Causality and Complexity in Adaptive Neural Systems: A Review
in Progress
David Batten (CSIRO)
0900 – 1020 10 July 2009-07-10
Presentation with accompanying Powerpoint slideshow
Notes by Martin Burke
Attendees
Michel
Gina
Martin
Will
Vanja
Russ
Jimmy
Franzy
Cliff
Bob
1.Goal and Method
Explore and review concepts in brain research and cognition from perspective of
complex systems scientist making use of published papers and books.
2. What is Consciousness?
3. Linear Causality in Action
Stimulus initiates chain of events
Awareness occurs at nodes
Temporal sequencing is crucial
4. Circular causality of self
Double-dot shows represents an event as a state in both inner time and real time
Franzy: Feedback loops?
Michel?: Inner time and real time?
Cliff/David/Will: Brain/mind/thoughts alter brain structure
5. Circular causality = systemic causality?
John Finnigan has encouraged us to focus on examples
Cliff: These cycles are everywhere not just in the brain. Bernard cells. CAS
always based on such cycles.
Franzy: Many of the concepts in this area are themselves not well defined and/or
uncertain. Fascinating but about need to define new tools in order to promote
shared understanding.
Will: Ultimate example of emergence where “circuitry of neurons” generates
cognition, understanding. Any glimpse into this will be helpful.
Jimmy: Need to think about causality and causation differently. Pluralistic
understanding/multiple viewpoints/multiple resolutions are essential to progress
in practical situations. In some cases not even meaningful to talk causation.
David: Spectrum of context always present. 4-5 standard examples may be
useful in focusing are thinking and structuring our output to our funding sources.
Try to think towards what we are need to deliver.
Russ: Lots of work on causality seems to be attempts to force phenomena into
single framework.
Gina: This makes me think differently to how I did yesterday when looking at the
“cyclinder diagram”. Is there a difference between the topology as an energy
function and its structural features.
Cliff: Toplogy just a math concept that describes “connectedness” on entities.
Gina: Need to be careful that we know whether we are talking about the “object
system” and its conception and representation.
Circular causality express between interrelations between levels in a hierarchy.
Circular and hierarchical relationships.
6. Some of Freemans conclusions
Awareness: intentionality cannot be explained by linear causality
Serlf-organisation: circular causality in a selforgansing brain is a useful concept
in this concept.
Cliff: Prigogine and Bannerman
Franzy: “Hard problem of consciousness”?
David: Literature is not clear on this.
Russ: Some of the words used on this slide are not well defined/understood.
Will: This is where we get into the concept of “emergence”.
Franzy: We can deal with phenomena in the physical world but not in the “mind”.
Jimmie. Ontological/epistemological issue. Are we getting simple enough models
to explain what’s going on?
Will: how do we know that we are not just bullshitting ourselves?!
Jimmie: Models built up through combination of our studies and experiences.
How do we know that we are modelling well? How established are the notions?
How well do we understand the concepts labelled by these terms?
Gina: does this help us address our CIN agenda?
David: Perhaps too early to be able to answer this question.
Russ: Awareness is the key.
Gina: Can I be self-appointed moderator and say “can we get on with the talk”!
Russ: No I want to carry on with this point! Subjectivity. Colour blinded example.
New methods: similar pattern switching.
Russ: “Spread of academics”!
Cliff: Not necessarily generating any new mind!
7. New Method 1: S-O and Synergetics
Synergetics and self-organisation of brain function and cognition (Haker, Kelso,
Freeman, Lewis)
Concept of circular causality should be discarded (Bakker) since it suggests it
suggests interactions between entities that don’t really exist.
8. New Method 2: Attractor Neural Networks
Hopfield introduced general concept.
Associative memeory model based on formal neurons
9. New Methods 3: Causal Networks
Neurons engage inb causal interactions with one another
Neural systems can thus be analysed in terms of causal networks.
Neurobitotic model of hippocampus has been developed.
Causal networks may be useful in understanding characteristics of
Consciousness.
10. Distinguishing Causal Interactions (Seth)
Neural reference
Context network (by backtrace)
Causality analysis
Causal core
Etc
Seth will be attending next workshop.
11. Granger Causality
Granger is a Nobel laureate in ecometrics
Method for determining whether one time series is useful in forecasting another
Statistical not physical
Causality canbe unidirectional or reciprocal
Many extensions to suit neurodynamics
Becoming accepted as a promising method for the future.
Granger casual relationships can be represented as a directed graph.
12. Lakoff on Frames and Metaphors
Frames are mental models of limited scope.
Frames and metaphors define our common sense.
Thinking in frames and metaphors give rise to inferences that don’t fit the laws of
logic or deductive rationality.
Jimmie: “Truth” Influencing populations through storytelling to create biased
beliefs. An important recognition.
Martin: Lakoff argues that metaohors give succinct descriptions of systemic
properties and emotional connotations. (Metaphors We Live By, Lakoff and
Johnson)
13. Two Competing Worldviews
As many worldviews as humans. Some crop up repeatedly in the social sciences:
Sheep and explorers
Imitators and innovators
Cartesians and stochasts
Conservatives and progressives
They correspond to 2 extremes in risk-taking
Lakoff: 2 parenting models -> 2 worldviews
Does Lakoff really have evidence?
14. Conclusions for our Workshop Series
Causality and complexity have been discussed at length in neuroscience field
Thus possible focus as a sub-theme in future workshops and deliverables
Thought leaders:
Freeman
Haken
Bressler
Seth
Neuroscience Institute (San Diego)
Lakoff
Tough area. Do they have any methodologies that we could make use of?
Jimmie: DARPA doing multi-trillion node brain modelling. May not be taking
evolutionary design approach. Can look into on our behalf.
15. Thank You.
Discussion (1020 Cliff: Thousands of brain modellers out there. Check with Dave Green at Wogga
if we need better info.
Cliff: Social vs ABM. Someone must be thinking of this but who? Search on
keywords: Protein computing; neural axons.
Russ: AAAI 2009
Symposia each Spring and Fall. This year in Arlington.
Russ: Wiki
cs.calstatela.edu