[Comp-neuro] Gatsby Unit Quinquennial Symposium: 22nd March 2010

Peter Dayan dayan at gatsby.ucl.ac.uk
Mon Jan 18 15:17:19 CET 2010

		   Gatsby Unit Quinquennial Symposium
 		  10.30am-6:00pm  Monday 22 March 2010

We are delighted to announce the 2010 Gatsby Unit Quinquennial Seminar, 
with talks by distinguished researchers in theoretical neuroscience and 
machine learning.

The symposium will start at 10:30am on Monday 22nd March in the basement
Lecture Theatre, 33 Queen Square, London WCIN 3BG

All are welcome. Lunch and tea will be provided. 


           asstadmin at gatsby.ucl.ac.uk 
before 15 March 2010


10:30-11:30 Daniel Wolpert
	    Department of Engineering, University of Cambridge

	    Probabilistic models of sensorimotor control and decision making

	    The effortless ease with which humans move our arms, our
	    eyes, even our lips when we speak masks the true complexity
	    of the control processes involved. This is evident when we
	    try to build machines to perform human control tasks. While
	    computers can now beat grandmasters at chess, no computer
	    can yet control a robot to manipulate a chess piece with the
	    dexterity of a six-year-old child. I will review our recent
	    work on how the humans learn to make skilled movements
	    covering probabilistic models of learning, including
	    Bayesian and structural learning, as well as decision making
	    and the revision of decisions in the face of uncertainty.

11:30-12:30 Israel Nelken                                                            
	    Dept. of Neurobiology and the ICNC, Hebrew University

	    The representation of surprise in the auditory system

	    Neurons in auditory cortex show high sensitivity to rare
	    sounds, a phenomenon often called stimulus-specific
	    adaptation (SSA). I will describe our attempts to find out
	    what do the neurons really respond to, and to what extent
	    SSA can be understood in terms of the simplest possible
	    model, consisting of adaptation in narrow frequency
	    channels.  Finally, I will discuss some recent experiments
	    in which we tested the sensitivity of neurons to features of
	    the sound sequence that go beyond the rarity of the rare
	    event, suggesting that neurons in auditory cortex are
	    sensitive to higher-order regularities of the stimulus

12:30-14:30 Lunch and posters                                                        

14:30-15:30 John Hertz                                                               
	    Niels Bohr Institute, Copenhagen, and NORDITA, Stockholm

	    The Inverse Ising Model: Why and How

	    Ising models form a natural framework for modeling the
	    distribution of multi-neuron spike patterns: Of all models
	    that correctly describe the firing rates and pairwise firing
	    correlations, the Ising model is the one of maximum entropy.

	    The problem at hand here is an inverse one to that we
	    usually encounter.  Normally, one has a model with given
	    couplings (Jij) and the task is to compute averages and
	    correlation functions of the variables of the model.  Here
	    we are given the averages and correlations and the task is
	    to find the couplings.
	    In the simplest approach to this problem, one considers only
	    the measured firing rates and equal-time pairwise firing
	    correlations and tries to find the Ising model that has
	    these statistics.  In our work we have explored and compared
	    a number of methods for doing this, using data from a
	    realistic model network of spiking neurons.  Several of
	    these methods work remarkably well.
	    This success is tempered, however, by our second set of
	    findings.  Using an information-theoretic measure of the
	    overall quality of fit, we find that, while the Ising model
	    is a good description of the distribution of spike patterns
	    for small populations of neurons (~ 10), it does worse and
	    worse for larger and larger populations (for reasons that
	    are not yet understood).
	    Finally, I will describe some recent work, which extends the
	    Ising approach to describe non-equal-time firing

14:30-15:30 Yair Weiss                                                               
	    School of Computer Science and Engineering, 
	    The Hebrew University of Jerusalem 

	    Learning and inference in low-level vision

	    Low level vision addresses the issues of labeling and
	    organizing image pixels according to scene related
	    properties - such as motion, contrast, depth and
	    reflectance. I will describe our attempts to understand
	    low-level vision in humans and machines as optimal inference
	    given the statistics of the world. In particular, I will
	    show how message passing algorithms allow us to solve
	    real-world instances of NP-hard problems and to efficiently
	    learn energy functions despite an exponential number of
16:30-17:00 tea

17:00-18:00 Marty Banks 
	    Visual Space Perception Laboratory, UC Berkeley, USA

	    Perceptual Bases for Rules of Thumb in Photography

	    Photographers utilize many rules of thumb for creating
	    natural-looking pictures. The explanations for these
	    guidelines are vague and probably incorrect. I will explore
	    two common photographic rules and argue that they are
	    understandable from a consideration of the perceptual
	    mechanisms involved and peoples' viewing habits.
	    The first rule of thumb concerns the lens focal length
	    required to produce pictures that are not spatially
	    distorted. Photography textbooks recommend choosing a focal
	    length that is ~3/2 the film width. The textbooks state
	    vaguely that the rule creates a field of view that
	    corresponds to that of normal vision" (Giancoli, 2000), "the
	    same perspective as the human eye" (Alesse, 1989), or
	    "approximates the impression human vision gives" (London et
	    al., 2005). There are two phenomena related to this
	    rule. One is perceived spatial distortions in wide-angle
	    (short focal length) pictures. I will argue that the
	    perceived distortions are caused by the perceptual
	    mechanisms people employ to take into account oblique
	    viewing positions. I will present some demonstrations that
	    validate this explanation. The second phenomenon is
	    perceived depth in pictures taken with different focal
	    lengths. The textbooks argue that pictures taken with short
	    focal lengths expand perceived depth and those taken with
	    long focal lengths compress it. I will argue that these
	    effects are due to a combination of the viewing geometry and
	    the way people typically look at pictures. I will present
	    demonstrations to validate this.
	    The second rule of thumb concerns the camera aperture and
	    depth-of-field blur. Photography textbooks do not describe a
	    quantitative rule and treat the magnitude of depth-of-field
	    blur as arbitrary. I will examine the geometry of apertures,
	    lenses, and image formation. From that analysis, I will
	    argue that there is a natural relationship between
	    depth-of-field blur and the 3D layout of the photographed
	    scene. I will present demonstrations that human viewers are
	    sensitive to this relationship. In particular, depicted
	    scenes are perceived differently depending on the
	    relationship between blur and 3D layout.

           asstadmin at gatsby.ucl.ac.uk 
before 15 March 2010


More information about the Comp-neuro mailing list