Research & Reviews: Journal of Engineering and Technology
MenuISSN: 2319-9873
ISSN: 2319-9873
+44 7456 035580 Christian Huyck
Middlesex University, UK
Keynote: JET
Cell Assemblies (CAs) are crucial to human and mammalian
cognition and behaviour. A CA is a group of neurons that
can maintain firing without external stimulation. Our symbolic
concepts, like dog, are represented by CAs. Many non-human
mammals do not have symbols, but they do have concepts.
So, a rat will probably have a generic CA for cat, which will fire
when a cat is present.
There is an enormous gap in the academic communityâ??s
understanding of CAs, how they affect motor control, and
how they regulate sensing. Theoretically, my CA for walk to
the door is firing when I am walking to the door, but it is
not clear how that interacts with central pattern generators
(CPGs), or even if those neurons that execute the CPG are
part of the CA. Similarly, it is clear that, for instance, neurons
in the primary visual cortex are involved when a human
view a dog, but it is not entirely clear how they lead to the
ignition of the dog CA, or which neurons are in the dog CA.
As there's a gap, I, and my collaborators, are trying to fill
the gap. I am a computer scientist, so I am trying to develop
programs based on CAs. In particular, we think embodiment
is important, and that working from simulated neurons is
important. So, we work with robots, virtual and physical. We
work with spiking neurons, typically point models. We have
been developing neural topologies that can be used for virtual
agents. We are now working as part of the Human Brain
Project, developing topologies that can be reused by others
to implement agents. We have done a fair bit of work on
developing â??higherâ? function such as neuro-cognitive models
of natural language parsing and learning a two-choice task.
We have also done some work with physical robots. We
developed the neural software for a simple Braitenberg robot
that followed lines using vision; this was based on our CA
work. We are currently developing CA based neural models
for grasping control that are also neuro-cognitive models
of a stop task. More recently, we have been working on the
forward model for a fast walking robot. This work does not use
currently make use of CAs. Instead, it approximates standard
analytical models (like a cart and pole) with point neurons;
neurons are turing complete. The plan is to continue on with
this work. We can explore the use of CAs in virtual robots. It
is my contention that following this approach, mimicking the
human model as closely as possible, physically, neurally, and
psychologically is the best way to get to Turing test passing
AI. It also has the benefits of furthering our understanding of
human neural and psychological processing and developing
systems that are useful. These more useful systems include
robots.
Christian Huyck completed his PhD in 1994 from the University of Michigan. He is the professor of Artificial Intelligence at Middlesex University and has over 100 publications. He has been head of the AI research group at Middlesex for over 20 years. His two main areas of research are natural language processing and processing with simulated neurons.
E-mail: c.huyck@mdx.ac.uk