the brain to regulate the pathological neural activity. Although DBS is virtually a breakthrough for PD, it is necessary to search for the optimal stimulation signal postoperatively. This calibration often takes several weeks or months because the process is trial-and- error. During a post-operative visit, the neurologist asks the patient to perform various motor tasks and makes subjective observations. Based on these, he/she tweaks the stimulation parameters and asks the patient to return in hours, days or even weeks. The difficulty is that there are millions of stimulation parameters to choose from, though experience has reduced this to roughly 1000 options. In this talk, I will describe my current research efforts, which are to 1. reduce calibration time down to days by developing a systematic testing paradigm using feedback control principles, and to 2. develop a new feedback stimulation paradigm that allows for broader classes of DBS signals to be administered. The former will allow neurologists to treat more patients with DBS and significantly cut medical costs, and the latter may result in further improving patient's responses to DBS while reducing the need for replacements surgeries.


Title:   Rational Design of Electrotherapy Devices: Translational Neural Engineering at CCNY BME
Speaker:   Marom Bikson
Affiliation:   Department of Biomedical Engineering
The City College of New York of CUNY
Date:   Monday, March 10, 2008
Time:   11:00 am
Location:   Biomedical Engineering Conference Room, 351 Eng. Terrace
Announcement:   PDF, PowerPoint
Abstract:   Clinical application of electrical stimulation is a promising treatment for a range of neurological and psychiatric disorders. Despite the establishment of therapeutic electrical stimulation as a standard treatment for several diseases (including Parkinson.s and Depression), fundamental challenges remain in the design of safe and effective technology. This talk summarizes ongoing basic and translational research studies by our group, with the overall goal of developing targeted electrotherapies. Experimental tools ranging from single cell recording and complex morphological reconstruction to system level finite-element-modeling and prototyping are used by our group toward the rational design of therapy treatments. Non-invasive (rTMS, tDCS, TES, ECT) and invasive (DBS, SCS) approaches, diverse cell targeting (neurons, glia, endothelial cells), concurrent drug delivery (Blood-Brain Barrier permeabilization). spatial focality, and safety optimization (joule heat, electroporation) are considered.
Biography   Professor Bikson's research falls broadly into two related topics: 1) the synchronization of neuronal activity in central networks during physiological oscillations (e.g. cognition) and pathological oscillations (epilepsy), with a specific emphasis on the role of non-synaptic (e.g. gap junction, glial) mechanisms; and 2) computer-neural interfaces, including assessing the risks of exposure to 'environmental' electric fields and developing stimulation protocols for the control of abnormal neuronal function (Deep Brain Stimulation).


Title:   Biologically Inspired Sensing and Coding of Signals
Speaker:   John G. Harris
Affiliation:   University of Florida
Date:   Friday, January 25, 2008
Time:   2:00 PM
Location:   Electrical Engineering Conference Room, 13th Floor Mudd
Announcement:   PDF, PowerPoint
Abstract:   We discuss the role of biologically inspired spike representations in various engineering applications including sensor design, time-based signal processing, and power-efficient neural recording circuitry for brain-machine interfaces. These spike-based systems are shown to outperform conventional approaches in terms of various performance metrics such as power consumption, size, SNR, signal bandwidth and dynamic range. We also consider the implications this work has on our understanding of neurobiological systems.
Biography:   Dr. John G. Harris received his BS and MS degrees in Electrical Engineering from MIT in 1983 and 1986. He earned his PhD from Caltech in the interdisciplinary Computation and Neural Systems program in 1991. After a two-year postdoc at the MIT AI lab, Dr Harris joined the Electrical and Computer Engineering Department at the University of Florida (UF). He is currently a full professor and leads the Hybrid Signal Processing Group in researching biologically-inspired circuits, architectures and algorithms for sensing and signal processing. Dr. Harris has published over 100 research papers and patents in this area. He co-directs the Computational NeuroEngineering Lab and has a joint appointment in the Biomedical Engineering Department at UF.


Related Seminars

Title:   Spectral Analysis of Brain Signals
Speaker:   Hernando Ombao
Affiliation:   Brown University
Department of Community Health
Center for Statistical Sciences
Date:   Monday, March 31, 2008
Time:   12:00 - 1:30pm
Location:   1255 Amsterdam Ave., Room 903
Reception:   Tea and coffee served at 11:30am, Room 1025
Abstract:   In many neuroscience experiments, one of the key goals is to investigate the oscillatory behavior of brain signals as quantified by spectral analysis. First, we review some basic ideas of Fourieranalysis of stationary time series and highlight its connection to analysis of variance. Second, we discuss current models and methods for analyzing non-stationary processes (i.e., processes whose spectral decomposition change overtime). Stochastic representations using localized basis functions will be discussed. The talk will conclude with some current investigations including spatio-temporal-spectral analysis and classification of> biological signals. These methods will be illustrated using> electroencephalogram (EEGs) and magnetoencephalogram (MEGs).


Title:   Biologically Inspired Sensorimotor Processing
Speaker:   Daniel Lee
Affiliation:   GRASP (General Robotics, Automation, Sensing, Perception) Lab
Dept. of Electrical and Systems Engineering
University of Pennsylvania
Date:   Tuesday, March 25, 2008
Time:   11:00 am
Location:   Interschool Lab, Room 750 Schapiro CEPSR
Abstract:   How do animals process the tremendous amount of information coming from their senses, in time to take appropriate actions with their muscles? This type of robust sensorimotor processing is still difficult to replicate in robots even with the latest computers, sensors, and actuators. However, new advances in machine learning that borrow techniques from statistical physics, information theory, and differential geometry are helping to create new algorithms that replicate behaviors that animals routinely perform. I will describe some of my lab's recent work on artificial sensorimotor processing systems and demonstrate some of their latest feats and tricks.
Biography   Daniel D. Lee is currently Graduate Chair, Raymond S. Markowitz Faculty Fellow, and Associate Professor of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.A. in Physics from Harvard University in 1990, and his Ph.D. in Condensed Matter Physics from the Massachusetts Institute of Technology in 1995. Before coming to Penn, he was a researcher at Bell Laboratories, Lucent Technologies, from 1995-2001 in the Theoretical Physics and Biological Computation departments. He has received the NSF Career award and the Univ. of Pennsylvania Lindback award for distinguished teaching; he is a fellow of the Hebrew University Institute of Advanced Studies in Jerusalem, and a foreign affiliate of the Korea Advanced Institute of Science and Technology, and has helped organize the US-Japan National Academy of Engineering Frontiers of Engineering symposium. His research focuses on understanding the general principles that biological systems use to process and organize information, and on applying that knowledge to build better artificial sensorimotor systems. He resides in Leonia, New Jersey, with his wife Lisa, six-year old son Jordan, and four-year old daughter Jessica.


Title:   Bio-ionic Neural Interfaces
Speaker:   Luke S. Theogarajan
Affiliation:   Research Laboratory of Electronics
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Date:   Thursday, March 13, 2008
Time:   10:00 am
Location:   Interschool Lab, Room 750 Schapiro CEPSR
Abstract:   Retinal Prostheses are being developed around the world in hopes of restoring useful vision for patients suffering from certain types of diseases like Age Related Macular Degeneration and retinitis pigmentosa. This talk will examine two approaches to developing such a retinal prosthesis. The first is an electrically based retinal prosthesis and the second is a novel bio-ionic neural interface.

The central component of an electrical retinal prosthesis is a wirelessly powered and driven stimulator chip. In this talk we will discuss the design of a 15-channel, low-power, fully implantable stimulator chip. The chip is powered wirelessly and receives wireless commands. The chip features a CMOS only ASK detector, a single-differential converter based on a novel feedback loop, a low-power adaptive bandwidth DLL and 15 programmable current sources that can be controlled via four commands.

Though electronics offer a superior computational platform, the electrical interface to neural tissue is not always optimal. The key limitation of the electrical interface is fundamental: electronics and the natural neural environment are incompatible in both, form and function. One of the main issues with the electrical interface is the need for large amounts of current. This further necessitates the use of large electrodes, due to safety issues, that leads to stimulation of large populations of neurons rather than a few.

Clearly there is a need for a fundamentally different approach to neural interfaces. The ultimate challenge is to design a self-sufficient neural interface. The ideal device will lend itself to seamless integration with the existing neural architecture. Communication with the neural tissue should then be performed via chemical rather than electrical messages. However, catastrophic destruction of neural tissue due to release of large quantities of a neuroactive species precludes the storage of quantities large enough to suffice for the lifetime of the device. The ideal device then should actively sequester the chemical species from the body and release it upon receiving appropriate triggers in a power efficient manner.

The use of ionic gradients, specifically K+ ions as an alternative chemical stimulation method will be examined in this talk. The key advantage being that the required ions can readily be sequestered from the background extracellular fluid. Results from in-vitro stimulation of rabbit retina show that modest increases in K+ ion concentration are sufficient to elicit a neural response.

The talk will then outline the different components that will be needed to build a neural interface using the ionic stimulation method. One of the key components is the development of a self-assembling potassium selective membrane. To achieve low-power the membranes must be ultrathin to allow for efficient operation in the diffusive transport limited regime. One method of achieving this is to use lyotropic self-assembly; unfortunately, conventional lipid bilayers cannot be used since they are not robust enough. Furthermore, the membrane cannot be made potassium selective by simply incorporating ion carriers since they would eventually leach away from the membrane. A single solution that solves all the above issues will be then discussed. The talk will then conclude by discussing some of the exciting opportunities and challenges that lie in this intersection of biology, chemistry and electrical engineering.