Complexity Systems Simulation Seminar Series (CS^4) Event

Time:: 15:00 - 18:00
Date:: 7 November 2012
Venue:: Highfield Campus, Building 53 room 4025a/b, University of Southampton.

For more information regarding this event, please telephone Alison Simmance on +44 (0) 23 8059 3244 or email A.Simmance@southampton.ac.uk .

Event details

You are warmly invited to attend the Complex Systems Simulation Seminar Series (CS^4) 2012/13.

CS4: Complexity Systems Simulation Seminar Series- Daniel Polani University of Hertfordshire 7 November

The third seminar of the CS4: Complexity Systems Simulation Seminar Series 2012/13 will take place next Wednesday 7 ^th November. Dr Daniel Polani from the University of Hertfordshire will be providing a talk on ‘Informational principles in the perception-action loop'. 4-5pm, B53/4025, Highfield Campus.

All talks within the CS4 seminar series are free and take place fortnightly on Wednesdays, 4-5pm, B53/4025, Highfield Campus. All are warmly welcome to attend. Please find details of the Autumn Semester Schedule here: http://cs4southampton.wordpress.com/

No registration is required. Please contact Alison Simmance ( a.simmance@soton.ac.uk ) for all queries.

Complex Systems Simulation Seminar Series no 3

Abstract:

‘Informational principles in the perception-action loop'

Dr Daniel Polani

7th November, 4-5pm, B53/4025, Highfield Campus

Ashby's Law of Requisite Variety (1956) and, in last years, especially its later rediscovery and extension by Touchette and Lloyd (2000, 2004) have indicated that Shannon information acts as fundamental "currency" constraining the potential organisation and "administration" of cognitive tasks. In particular, there is increasing evidence that decision processes in biological organisms in fact exploit the limits implied by aforementioned work, and can therefore be subject to analysis with respect to information-theoretical optimality principles.

Under this hypothesis, many aspects of biologically plausible cognitive processing can be treated informationally, requiring only high-level constraints without having to specify detailed mechanisms.
This gives rise to novel tools not only for high-level analysis of biological cognitive systems, but also for purposes of prediction and construction of biologically plausible artificial cognitive models.

The talk will give an introduction into the question and methodology and demonstrate its operation with a number of examples.