Plenary Speakers
Speaker 1: Machine learning and brain science, Kenji Doya, Neural Computation Unit, Okinawa Institute of Science and Technology(OIST)
Speaker 2: Modelling basic perceptual functions, Andrew P. Paplinski, Monash University, Australia
Title: Machine learning and brain science
Speaker: Kenji Doya, Neural Computation Unit, Okinawa Institute of
Science and Technology(OIST)
Abstract: Machine learning research has evolved in interaction with
brain science research in a variety of ways. For example, the
discovery of feature detectors in the visual system motivated the
design of the Perceptrons, and in return, the theory of unsupervised
learning gave account of how such feature detectors can emerge by
capturing the statistics of visual environment. The hierarchical
organization of the visual cortex motivated the design of deep
convolutional neural networks, which now achieves superb performance
in machine vision.
In this lecture, I will review such dynamic interactions between
machine learning and brain science and report our own brain science
research motivated by reinforcement learning theory. Topics include
action value coding in the basal ganglia and
javascript:__doPostBack('lnkSend$_ctl1','')the regulation of temporal
discounting by serotonin.
Biography: KENJI DOYA took BS in 1984, MS in 1986, and Ph.D. in 1991
at U. Tokyo. He became a research associate at U. Tokyo in 1986, U. C.
San Diego in 1991, and Salk Institute in 1993. He joined ATR in 1994
and became the head of Computational Neurobiology Department, ATR
Computational Neuroscience Laboratories in 2003. In 2004, he was
appointed as the principal investigator of Neural Computation Unit,
Okinawa Institute of Science and Technology (OIST) and started Okinawa
Computational Neuroscience Course (OCNC) as the chief organizer. As
OIST re-established itself as a graduate university in 2011, he became
a professor and the vice provost for research. He serves as the
co-editor in chief of Neural Networks from 2008. He is interested in
understanding the functions of basal ganglia and neuromodulators based
on the theory of reinforcement learning. Contact: doya@oist.jp, 1919-1
Tancha, Onna, Okinawa 904-0495, Japan.
Title: Modelling basic perceptual functions
Speaker: Andrew P. Paplinski, Monash University, Australia
Abstract: Perception describes the way in which our brain interprets sensory information and creates the representation of the environment.
We present a system that can integrate visual and auditory information and bind it to the internal mental concepts.
The basic module of the system, loosely identified with a cortical area of the brain, consists of stochastically fixed number of neuronal units per perceptual object, and maps the higher dimensionality afferent signals into a lower dimensionality “neuronal code”.
A typical perceptual system consist of three hierarchical layers of such modules, namely, the sensory layer, the unimodal association layer and the top, multimodal association module holding the representation of the collected knowledge.
We will demonstrate three versions of such a system that:
- binds concepts to spoken names,
- binds written words to mental objects,
- integrates visual and auditory stimuli.
Biography: Andrew P. Paplinski received his M.Eng. and Ph.D. degrees from the Faculty of Electronic Engineering, Warsaw University of Technology, Poland.
After moving to Australia, Andrew worked at the Department of Computer Science, Australian National University in Canberra, the Department of Electrical and Electronic Engineering, University of Adelaide, and finally at School of IT at Monash University where he is Associate Professor.
Since 2012 Andrew has been teaching in the newly formed Southeast University-Monash University Joint Graduate School in Suzhou, China
Andrew visited and collaborated with Kings College London, University of Oregon, University of New Mexico, University of Illinois at Urbana-Champaign, Nanyang Technological University, Technical University of Denmark and Lulea Technological University, Sweden.
His research activities evolved from designing computer hardware, through theory of control systems, signal and image processing, ultrasonic imaging into current involvement in computer vision and computational neuroscience and intelligence.
