Otto-von-Guericke-Universität Magdeburg

 
 
 
 
 
 
 
 

Simulation and application of neurobiological networks and design of biologically inspired self-organizing systems

In this area, bioinformatics and some of the biologi-
cal background are investigated computationally within
the field of neuroscience. The established researching
points of interest in Artifical Neural Networks and Im-
age Processing are concerned in cooperation with neu-
robiology with the understanding and applications of
biologically plausible neural networks. As in the con-
nectivity structures of early mammalian brains, the ac-
tivation patterns and macroscopic phenomena develop-
ing thereby and their meaning for information process-
ing of cognitive behaviour at an operational point of
view are considered. For instance, biochemical pro-
cesses of systemic changes in the brain, produced by
biological learning, which are auto-adaptive and self-
organizing systemic descriptions of the impact of chan-
ges on cellular level and connectivity on the systemic
behaviour, are abstracted as controlling operations for
applications, as associative memories from spiking neu-
rons and self-organizing decentralized adaptive systems,
which are to be contemplated.
By computational simulated phenomena of structural
emergence and dynamic information overlay in the cor-
tex, biologically detailed simulations are used to inves-
tigate universal mechanisms of stability and regulariza-
tion principles of nervous systems, and are brought to
transfer neural behaviour of massive data streams in
real brains towards numerical paradigms of data pro-
cessing in machine operations and applications for real-
world-problem-solving. Detailed biologically plausible
simulations, which are undertaken to observe behaviour
and validate biological hypothesises from the neuro-
biological detailed point of view, and their complex-
ity of modelling is accompanied by an exponential in-
crease of the requirements of the numeric processing
capabilities, so the systems are abstracted and simpli-
fied by high level parameters. (S. Handrich
-491, A. Herzog +49 391 4090767)

 

 

Letzte Änderung: 08.11.2017 - Ansprechpartner: Dipl.-Ing. Arno Krüger
 
 
 
 
image_pose
Video: Head pose and orientation
 
 
 
 
v01_winglets
Video: Particle Tracking
 
 
 
 
Johanniskirche_1_300x240
Video: Multi-object tracking
 
 
 
 
Test_584
Video: Gestures and intention
 
 
 
 
Kinect-pose-ayoub1-logo
Video: Pose and Face detection using Kinect
 
 
 
 
kinect_pose
Video: HCI Face Attention
 
 
 
 
mimik-flow1
Video: Static and dynamic features
 
 
 
 
Motionblobs-gut
Video: Trisectional Multi-object tracking
 
 
 
 
s8
Video: Ephestia Parasitization