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Kechen Zhang speaking in IGSN Colloquium organized by INI

On Wednesday, 22nd of June 2016, Kechen Zhang from Johns Hopkins University, Baltimore, USA will give an IGSN Colloquium talk about "How large spaces are represented in hippocampus" in the FNO building  01/117 (the IGSN seminar room). Host is Laurenz Wiskott. INI members are welcome to attend.

Abstract of talk: Earlier experiments of hippocampal place cells in rats were confined to relatively
small chambers, which were smaller than the natural habitats of wild rats by at
least two orders of magnitude. More recent experiments in larger environments
show that a place cell typically has multiple, irregularly spaced place fields,
rather than a unique place field as in a small environment. Many computational
models, especially those with attractor maps embedded in a recurrent network,
are often based on the experimental data from small environments, and their
predictions of rigid correlations of place cell activities are incompatible with the
new data. Existing evidence indicates that place cells are recombined with near
maximal flexibility to represent a large space, presumably forming a cognitive
map with maximal representational capacity. A seamless large spatial map may
potentially emerge as a quasi-continuous attractor in a recurrent network by
unsupervised learning. There are several theoretical advantages of such a system,
including robustness against input noise, simultaneous representation of
conflicting cues, and easy integration of nonspatial information. These results
suggest that a flexible, combinatorial map might be a basic computational building
block of hippocampal function.


The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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