We develop theories and working principles for neural
models and synaptic learning. Our interest involve
both autonomous systems with self-sustained neural
activity and self-organized locomotion.
Robots are normally hard controlled with the aim
to perform predefined movements. The same holds
for genetically encoded locomotion. Alternatively
one may consider motion to be generated by self-organizing
principles within the sensory-motor loop.
We use simulated robots to study how locomotion can
be generated through short-term synaptic plasticity and
other adaption processes, like poly-homeostasis. One finds
that complex and reactive motion patterns may emergy,
with the actual motion pattern realized being dependent
on the initial conditions.
transient state dynamics
One of the aims of complex system theory is to generate
and examine the state of dynamical systems, which may
be chaotic, laminar or synchronized. We have studied
dynamical principles, such as attractor relict networks,
leading to transient state dynamics. Transient states of
activity correspond, as observed in the brain, to
semi-stable alternating attractors, which are thought to
be at the foundationa of neural decision processes.
Cognitive system theory
Cognitive systems may be biological or artificial intelligences (AI).
One of the long-term goals is to formulate generating principles
allowing to construct modular information processing cognitive
systems which are, as far as possible, self-organizing.
guided self organization
It is mandatory, for the development of complex cognitive
systems, to develop principles on how to guide the
self-organization using overriding principles. For this
pupose we imploy generating functionals for the derivation of
the evolution equations and examine the competition between
conflicting objective functions. In particular we investigate
generating functionals based on the principle of
homeostatic optimization; the system tries to achieve a
time-average distribution of local activities as close
as possible to a given target distribution determined
through information theoretical principles.
diffusive emotional control
Our brain is a cognitive system, an adapting information
processing dynamical system having a task - to keep its
support unit, the body, alive. The brain has many different
working modi and these dynamical states are regulated
by an elaborated diffusive control mechanism, the emotional
control. It operates diffusively, influences large areas
of downstream neurons. Emotional control does not influence
directly cognitive information processing, but regulates
the adaption of slow parameters, a mechanism denoted
meta-learning within dynamical systems theory. We study models
and implications of diffusive control mechanisms in general.
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