CNEL Seminars


Date	Presented By	Seminar	Description
13 Jan. 2002	Morrison, Scott	Hardware Implementation of the LMS Adaptive Filter for a Brain-Machine	A description of the TI C33 floating point DSP along with comments of implementing adaptive algorithms in DSP. Included is a performance comparison of the normalized LMS algorithm in DSP as well as MATLAB using neural data from the BMI project.
19 Jan. 2005	Pokharel, Puskal P.	Generalized Correlation Function: Definition, Properties and Examples	abstract of the 40 minute seminar
21 Jan. 2003	Erdogmus, Deniz	Information Theory: A Brief Introduction	An overview of the basic principles of information theory is presented. To motivate an application to blind source separation is demonstrated. In order to view the slides, change the file extension from pdf to ppt and open using Powerpoint.
21 Jan. 2004	Li, Yuan	A spiking neural network	A spiking neural network implementing an associative memory is proposed. The corresponding circuit with eight neurons is designed with the AMI 0.5um CMOS process. Willshaw-type or Palm-type binary synapses and integrate-and-fire (IF) neurons are used to simplify the circuit design. Time thresholding is also used in the circuit. A large- scale network with 64 neurons is simulated and its storage capacity is calculated and analyzed.
28 Jan. 2004	Yan, Rui	Phased-Array MRI Image Combination by Mixture of Local Linear Experts	Phased-array magnetic resonance imaging is an important contemporary research field in terms of the expected clinical gains in medical imaging technology. Recent research focused on heuristic coil image recombination methods as well as statistical signal processing approaches. In this presentation, we investigate the performance of an adaptive signal processing approach, namely mixture of local linear experts. The proposed method has the ability to train on a set of images and generalize its performance to previously unseen images. Performance evaluations on real data validate the effectiveness of this method.
30 Jan. 2007	Pokharel, Puskal P.	Single-Trial ERP Estimation Based on Spatio-Temporal Filtering	Seminar by Ruijiang Li, graduate student at the CNEL.
1 Feb. 2006	Ramani, Meena	GENERALIZED BOOSTING ENSEMBLES	A talk by: Prof. Aníbal R. Figueiras-Vidal, DTSC, Universidad Carlos III de Madrid, Spain
6 Feb. 2002	Sanchez, Justin C.	Brain Machine Interfaces for Motor Control	Using the firing patterns of populations of cortical neurons, research has shown that the hand trajectory of owl monkeys can be predicted in real-time. It is believed that the recordings from a fraction of the total number of cortical neurons contain enough information to encode the essential parameters (position, velocity, acceleration, force) of motor tasks. The firing of a neuron can be related to one or a combination of kinematic parameters. Cortical neurons encode the analog information of the environment as a series of membrane depolarizations. Using microwire electrodes, depolarizations can be recorded as voltage spike trains from populations of single neurons. Communicating throughout a 3-D network over the space of the brain cortex, neurons send their information over time. We seek to understand the spatio-temporal encoding and decoding of neuronal firing patterns. This novel research seeks not only to predict the next motor position in time, but to find a model or multiple models which map the firing patters to position over all time. The tools which will exploit the spatio-temporal encoding are adaptive systems. Adaptive systems have many forms but all contain the same components: topology, adaptation algorithm, and optimization criterion. Depending on the problem, certain combinations of components will be better suited to extract information. The mapping of neuronal firing patterns to hand position is a many-to-one. It remains unknown whether this many-to-one relationship is a simple weighted combination of the firing patterns, or a complex combination of many nonlinear functions. We seek to compare the modeling performance of the linear filter with a Time-Delay Neural Network and Recurrent Network. This research is funded by DARPA and represents the collaboration of the University of Florida, Duke University, MIT, and SUNY.
6 Feb. 2007	Pokharel, Puskal P.	An Information Measure of Temporal Structure for Multichannel Spike Trains	Seminar by António R. C. Paiva, graduate student at the CNEL.
8 Feb. 2006	Ramani, Meena	Investigation of Spatio-Temporal Dependencies in Epileptic ECOG	A talk by: Anant Hegde, CNEL, UFL
11 Feb. 2004	Davis, Bryan	Design and Performance of Nanoscale Content-Addressable Memories	Nanoscale components are currently being investigated by many researchers in order to create smaller and potentially faster logic and memory devices. There are many challenges to using components at this scale, such as interfacing these components with microscale components, and mitigating the high defect rate inherent in the assembly of these devices. In this presentation, we propose a content-addressable memory architecture that uses a stochastic interface between the micro- and nano- scales. We show how this techinque provides a solution to both the interface problem and the high defect rate of nanoscale components. Some theoretical performance results are given and high-level simulations are presented.
13 Feb. 2007	Pokharel, Puskal P.	Feature Selection through Local Learning: Theories, Algorithms, and Applications	Seminar by Yijun Sun, Interdisciplinary Center for Biotechnology Research, University of Florida
18 Feb. 2004	Lan, Jing	Gaussian Mixture Model for system identification & control	In this talk, I will outline the GMM on system ID and control. In particular I will discuss the benifit of GMM methods compared with other approaches. These issues are of interest to people who are dealing with system identification, prediction or interpolation. Finally, results on some applications like Chaos control, SISO control, MIMO control are shown.
22 Feb. 2006	Ramani, Meena	Micro Air Vehicle Research at the University of Florida	A talk given by: Dr. Peter Ifju .Professor of Mechanical and Aerospace Engineering Department, University of Florida
3 Mar. 2004	Jenssen, Robert	Information Theoretic Spectral Clustering	It this talk, we show that there is a close connection between an information theoretic pdf distance and the so-called graph-cuts used in spectral clustering. Specifically, we develop a new spectral clustering algorithm where the solution is given as a weighted sum of certain top eigenvectors of the data affinity matrix
8 Mar. 2006	Ramani, Meena	A Lyapunov-Based Control of Engineering Systems	A talk by Dr. Warren Dixon Assistant Professor, Department of Mechanical and Aerospace Engineering, University of Florida
13 Mar. 2007	Pokharel, Puskal P.	Hierarchal Decomposition of Neural Data Using Boosted Mixtures of Independently Coupled Hidden Markov Chains	Seminar by Shalom Darmanjian, graduate student at the CNEL.
29 Mar. 2006	Ramani, Meena	Special two sessions seminar	Session-1: Correntropy as a Novel Measure for Nonlinearity Tests By Aysegul Gunduz Session-2: A Monte Carlo Sequential Estimation for Point Process Optimum Filtering By Yiwen Wang
5 Apr. 2006	Ramani, Meena	A review of electronic nose sensor technologies and signal processing techniques	A talk by: Mr Vikas Meka, Convergent Engineering, Gainesville, Florida
7 Apr. 2004	Ozturk, Mustafa Can	Freeman’s K Sets as Dynamic Computational Models	Recently, the liquid state machine and the echo state network have been shown to possess interesting computational properties (universal approximation) in the class of functionals with exponential decaying memory. In this talk, we briefly review these models and contrast them with another dynamical, biologically plausible model of the olfactory cortex proposed by Walter Freeman. Here we will restrict the architecture to a distributed interconnection of reduced KII sets or a KI network. Our approach models the states of the Freeman network as a representational infrastructure that stores in its dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out, information related to the input history can be accessed and read out. This work provides two important additions to Freeman networks. First, it emphasizes the need for optimal readout, and show how to adaptively derive them. Second, it shows that the Freeman model is able to process continuous, time varying inputs. We will provide theoretical results for the conditions on the system parameters of Freeman model resulting in echo states. We will also present experimental results that verify the validity of this approach. Finally, we will discuss the implications of the echo state property of the Freeman model on how brain might be processing information.
8 Apr. 2004	Holz, Thomas	Spiking Freeman, final report	My final presentation to the CNEL group, covering the spiking Freeman research.
12 Apr. 2000	Hild, Kenneth E.	Blind source separation using information theory	Maximizing the output entropy of a nonlinear demixer has been shown to separate a mixture into its independent components. This can also be accomplished by minimizing the mutual information between the outputs. However, the mutual information method uses an estimate of the joint entropy. For accurate estimation of the joint entropy, an exponentially increasing amount of data is required as the dimensionality increases linearly. This is circumvented in the proposed criteria by restricting the structure to be sphering followed by a rotation matrix. In addition, the proposed method utilizes Renyi’s quadratic entropy as a substitute for Shannon’s, which reduces the computational complexity considerably. The proposed method is compared to a second order technique and to the InfoMax criterion proposed by Bell and Sejnowski.
12 Apr. 2006	Ramani, Meena	Bio-Medical Image Analysis	A talk by: Dr. Aurelio Campilho, INEB - Biomedical Engineering Institute, University of Porto, Portugal
19 Apr. 2006	Ramani, Meena	How to build a time machine	A talk by: Dr. John G. Harris , Professor, Department of Electrical & Computer Engineering, University of Florida
20 Apr. 2005	Xu, Jianwu	Some connections between kernel methods and Information theoretic learning	Information theoretic learning and kernel methods are two research topics emerged from signal processing and machine learning fields. ITL is a signal processing technique that combines information theory and adaptive system. It utilizes information theory as a criterion to update the structure of adaptive system. Kernel-based learning algorithms have been developed in the machine learning community. They are nonlinear versions of linear algorithms where the data has been nonlinearly transformed to a high dimension feature space. In this talk, I will discuss some intriguing connecitons between kernel methods and information theoretic learning. This new discovery enables us to haver different perspectives towards both reseach topics.
22 Apr. 2003	Thogulua, Rajvignesh	Information Theoretic Self-Organization of Multiple Agents	Talks about the application of Information Theoretic Interactions for self-organizing a group of robots
31 Apr. 2004	Hegde, Anant	Analysis of Multivariate structures: a SOM-based Similarity Index Measure	Synchronization and de-synchronization between the cortical-columns of the brain is believed to be one of the plausible reasons for most neurological disorders including epilepsy . It is believed that synchronization occurs due to both local discharges of the neurons as well as at the global levels.Various linear and nonlinear techniques have been developed to quantify the degree of synchronization. I plan to give a brief overview to some of the existing techniques for analyzing Multi-variate structures and then propose SOM-based Similarity Index measure, which is mainly a state-space approach based on nearest neighbors. Simulations performed on toy-data will be presented. I would be concluding with some preliminary results obtained from applying the SOM-based SI on the epileptic data.
10 May 2006	Ramani, Meena	Automatic speech recognition using an echo state network	A talk by: Dr. Mark Skowronski, CNEL, UFL
3 June 2003	Sanchez, Justin C.	Interpreting Neural Activity Through Linear and Nonlinear Models for Brain-Machine Interfaces	NeuroNoon talk about interpreting neural activity through linear and nonlinear models
25 July 2003	Sanchez, Justin C.	BMI Hardware July 03	Overviwe of hardware for DARPA meeting.
31 Aug. 2005	Skowronski, Mark	Electrohysterography	First CNEL seminar of semester, on intrauterine pressure prediction from electrohysterography using the Wiener filter.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: BMI	Justin’s review of the Brain Machine Interface project.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: ITL	Deniz’s review of the Information Theoretic Learning project.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: SiCortex	Dongming’s review of the Silicon Cortex project.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: BDL	Anant’s review of the Brain Dynamics Lab project.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: LoFlyte	Jeongho’s review of the LoFlyte project.
3 Sept. 2003	Skowronski, Mark	CNEL Overview: Speech	Mark’s review of the speech-related projects.
4 Sept. 2002	Rao, Yadunandana	Error whitening criterion for adaptive filtering: Theory and Algorithms	MSE has been the criterion of choice in many function approximation tasks including adaptive filter optimization. There are alternatives and enhancements to MSE that have been proposed in order to improve the robustness of learning algorithms in the presence of noisy training data. In FIR filter adaptation, noise present in the input signal is especially problematic since MSE cannot eliminate this factor. A powerful enhancement technique, total least squares, on one hand, fails to work if the noise levels in the input and output signals are not identically equal. The alternative method of subspace Wiener filtering, on the other hand, requires the noise power to be strictly smaller than the signal power to improve SNR. We have proposed an extension to the traditional MSE criterion in filter adaptation, which we have named the error-whitening criterion. This new criterion is inspired from the observations made on the properties of the error autocorrelation function. Specifically, we have shown that using non-zero lags of the error autocorrelation function, it is possible to obtain unbiased estimates of the model parameters even in the presence of white noise on the training data. In this talk, we will present the theory behind the Error-Whitening Criterion along with the adaptive algorithms that work on this criterion. Both the stochastic and recursive (fixed-point type) algorithms will be presented.
4 Sept. 2002	Sanchez, Justin C.	Input-Output Mapping Performance of Linear and Nonliner Models for Estimating Hand Trajectories from Cortical Neuronal F	NNSP BMI Presentation
5 Sept. 2006	Pokharel, Puskal P.	Synchrony of Neuronal Culture and reproducing kernel Hilbert space on Spike Trains	Talk by Il Park (Memming), graduate student at the CNEL.
6 Sept. 2002	Sanchez, Justin C.	Modified Kalman Filter Based Method for Training State-Recurrent Multilayer Perceptrons	NNSP presentation on using Kalman based methods for training RMLPS
7 Sept. 2005	Ramani, Meena	Evolving into Epilepsy: Multiscale Electrophysiological Analysis and Imaging in an Animal Model	A talk by: Dr. Justin C. Sanchez Assistant Professor, Department of Pediatrics, Division of Neurology University of Florida
9 Sept. 2002	Nechyba, Michael	HMM-based Neural Spike Analysis	Goal: Classify arm movement into two classes: (1) stationary and (2) moving Talk outline: • Classifier overview • HMM/VQ discussion • Generating training data • Results • Current/future work
10 Sept. 2003	Xu, Dongming	Synchronization Analysis on Coupled Reduced KII sets	We discuss the synchronization of two identical reduced KII sets. Analytical solutions of the conditions on the coupling strength between the two identical nonlinear dynamical systems are presented. When determining the strength according to their computed boundaries, the two systems are guaranteed to be synchonized and presenting the dynamical behavior of an individual set. The results provide theoretical information for understanding higher level structures in Freeman’s model as well as for constructing applications of KII networks.
14 Sept. 2005	Uysal, Ismail	Bandwidth Extension of Telephone Speech Using Frame-based Excitation and Robust Features	The standards that are still in use for telephone commu-nications since the 1950s limit the information bandwidth to 300-3400Hz. However, in normal conversational speech, the frequency content is mainly between 0-8000Hz. This constraint degrades not only the sound quality but also the intelligibility of the transmitted signal. Instead of modifying the present telecommunication infrastructures, which would cost billions of dollars, many researchers have been studying more efficient methods to increase the quality of telephone speech. This paper develops an innovative solution to bandwidth extension, which is based upon the Linear Source Filter Model that breaks speech up into two parts: the excita-tion and the spectral envelope. Novel approaches are used to extend the frequency information for both parts. This algorithm particularly emphasizes low frequency reconstruc-tion without neglecting high frequencies. Furthermore, dif-ferent feature sets to model the spectral envelope are em-ployed for better performance under noisy conditions
15 Sept. 2004	Hegde, Anant	Spatio-temporal synchronizations in ECOG data	Synchronization and de-synchronization between the cortical-columns of the brain are believed to be one of the plausible reasons for most neurological disorders including epilepsy.It is believed that synchronization occurs due to both local and global discharges of the neurons. In quantifying this phenomenon, one of the main difficulties is that the brain is a highly complex, non-linear system. The spatio- temporal changes in information across different regions of the brain are rapid and often very subtle. Therefore, one way to understand how physiological activities are coordinated in the brain is to understand how subsystems are coupled and how information propagates through them. From the epilepsy perspective, quantifying the changes in spatio- temporal interactions could potentially help us develop seizure- warning systems. This quantification would also help us identify the regions that actively participate during epileptic seizures. Various linear and nonlinear techniques have been developed to quantify the degree of synchronization. A brief overview a few of the existing techniques will be discussed . In detail, we will discuss the SOM-based Similarity Index (SI) measure, a non-linear technique to quantify synchronization in multivariate time-structures. Simulation results will be presented followed by results on epileptic EEG data. We will also discuss a couple of clustering techniques used to quantify the spatial-distribution of the channel interactions.
15 Sept. 2004	Cho, Jeongho	Research Progress in the CNEL	Review of the Silicon Cortex Project, Motorola Project, Brain Dynamics Project, Bio-Nano Project, Information Theoretic Learning Project, and LoFLYTE Project.
18 Sept. 2002	Skowronski, Mark	Human Factor Cepstral Coefficients: Improving on a generation's speech feature extraction algorithm	First described in 1980, mel frequency cepstral coefficients (mfcc) have become the premier speech front end for today’s state-of-the-art speech recognition systems. The key aspect of the algorithm is a filter bank inspired by the human auditory system: triangular filters are spaced along the perceptually-motivated mel frequency scale. Yet the original 1980 algorithm does not adequately describe filter _bandwidth_. This ambiguity has led to the creation of popular variations seen today as well as tragically-flawed implementations. This talk will present a solution that clarifies this ambiguity--human factor cepstral coefficients--by decoupling filter bandwidth from other filter design parameters. Recognition experiments using a Hidden Markov Model and various additive noise sources will be presented to validate the new algorithm.
19 Sept. 2001	Hild, Kenneth E.	Introduction to Blind Source Separation	Blind source separation attempts to restore mutual statistical independence of a set of mixed (originally independent) signals. It is achieved with an adaptive filter, and all adaptive filters have three components, the architecture, criterion, and optimization method. This discussion will focus on the different architectures and criteria used for source separation, as indicated in the source separation literature. In addition, a special emphasis will be placed on criteria developed within CNEL.
19 Sept. 2006	Pokharel, Puskal P.	Implementation of a Receiver Modem on FPGA using Xilinx System Generator	Talk by Dongho Han
21 Sept. 2005	Ramani, Meena	AN IMPROVED MINIMUM ERROR ENTROPY CRITERION WITH SELF ADJUSTING STEP-SIZE	A talk by: Seungju Han and Sudhir Rao Computational NeuroEngineering laboratory, Department of Electrical and Computer Engineering
22 Sept. 2004	Hegde, Anant	Spatio-temporal synchronizations in Epileptic EEG	Synchronization and de-synchronization between the cortical-columns of the brain are believed to be one of the plausible reasons for most neurological disorders including epilepsy.It is believed that synchronization occurs due to both local and global discharges of the neurons. In quantifying this phenomenon, one of the main difficulties is that the brain is a highly complex, non-linear system. The spatio- temporal changes in information across different regions of the brain are rapid and often very subtle. Therefore, one way to understand how physiological activities are coordinated in the brain is to understand how subsystems are coupled and how information propagates through them. From the epilepsy perspective, quantifying the changes in spatio- temporal interactions could potentially help us develop seizure- warning systems. This quantification would also help us identify the regions that actively participate during epileptic seizures. Various linear and nonlinear techniques have been developed to quantify the degree of synchronization. A brief overview a few of the existing techniques will be discussed . In detail, we will discuss the SOM-based Similarity Index (SI) measure, a non-linear technique to quantify synchronization in multivariate time-structures. Simulation results will be presented followed by results on epileptic EEG data. We will also discuss a couple of clustering techniques used to quantify the spatial-distribution of the channel interactions.
24 Sept. 2003	Ozturk, Mustafa Can	The Spike Activity of Neocortical Columns: A Dynamical Systems Analysis	I shall begin by introducing an abstract dynamical system for networks of spiking neurons that is formulated based on a general model of the biological neuron. I shall then present simulation as well as analytical results for the class of instantiations of the system that model typical neocortical columns. Based on these results I shall argue that the spike activity of neocortical columns is profoundly influenced by attractors that are not only almost surely chaotic but are also potentially anisotropic.
26 Sept. 2006	Pokharel, Puskal P.	Correntropy Coefficient: a Reproducing Kernel Hilbert Space Approach to Generalized Synchronization Detection.	Talk by Jian-Wu Xu
28 Sept. 2005	Ramani, Meena	Brainprints: A biological approach to understanding learning disability	A talk by: Dr. Christiana M Leonard Professor, Department of Neuroscience University of Florida
1 Oct. 2003	Kim, Phil	Optimal signal processing in brain-machine interface	The research of Brain-Machine Interfaces (BMIs) has recently demonstrated promising results of estimating the transfer function from the neural firing activities to the hand positions of a primate, with relatively simple Wiener filters. It is rather surprising that a simple linear projection shows an ability to approximate the operation of the highly complex neural-motor system between cortical neurons and muscle fibers, with the correlation coefficient around 0.8 between the estimated and the actual hand trajectories. In this work, we investigate the challenges and opportunities of this class of models for BMIs based on the optimal signal processing framework, and possibly shed light on the direction to go beyond the present level of estimation performance.
3 Oct. 2006	Pokharel, Puskal P.	Application of biomechanical models and reinforcement learning to the	Seminar By Jack DiGiovanna
6 Oct. 2004	Lan, Jing	Gaussian Mixture Model for System Identification & Control	This proposal investigates a new methodology of combining the improved Gaussian mixture models (GMM) with local linear model (LLM) for dynamical system identification and control. In order to understand the advantage of the mixture model, mixture model structure and training method are discussed in detail, along with growing self-organizing criterion as an improving initial setup for Gaussian distribution which makes the GMM converge more stable. To increase local modeling capability and decrease modeling error, local linear models are trained based on GMM to make one-step prediction. Following the local modeling approach, dynamic inverse controllers are designed to realize a tracking application. Four application systems with different dynamics are simulated to verify the modeling and control capability of the improved Gaussian mixture model. Through experiments and comparison with self-organizing maps and radial basis functions, it is shown that the improved GMM-LLM is a more flexible modeling approach with higher computation efficiency and higher global-minimum-converge performance.
9 Oct. 2002	Yan, Rui	Phased Array MRI Image Reconstruction	The seminar Introduces some background with respect to Magnetic Resonance Imaging (MRI) and phased-array coil. A data model of phased-array coil MRI is set up based on the coil senistivity constancy. The analysis of three new Algorithms are compared to the conventional sum-of-squares (SoS).
10 Oct. 2006	Pokharel, Puskal P.	A Pulse-Based Feature Extractor for Spike Sorting Neural Signals.	Talk by Christy Rogers, Graduate student at the CNEL.
16 Oct. 2002	Xu, Dongming	Understanding the KII	Recent progress in SiCortex group is presented. Based on bifurcation analysis, we find regions where a reduced KII set (in the computational model of the olfactory cortex) could be linearized. This helps us to control the dynamical behavior of a reduced KII set and KII network. Hardware measurement of an analog VLSI chip that contains one reduced KII set is also presented.
20 Oct. 2004	Yan, Rui	Competitive Mixture of Local Linear Experts	This presentation investigates phased-array magnetic resonance imaging (MRI) as an important contemporary research field propelled by expected clinical gains. Although many algorithms have been proposed for phased-array MR image reconstruction, in addition to perhaps the most commonly used sum-of-squares (SoS) algorithm, these approaches are not based on an optimal signal processing framework. In this presentation, the problem of combining images obtained from multiple MRI coils is investigated from a statistical signal processing point- of-view with the goal of improving signal-to-noise-ratio (SNR) in the reconstructed images. However, some weak points including statistical assumptions limit the reconstruction performance. Adaptive learning strategy provides a new scheme for phased-array MRI. To date there are no reported results from the literature in this area. The purpose of this research is to study the performance of an adaptive signal processing approach based on competitive mixtures of local linear experts. The proposed method has the ability to train on a set of images and generalize its performance to previously unseen images. Performance evaluations on real data validate the effectiveness of this method. Future work is also pointed out for further research.
24 Oct. 2006	Pokharel, Puskal P.	A Pitch Estimation Algorithm Based on the Human Perception of Inharmonic Sounds	Talk by Arturo Camacho, Graduate student at the CISE department.
26 Oct. 2005	Ramani, Meena	Advances in Cochlear Implants	A talk by: Dr. Alice E. Holmes Professor, Department of Communicative Disorders University of Florida
31 Oct. 2006	Pokharel, Puskal P.	A Brief Introduction to Funded Research Opportunities with the Air Force Research Laboratory / Munitions Directorate	Talk by David Gray
2 Nov. 2005	Ramani, Meena	Correntropy: A new similarity measure in kernel spaces	A talk by:Dr. Jose Principe Distinguished Professor, Department of Electrical & Computer Engineering University of Florida
3 Nov. 2004	Stiber, Michael	Local and Global Behavior of Neural Error Correction	Neural information coding is often characterized as noisy and unreliable, typically because spike trains are irregular in appearance and experimental stimuli presented at different times produce different responses. This paper describes ongoing work investigating the effects and significance of errors in spike trains. More specifically, here we test the dependence of error correction in neural coding on a postsynaptic neuron’s extant behavior, described in nonlinear dynamical terms. We show that the time for a neuron to recover from an error varies significantly, depending on its preceding stationary behavior and that behavior’s position within the cell’s global bifurcation structure. This implies a model of neural computation based not solely on attractors or motion within a state space, but rather motion within a global response space.
4 Nov. 2003	Agrawal, Rati	Block Turbo Codes (BTC) using Graphical Models	Application of Graphical Models (e.g.: Bayesian Networks) to Error Control Coding (ECC) methods, specifically to Block Turbo Codes
7 Nov. 2006	Pokharel, Puskal P.	Least Mean Square Learning in a Reproducing Kernel Hilbert Space	Talk by Weifeng Liu, Graduate student at the CNEL.
16 Nov. 2005	Ramani, Meena	COMPUTATIONAL MODELING OF KNEE MECHANICS	A talk by: Dr. B.J. Fregly Department of Mechanical & Aerospace Engineering, Department of Biomedical Engineering, and Department of Orthopaedics and Rehabilitation, University of Florida
18 Nov. 2003	Agrawal, Rati	Graphical Models: Bayesian Networks	Brief Overview of Graphical Models (continuation of seminar1)
19 Nov. 2003	Ozturk, Mustafa Can	Modelling and Detection of Limit Cycle Oscillations in Thin-Wing Aircraft Using Adaptable Linear Models	A method for modeling the flutter response of a thin winged aircraft is presented. Flutter modeling traditionally relies on complicated mathematical approximations or elaborate adaptive systems. In order to simplify the task, this work proposes a two-part adaptable hybrid physical model. The first part models the structure from its known physical properties. The output from the first part is fed into an adaptive model that provides the necessary signal changes to reflect the forces encountered by the wing during flight. The result is an accurate synthesis of flight test data for a period of time where conditions remain nearly constant. In order to cope with changing conditions during flight, test data is segmented with statistical methods using numerical estimates of the error distributions. Models are trained for each segment. The overall sequence of models provides a complete synthesis of the nonlinear flutter response as flight conditions change.
20 Nov. 2002	Lazaro, Marcelino	Blind Equalization by Sampled PDF fitting	This is a new blind equalization technique for multilevel modulations. The proposed approach consists of fitting the probability density function (pdf) of the corresponding modulation at a set of specific points. The symbols of the modulation, along with the requirement of unity gain, determine these sampling points. The underlying pdf at the equalizer output is estimated by means of the Parzen window method. A soft transition between blind and decision directed equalization is possible by using an adaptive strategy for the kernel size of the Parzen window method. The method can be implemented using a stochastic gradient descent approach, which facilitates an on-line implementation. The proposed method has been compared with CMA and Benveniste-Goursat methods in QAM modulations.
20 Nov. 2003	Holz, Thomas	Implementing the Silicon Cortex	CNEL Day poster. Spiking Freeman model.
28 Nov. 2006	Pokharel, Puskal P.	An Integrated Multichannel Neural Recording System With Spike Outputs	Talk by Yuan Li, Graduate student at the CNEL.
30 Nov. 2005	Ramani, Meena	A coronary circulation model for the Human Patient Simulator	A lecture by: Dr. Johannes H. van Oostrom Associate Professor, College of Medicine – Anesthesiology and Biomedical Engineering
1 Dec. 2004	Skowronski, Mark	CNEL Seminar	Statistical automatic identification of microchiroptera from echolocation calls: Lessons learned from human automatic speech recognition
7 Dec. 2002	Erdogmus, Deniz	Minimax ICA	In this presentation, we have seen how Jaynes’ maximum entropy could be applied to ICA and blind deconvolution/equalization.
13 Oct. 2004	Venkatesan, Ravi C.	Knowledge Discovery Through a Fisher Game	Extreme Physical Information (EPI) is a self contained theory to elicit physical laws from a system/process (Nature) based on a measurement-response framework. A specific form of the Fisher information measure (FIM) known as the Fisher channel capacity (FCC) is employed as a measure of uncertainty. The FCC is the trace of the FIM. EPI may be construed as being a zero-sum-game between a gedanken observer and a system under observation (characterized by a demon, reminiscent of the Maxwell demon, residing in a conjugate space). The payoff of the competitive game results in a variational principle that defines the physical law that generates the observations made by the gedanken observer, as a consequence of the response of the system to the measurements. A principled formulation for reconstructing pdf’s from arbitrary discrete time independent random sequences based on an invariance preserving extension of the Extreme Physical Information (EPI) theory, is presented. Invariances are incorporated into the invariant EPI (IEPI) model through a Discrete Variational Complex inspired by the seminal work of T. D. Lee. A quantum mechanical connotation is provided to the Fisher game. This is accomplished through the IEPI Euler-Lagrange equation that acquires the form of a time independent Schrödinger-like equation, and, the quantum mechanical virial theorem. The concomitant constraints of the IEPI variational principle are consistent with the Heisenberg uncertainty principle. The ansatz’ describing the state estimators are obtained so as to selfconsistently satisfy an analog of the Fisher game corollary. The game corollary permits the demon to make the closing move in the Fisher game, by minimizing the FCC. This corresponds to a state of maximum uncertainty, and, is in keeping with the demon’s strategy of minimizing the information made available to the observer. A fundamental tenet of the EPI/IEPI model is the collection of statistically independent data by the observer. A principled IEPI Fisher game formulation guaranteeing the statistical independence of the quantum mechanical observables is presented, utilizing statistical analyses commonly employed in Independent Component Analysis (ICA). Specifically, correlations are first eliminated using a whitening process (facilitated by a linear filter, or PCA), in conjunction with Givens rotation (a unitary transform). Next, the IEPI Fisher game is played between the gedanken observer and the process inhabiting the conjugate system space. Finally, an inverse whitening filter is applied to the observables corresponding to the reconstructed state vectors obtained from the Fisher game. This yields a novel form of ICA based on minimizing the FCC. The prospect of obtaining an optimal whitening filter based on the Fisher game corollary is investigated into. Qualitative analogies and distinctions between the Fisher game ICA model and other prominent ICA theories are briefly discussed. Reconstruction of time independent random sequences generated from Gaussian mixture models demonstrates the efficacy of the Fisher game/ICA formulation.
10 Nov. 2004	Ignacio Santamaria	Merging temporal and pdf information: from kernel matched	In this talk we will present some recent work that exploits the link between kernel-based methods and ITL concepts. We start by solving the matched filtering problem in the feature space. To this end we apply kernel Fisher discriminant analysis (K-FDA). The interesting point is that the obtained test statistic uses time-domain information (through a set of projectors from the feature space to a lower dimensional space), as well as the pdf or statistical information about the problem. In this way, the structure of the K-FDA solution gives us some insight on how to obtain an autocorrelation function in the feature space, which is a much more ambitious goal. A very simplistic kernel autocorrelation function is presented and its performance is illustrated with some simulation examples.
23 Feb. 2005	Bashirullah, Rizwan	Wireless Interface Electronics for Implantable BMI devices	Wireless power transfer and bi-directional data links are often required in biomedical implants used for long term neural recording/stimulating systems. This talk will provide an overview of key factors to achieve high data rate transmission and internal power supply regulation for implant electronics that are limited by size, power and operational range. System level specifications, architectural considerations and circuit topologies specific to brain machine interface devices will be presented.
9 Feb. 2005	Boykin, P. Oscar	Filter Based Encoding Model of Retinal Ganglion Cells	Using uniform field input, we model mouse retinal ganglion cells as linear filters with a stochastic rate code to encode the filtered value. For each cell, the genetic algorithm is used to find the linear filter of the input which has the optimal mutual information with the output of the cell. Based on the shape of the filters (the encoding time course of the neurons), 3 broad classes of cells: FAST-ON, SLOW- ON, and OFF (as well as smaller sub-clusters) are identified.
20 Mar. 2002	Nechyba, Michael C.	Towards Autonomous Flight for Micro Air Vehicles (MAVs)	Substantial progress has been made recently towards designing, building and flying Micro Air Vehicles (MAVs), nominally defined by DARPA to have a maximum dimension no greater than six inches. Virtually undetectable from the ground, MAV systems can serve as remote sensors for a number of important applications, including surveillance, chemical-agent detection and target tracking; such applications have taken on special significance in light of the recent terror attacks and the consequent war on terrorism. Thus far, however, progress in overcoming the aerodynamic obstacles to flight at very small scales has not been matched by similar progress in equipping MAVs with autonomous flight capabilities. Practical considerations, such as limited weight, power and payload capacity, do not permit the same approach as on larger Unmanned Air Vehicles (UAVs). To address this challenge, we have developed a vision-guided flight stability and control system, and are moving towards full flight autonomy with on-board GPS navigation. In this talk, I will first give an overview of MAV (and small UAV) research at the University of Florida. Then, I will describe a real-time, vision-based horizon detection and tracking algorithm which lies at the core of our flight stability and control system, and will show video of recent self-stabilized, semi-autonomous MAV flights. Finally, I will conclude with a discussion of our current work in integrating on-board GPS for navigation, and future research directions, including the deployment of MAVs on Unmanned Ground Vehicles for remote sensing, and multiple-MAV deployment for coordinated flight missions.
9 Mar. 2005	Keil, Andreas	Spatio-temporal brain dynamics in early affective perception: clues from time-frequency analyses	Perception and evaluation of emotionally arousing scenes is essential for the organization and regulation of an organism’s behavior. Recent work in the field of the cognitive neuroscience of emotions has provided a dimensional framework for emotional perception being based on the two dimensions of valence (appetitive versus defensive/aversive system) and arousal (amount of activation in either system or degree of co-activation of both systems). This work is based on network approaches to the organization of emotional perception and memory. Affective modulation of visual processing, for instance, may be effected by afferent projections to visual cortices, resulting in a facilitation of the neural tissue in cases where perceptual content is emotionally arousing. While interactions and overlaps between emotional arousal systems and attentional systems have recently been studied extensively, the dynamics of acquisition of affective dispositions remain largely unclear. The present talk presents a series of experiments using large-scale high-frequency and low-frequency oscillations as measured using Electro- and Magnetoencephalography, examining the time course of learning aversive reactions to conditioned stimuli. Using measures of within-site and between-site phase-locking, the functional organization of various subsystems was examined that act to bind together the activity of the distributed areas and finally establish emotional erception. Both induced and evoked (driven) oscillations showed time-dependent sensitivity to classical conditioning, with phase-locking and amplitude being increased as a function of (i) duration and (ii) motivational significance of a given stimulus.
17 Apr. 2002	Srivastav, Rahul	Using an Auditory Model to Predict Voice Quality	Description and quantification of voice quality is important in many applications, particularly in the assessment and treatment of patients with voice disorders. Several objective measures, based either on the physiological correlates of voice production or on the vocal acoustic signal have been proposed for this purpose. However, these measures often fail to predict subjective ratings of voice quality. Using an auditory model to simulate peripheral encoding of vocal signals appears to provide a better method to understand the acoustic-perceptual relationships for voice quality and can help develop objective measures of voice quality that correspond well with subjective judgments.
21 Apr. 2004	Todorovic, Sinisa	Graphical Models and Their Application in Computer Vision	In recent years, it has become increasingly evident that researchers, concerned with systems that function under uncertainty, seek solutions within the probabilistic graphical-model framework. A graphical model represents a graph, whose nodes are characterized with random variables, and whose joint distributions are defined as a product over functions specified on connected subsets of nodes. The graph formalism provides general algorithms for computing marginal and conditional probabilities of interest, as well as control over computational complexity. A review of the literature offers two broad classes of models -- namely descriptive and generative models, which differ in structural complexity and difficulty of inference. We will present and overview of these models and illustrate their advantages and shortcomings on examples related to the object-recognition problem in computer vision.

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