Phd studentship available in computational neuroscience and consciousness

Large-Scale Computational Models of Thalamocortical Systems Underlying Consciousness.

Funding is available (for UK/EU applicants) for a three-year full-time D.Phil. (Ph.D) in the area of computational neuroscience and consciousness, supervised by Dr. Anil Seth and Prof. Owen Holland. The successful candidate will develop a large-scale computational model of human thalamocortical systems using Graphics/ /Processor Unit (GPU) technology, and will use the model to investigate the neuronal consequences of simple psychophysical manipulations. The position will suit a candidate highly qualified in software engineering with a strong interest, and preferably prior training, in computational neuroscience and consciousness science. Sussex has one of the highest concentrations of expertise in consciousness science in the world, and this research area is growing rapidly within the University. 

For more information and for how to apply please visit http://www.jobs.ac.uk/job/AAN013/research-studentship/. Interested candidates should contact Dr. Seth (a.k.seth at sussex.ac.uk).  Closing date for applications is Feb 20, 2010.

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Anil K. Seth, D.Phil.
Reader, EPSRC Leadership Fellow
School of Informatics, University of Sussex, Brighton, BN1 9QJ, UK
W: www.anilseth.com, T: (0)1273 678549

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Details for the Chair in Cognitive Robotics http://www.download.bham.ac.uk/vacancies/jd/38193.pdf

Details for the Chair in Computational Neuroscience http://www.download.bham.ac.uk/vacancies/jd/38192.pdf

School of Computer Science, University of Birmingham www.cs.bham.ac.uk/~nah || +44 121 414 3739 || skype: nickhawes

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Pentti Haikonen comments invited by Conscious-Robots.com editor on the recent article about Haikonen's Architecture for Conscious Machines written by Trung Doan.

I wish to thank Trung Doan for his analysis of my approach towards machine consciousness here. Trung Doan has done a lot of work here as he illustrates the main principles with practical examples, which are worked out along the principles that I present in my book "Robot Brains".

I would like to add some comments. My realization relies on associative neurons, which form associative memories. An associative memory is a rather old invention, but it has not gained much popularity because of the so-called interference problem, which limits the capacity of the memory. My contribution relates to the interference problem and in my book I describe methods, which allow the interference-free use of the full capacity; the capacity of the associative memory can be the same as the capacity of similar complexity random access memory. However, if only partial capacity is used, then the associative memory also performs the act of classification.

Trung Doan notes correctly that the neural machinery does not operate with numeric values, instead the individual signals represent elementary features of sensed entities and these are the basic meaning of these signals. It is useful to note that in the machinery groups of signals that represent some sensed entity may be used to stand for completely different things; these signal groups act as symbols for these things. This is a necessary prerequisite for e.g. natural language and inner speech.

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Second Issue of the International Journal of Machine Consciousness Available

The second issue of the International Journal of Machine Consciousness is available online (Vol. 1. Issue 2. December 2009)! The second issue of IJMC is a collection of selected papers from the 2008 Nokia Workshop on Machine Consciousness; Guest editor: P.O.A. Haikonen.

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Pentti Haikonen's architecture for conscious machines

By Trung Doan (doanviettrung a_t gmail dot com). 

Haikonen's contribution to the machine-consciousness endeavor is an architecture based on cognitive principles. He also developed some electronic microchips as a first step to building a machine based on that architecture.

Below, we look at how a Haikonen machine might achieve consciousness once built, by examining some of its cognitive capabilities, and in the process will briefly discuss the Haikonen architecture.

The Haikonen machine perceives

Say the Haikonen machine's cameras are  focusing on a yellow ball. The cameras' pixel pattern is fed into a preprocessor circuit which produces an array of, say, 10,000 signals, each signal carried by, for example, a wire. One wire is the output from the preprocessor's "roundness" circuitry and, in this case, the signal is On. Another wire, from the "squareness" circuitry, would be Off, i.e. carrying no voltage. A group of wires is the output from the spectrum-analysis circuitry, the wire corresponding to frequencies which we humans recognise as "yellow" is On while "red", "blue", etc., wires are Off. There would be many other groups of wires depicting size, brightness, edges, etc.

The machine does not internally represent the ball as a round graphic, nor a set of numbers representing diameter, color, etc., but by this signal array. Haikonen calls this a "distributed signal representation".

Suppose the machine is shown several balls of different sizes, colors, etc., one at a time, and each time its microphone hears the sound pattern we humans understand as the word "ball". Because they appear at the same time repeatedly, the machine associates the sound pattern and the visual pattern together. The making of associations is how the machine's perception is done.

After several different balls are associated with that sound pattern, the machine finally learns to associate the "ball" sound pattern with anything that is round.

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