---------------------------------------------------------------------- DYNAMICS IN NETWORKS OF SPIKING NEURONS http://diwww.epfl.ch/w3lami/team/gerstner/NIPS_works.html Organizer: W. Gerstner (Lausanne, Switzerland) Networks of spiking neurons have several interesting dynamic properties, for example very rapid and characteristic transients, synchronous firing and asynchronous states. A better understanding of typical phenomena has important implications for problems associated with neuronal coding (spikes or rates). For example, the population activity is a rate-type quantity, but does not need temporal averaging - which suggests fast rate coding as a potential strategy. The idea of the workshop is to start from mathematical models of network dynamics, see what is known in terms of results, and then try to find out what the implications for 'coding' in the most general sense could be. ---------------------------------------------------------------------- POPULATION CODING Organizers: Glen D. Brown, The Salk Institute Kechen Zhang, The Salk Institute We will explore experimental approaches to population coding in three parts. First, we will examine techniques for recording from populations of neurons including electrode arrays and optical methods. Next, we will discuss spike-sorting and other issues in data analysis. Finally, we will examine strategies for interpreting population data, including population recordings from the hippocampus. To facilitate discussion, we are establishing a data base of neuronal-population recordings that will be available for analysis and interpretation. For more information, please contact Glen Brown (glen@salk.edu) or Kechen Zhang (zhang@salk.edu) Computational Neurobiology Laboratory The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla, CA 92037 ---------------------------------------------------------------------- TEMPORAL CODING: IS THERE EVIDENCE FOR IT AND WHAT IS ITS FUNCTION? http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/hatsopoulos.html Organizers: Nicho Hatsopoulos and Harel Shouval Brown University Departments of Neuroscience and Physics One of the most fundamental issues in neuroscience concerns the exact nature of neural coding or representation. The standard view is that information is represented in the firing rates of single or populations of neurons. Recently, a growing body of research has provided evidence for coding strategies based on more precise temporal relationships among spikes. These are some of the questions that the workshop intends to address: 1. What do we mean by temporal coding? What time resolution constitutes a temporal code? 2. What evidence is there for temporal coding in the nervous system. 3. What functional role does it play? What computational problem can it solve that firing rate cannot? 4. Is it feasible to implement given the properties of neurons and their interactions? We intend to organize it as a debate with formal presentations and informal discussion with some of the major figures in the field. Different views regarding this subject will be presented. We will invite speakers doing work in a variety of areas including both vertebrate and invertebrate systems. ---------------------------------------------------------------------- OPTICAL IMAGING OF THE VISUAL CORTEX http://camelot.mssm.edu/~udi Organizers: Ehud Kaplan, Gary Blasdel It is clear that any attempt to model brain function or development will require access to data about the spatio-temporal distribution of activity in the brain. Optical imaging of the brain provides a unique opportunity to obtain such maps, and thus is essential for scientists who are interested in theoretical approaches to neuroscience. In addition, contact of biologists with theoretical approaches could help them focus their studies on the essential theoretical questions, and on new computation, mathematical, or theoretical tools and techniques. We therefore organized a 6-hour workshop on optical imaging of the cortex, to deal with both technical issues and physiological results. The workshop will have the format of a mini-symposium, and will be chaired by Ehud Kaplan (Mt. Sinai School of Medicine) and Gary Blasdel (Harvard). Technical issues to be discussed include: 1. What is the best way to extract faint images from the noisy data? 2. How does one compare/relate functional maps? 3. What is the best wavelength for reflectance measurements? 4. What is the needed (or possible) spatial resolution? 5. How do you deal with brain movement and other artifacts? See also: http://camelot.mssm.edu/~udi ---------------------------------------------------------------------- OLFACTORY CODING: MYTHS, MODELS AND DATA http://www.wjh.harvard.edu/~linster/nips98.html Organizers: Christane Linster, Frank Grasso and Wayne Getz Currently, two main models of olfactory coding are competing with each other: (1) the selective receptor, labeled line model whish has been popularized by recent results from molecular biology, and (2), the non-selective receptor, distributive coding model, supported mainly by data from electrophysiology and imaging in the olfactory bulbs. In this workshop, we will discuss experimental evidence for each model. Theorticians and experimentalists together will discuss the implications for olfactory codoing and for neural porprocessing in the olfactory bulb and cortex for each of the two predominant, and possibly, intermediate, models. ---------------------------------------------------------------------- STATISTICAL THEORIES OF CORTICAL FUNCTION http://www.cnl.salk.edu/~rao/workshop.html Organizers: Rajesh P.N. Rao, Salk Institute (rao@salk.edu) Bruno A. Olshausen, UC Davis (bruno@redwood.ucdavis.edu) Michael S. Lewicki, Salk Institute (lewicki@salk.edu) Participants are invited to attend a post-NIPS workshop on theories of cortical function based on well-defined statistical principles such as maximum likelihood and Bayesian estimation. Topics that are expected to be addressed include: statistical interpretations of the function of lateral and cortico-cortical feedback connections, theories of perception and neural representations in the cortex, and development of cortical receptive field properties from natural signals. For further details, see: http://www.cnl.salk.edu/~rao/workshop.html ---------------------------------------------------------------------- LEARNING FROM AMBIGUOUS AND COMPLEX EXAMPLES Organizers: Oded Maron, PHZ Capital Partners Thomas Dietterich, Oregon State University Frameworks such as supervised learning, unsupervised learning, and reinforcement learning have many established algorithms and theoretical tools to analyze them. However, there are many learning problems that do not fall into any of these established frameworks. Specifically, situations where the examples are ambiguously labeled or cannot be simply represented as a feature vector tend to be difficult for these frameworks. This workshop will bring together researchers who are interested in learning from ambiguous and complex examples. The workshop will include, but not be limited to, discussions of Multiple-Instance Learning, TDNN, bounded inconsistency, and other frameworks for learning in unusual situations. ---------------------------------------------------------------------- TURNKEY ALGORITHMS FOR IMPROVING GENERALIZERS http://ic.arc.nasa.gov/ic/people/kagan/nips98.html Organizers: Kagan Tumer and David Wolpert NASA Ames Research Center Abstract: Methods for improving generalizers, such as stacking, bagging, boosting and error correcting output codes (ECOCs) have recently been receiving a lot of attention. We call such techniques "turnkey" techniques. This reflects the fact that they were designed to improve the generalization ability of generic learning algorithms, without detailed knowledge about the inner workings of those learners. Whether one particular turnkey technique is, in general, "better" than all others, and if so under what circumstances, is a hotly debated issue. Furthermore, it isn't clear whether it is meaningful to ask that question without specific prior assumptions (e.g., specific domain knowledge). This workshop aims at investigating these issues, building a solid understanding of how and when turnkey techniques help generalization ability, and lay out a road map to where the turnkey methods should go. ---------------------------------------------------------------------- MINING MASSIVE DATABASES: SCALABLE ALGORITHMS FOR DATA MINING http://research.microsoft.com/~fayyad/nips98/ Organizers: Usama Fayyad and Padhraic Smyth With the explosive growth in the number of "data owners", interest in scalable, integrated, data mining tools is reaching new heights. This 1-day workshop aims at bringing together researchers and practitioners from several communities to address topics of mutual interest (and misunderstanding) such as: scaling clustering and prediction to large databases, robust algorithms for high dimensions, mathmatical approaches to mining massive datasets, anytime algorithms, and dealing with discrete, mixed, and multimedia (unstructured) data. The invited talks will be used to drive discussion around the issues raised, common problems, and definitions of research problems that need to be addressed. Important questions include: why the need for integration with databases? why deal with massive data stores? What are most effective ways to scale algorithms? How do we help unsophisticated users visualize the data/models extracted? Contact information: Usama Fayyad (Microsoft Research), Fayyad@microsoft.com, http://research.microsoft.com/~fayyad Padhraic Smyth (U.C. Irvine), Smyth@sifnos.ics.uci.edu, http://www.ics.uci.edu/~smyth/ ---------------------------------------------------------------------- INTEGRATING SUPERVISED AND UNSUPERVISED LEARNING www.cs.cmu.edu/~mccallum/supunsup Organizers: Rich Caruana, Just Research Virginia de Sa, UCSF Andrew McCallum, Just Research Michael Kearns, AT&T Labs This workshop will debate the relationship between supervised and unsupervised learning. The discussion will run the gamut from examining the view that supervised learning can be performed by unsupervised learning of the joint distribution between the inputs and targets, to discussion of how natural learning systems do supervised learning without explicit labels, to the presentation of practical methods of combining supervised and unsupervised learning by using unsupervised clustering or unlabelled data to augment a labelled corpus. The debate should be fun because some attendees believe supervised learning has clear advantages, while others believe unsupervised learning is the only game worth playing in the long run. More information (including a call for abstracts) can be found at www.cs.cmu.edu/~mccallum/supunsup. ---------------------------------------------------------------------- LEARNING ON RELATIONAL DATA REPRESENTATIONS http://ni.cs.tu-berlin.de/nips98/ Organizers: Thore Graepel, TU Berlin, Germany Ralf Herbrich, TU Berlin, Germany Klaus Obermayer, TU Berlin, Germany Symbolic (structured) data representations such as strings, graphs or logical expressions often provide a more natural basis for learning than vector space representations which are the standard paradigm in connectionism. Symbolic representations are currently subject to an intensive discussion (cf. the recent postings on the connectionist mailing list), which focuses on the question if connectionist models can adequately process symbolic input data. One way of dealing with structured data is to characterize them in relation to each other. To this end a set of data items can be characterized by defining a dissimilarity or distance measure on pairs of data items and to provide learning algorithms with a dissimilarity matrix of a set of training data. Prior knowledge about the data at hand can be incorporated explicitly in the definition of the dissimilarity measure. One can even go as far as trying to learn a distance measure appropriate for the task at hand. This procedure may provide a bridge between the vector space and the "structural" approaches to pattern recognition and should thus be of interest to people from both communities. Additionally, pairwise and other non-vectorial input data occur frequently in empirical sciences and pose new problems for supervised and unsupervised learning techniques. More information can be found at http://ni.cs.tu-berlin.de/nips98/ ------------------------------------------------------------------ SEQUENTIAL INFERENCE AND LEARNING http://svr-www.eng.cam.ac.uk/~jfgf/workshop.html Organizers: Mahesan Niranjan, Cambridge University Engineering Department Arnaud Doucet, Cambridge University Engineering Department Nando de Freitas, Cambridge University Engineering Department Sequential techniques are important in many applications of neural networks involving real-time signal processing, where data arrival is inherently sequential. Furthermore, one might wish to adopt a sequential training strategy to deal with non-stationarity in signals, so that information from the recent past is lent more credence than information from the distant past. Sequential methods also allow us to efficiently compute important model diagnostic tools such as the one-step-ahead prediction densities. The advent of cheap and massive computational power has stimulated many recent advances in this field, including dynamic graphical models, Expectation-Maximisation (EM) inference and learning for dynamical models, dynamic Kalman mixture models and sequential Monte Carlo sampling methods. More importantly, such methods are being applied to a large number of interesting real problems such as computer vision, econometrics, medical prognosis, tracking, communications, blind deconvolution, statistical diagnosis, automatic control and neural network training. _______________________________________________________________________________ ABSTRACTION AND HIERARCHY IN REINFORCEMENT LEARNING http://www-anw.cs.umass.edu/~dprecup/call_for_participation.html Organizers: Tom Dietterich, Oregon State University Leslie Kaelbling, Brown University Ron Parr, Stanford University Doina Precup, University of Massachusetts, Amherst When making everyday decisions, people are able to foresee the consequences of their possible courses of action at multiple levels of abstraction. Recent research in reinforcement learning (RL) has focused on the way in which knowledge about abstract actions and abstract representations can be incorporated into the framework of Markov Decision Processes (MDPs). Several theoretical results and applications suggest that these methods can improve significantly the scalability of reinforcement learning systems by accelerating learning and by promoting sharing and re-use of learned subtasks. This workshop aims to address the following issues in this area: - Task formulation and automated task creation - The degree and complexity of action models - The integration of different abstraction methods - Hidden state issues - Utility and computational efficiency considerations - Multi-layer abstractions - Temporally extended perception - The design of autonomous agents based on hierarchical RL architectures We are looking for volunteers to lead discussions and participate in panels. We will also accept some technical papers for presentations. For more details, please check out the workshop page: http://www-anw.cs.umass.edu/~dprecup/call_for_participation.html ---------------------------------------------------------------------- MOVEMENT PRIMITIVES: BUILDING BLOCKS FOR LEARNING MOTOR CONTROL http://www-slab.usc.edu/events/nips98 Organizers: Stefan Schaal (USC/ERATO(JST)) and Steve DeWeerth (GaTech) Traditionally, learning control has been dominated by representations that generate low level actions in response to some measured state information. The learning of appropriate trajectory plans or control policies is usually based on optimization approaches and reinforcement learning. It is well known that these methods do not scale well to high dimensional control problems, that they are computationally very expensive, that they are not particularly robust to unforeseen perturbations in the environment, and that it is hard to re-use these representations for related movement tasks. In order to make progress towards a better understanding of biology and to create movement systems that can automatically build new representations, it seems to be necessary to develop a framework of how to control and to learn control with movement primitives. This workshop will bring together neuroscientists, roboticists, engineers, and mathematicians to explore how to approach the topic of movement primitives in a principled way. Topics of the workshop include the questions such as: what are appropriate movement primitives, how are primitives learned, how can primitives be inserted into control loops, how are primitives sequenced, how are primitives combined to form new primitives, how is sensory information used to modulate primitives, how primitives primed for a particular task, etc. These topics will be addressed from a hybrid perspective combining biological and artificial movement systems. ---------------------------------------------------------------------- LARGE MARGIN CLASSIFIERS http://svm.first.gmd.de/nips98/ Organizers: Alex J. Smola, Peter Bartlett, Bernhard Schoelkopf, Dale Schuurmans Many pattern classifiers are represented as thresholded real-valued functions, eg: sigmoid neural networks, support vector machines, voting classifiers, and Bayesian schemes. Recent theoretical and experimental results show that such learning algorithms frequently produce classifiers with large margins---where the margin is the amount by which the classifier's prediction is to the correct side of threshold. This has led to the important discovery that there is a connection between large margins and good generalization performance: classifiers that achieve large margins on given training data also tend to perform well on future test data. This workshop aims to provide an overview of recent developments in large margin classifiers (ranging from theoretical results to applications), to explore connections with other methods, and to identify directions for future research. The workshop will consist of four sessions over two days: - Mathematical Programming - Support Vector and Kernel Methods, - Voting Methods (Boosting, Bagging, Arcing, etc), and - Connections with Other Topics (including an organized panel discussion) Further details can be found at http://svm.first.gmd.de/nips98/ ---------------------------------------------------------------------- DEVELOPMENT AND MATURATION IN NATURAL AND ARTIFICIAL STRUCTURES http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/haith.html Organizers: Gary Haith, Computational Sciences, NASA Ames Research Center Jeff Elman, Cognitive Science, UCSD Silvano Colombano, Computational Sciences, NASA Ames Research Center Marshall Haith, Developmental Psychology, University of Denver We believe that an ongoing collaboration between computational work and developmental work could help unravel some of the most difficult issues in each domain. Computational work can address dynamic, hierarchical developmental processes that have been relatively intractable to traditional developmental analysis, and developmental principles and theory can generate insight into the process of building and modeling complex and adaptive computational structures. In hopes of bringing developmental processes and analysis into the neural modeling mainstream, this session will focus developmental modelers and theorists on the task of constructing a set of working questions, issues and approaches. The session will hopefully include researchers studying developmental phenomena across all levels of scale and analysis, with the aim of highlighting both system-specific and general features of development. For more information, contact: Gary Haith, Computational Sciences, NASA Ames Research Center phone #: (650) 604-3049 FAX #: (650) 604-3594 E-mail: haith@ptolemy.arc.nasa.gov Mail: NASA Ames Research Center Mail Stop 269-3 Mountain View, CA 94035-1000 ---------------------------------------------------------------------- HYBRID NEURAL SYMBOLIC INTEGRATION http://osiris.sunderland.ac.uk/~cs0stw/wermter/workshops/nips-workshop.html Organizers: Stefan Wermter, University of Sunderland, UK Ron Sun, University of Alabama, USA In the past it was very controversial whether neural or symbolic approaches alone will be sufficient to provide a general framework for intelligent processing. The motivation for the integration of symbolic and neural models of cognition and intelligent behavior comes from many different sources. From the perspective of cognitive neuroscience, a symbolic interpretation of an artificial neural network architecture is desirable, since the brain has a neuronal structure and the capability to perform symbolic processing. From the perspective of knowledge-based processing, hybrid neural/symbolic representations are advantageous, since different mutually complementary properties can be integrated. However, neural representations show advantages for gradual analog plausibility, learning, robust fault-tolerant processing, and generalization to similar input. Areas of interest include: Integration of symbolic and neural techniques for language and speech processing, reasoning and inferencing, data mining, integration for vision, language, multimedia; combining fuzzy/neuro techniques in engineering; exploratory research in emergent symbolic behavior based on neural networks, interpretation and explanation of neural networks, knowledge extraction from neural networks, interacting knowledge representations, dynamic systems and recurrent networks, evolutionary techniques for cognitive tasks (language, reasoning, etc), autonomous learning systems for cognitive agents that utilize both neural and symbolic learning techniques. For more information please see http://osiris.sunderland.ac.uk/~cs0stw/wermter/workshops/nips-workshop.html Workshop contact person: Professor Stefan Wermter Research Chair in Intelligent Systems University of Sunderland School of Computing & Information Systems St Peters Way Sunderland SR6 0DD United Kingdom phone: +44 191 515 3279 fax: +44 191 515 2781 email: stefan.wermter@sunderland.ac.uk http://osiris.sunderland.ac.uk/~cs0stw/ ---------------------------------------------------------------------- SIMPLE INFERENCE HEURISTICS VS. COMPLEX DECISION MACHINES http://www.cs.cmu.edu/Groups/NIPS/1998/Workshop-CFParticipation/todd.html Organizers: Peter M. Todd, Laura Martignon, Kathryn Blackmond Laskey Participants and presentations are invited for this post-NIPS workshop on the contrast in both psychology and machine learning between a probabilistically- defined view of rational decision making with its apparent demand for complex Bayesian models, and a more performance-based view of rationality built on the use of simple, fast and frugal decision heuristics. ---------------------------------------------------------------------- CONTINUOUS LEARNING http://www.forwiss.uni-erlangen.de/aknn/cont-learn/ Organizers: Peter Protzel, Lars Kindermann, Achim Lewandowski, and Michael Tagscherer FORWISS and Chemnitz University of Technology, Germany By continuous learning we mean that learning takes place all the time and is not interrupted, that there is no difference between periods of training and operation, and that learning AND operation start with the first pattern. In this workshop, we will especially focus on the approximation of non-linear, time-varying functions. The goal is modeling and adapting the model to follow the changes of the underlying process, not merely forecasting the next output. In order to facilitate the comparison of the various methods, we provide different benchmark data sets and participants are encouraged to discuss their results on these benchmarks during the workshop. Further information: http://www.forwiss.uni-erlangen.de/aknn/cont-learn/ ---------------------------------------------------------------------- LEARNING CHIPS AND NEUROBOTS http://bach.ece.jhu.edu/nips98 Organizers: Gert Cauwenberghs, Johns Hopkins University Ralph Etienne-Cummings, Johns Hopkins University Marwan Jabri, Sydney University This workshop aims at a better understanding of how different approaches to learning and sensorimotor control, including algorithms and hardware, from backgrounds in neuromorphic VLSI, robotics, neural nets, AI, genetic programming etc. can be combined to create more intelligent systems interacting with their environment. We encourage active participation, and welcome live demonstrations of systems. The panel has a representation over a wide range of disciplines. Machine learning approaches include: reinforcement learning, TD-lambda (or predictive hebbian learning), Q-learning, and classical as well as operand conditioning. VLSI implementations cover some of these, integrated on-chip, plus the sensory and motor interfaces. Evolutionary approaches cover genetic techniques, applied to populations of robots. Finally, we have designers of microrobots and walking robots on the panel. This list is by no means exhaustive! More information can be found at URL: http://bach.ece.jhu.edu/nips98 __________________________________________________________________________