Workshop on “Causality in Complex Systems”, ISC-PIF, Paris, November 2009 Sources: David Lagnado and Anil Seth Causality and Complexity in Adaptive Neural Systems

Question 8 – Changing our minds? When we change our mind about something, how does it change the physical structure of our brains? Could the mental process of “changing one’s mind” correspond to the physical process of switching between attractors in our brain? Is brain dynamics governed by an adaptive order parameter that finds expression in the maintenance of a global state of self-organized criticality? Would a complex systems perspective centred on self-organizing, attractor neural networks be helpful in ordering our thinking about how internal psychic and neurodynamic processes lead us to reason and behave in one way or another?

My Goal and Method To explore and review the concepts of causality and complexity in brain research and cognition From the perspective of a complex systems scientist only vaguely familiar with advances in neuroscience Making use of: Published papers and books in neuroscience and in related fields (e.g. psychology, psychophysiology, etc.) Special issues of leading journals (e.g. the 2006 special issue of the International Journal of Psychophysiology on the Quiet Revolutions in Neuroscience) Important Conferences (e.g. the Brain Network Dynamics Conference at UC Berkeley in honour of Walter Freeman’s 80th Birthday, 2007) In order to better understand, and perhaps eventually to better model, causal and influence networks that evolve within the human brain  human aspirations

What is Consciousness? According to Freeman, the pertinent questions are: How and in what senses does consciousness cause the functions of our brains and bodies? How do brain and body functions cause consciousness? How do actions cause perceptions? How do perceptions cause awareness? How do states of awareness cause actions? Analysis of causality is a necessary step towards a better comprehension of consciousness The types of answers depend on the choice among meanings that are assigned to the word “cause”: linear causality circular causality non-causal interrelationships

Some of Freeman’s Conclusions Awareness cannot be explained by linear causality Intentionality cannot be explained by linear causality Interactions between microscopic and macroscopic domains of the brain accord with the laws of self-organization Circular causality in a self-organizing brain is a concept that is useful to describe interactions between microscopic neurons in assemblies and the macroscopic emergent state variable that organizes them. New methods are needed to explain how all those neurons simultaneously get together in a virtual instant & switch from one harmonious pattern to another in an orderly dance! A surprisingly similar kind of pattern switching holds for: the excitation of atoms in a laser to produce light (Haken) the metamorphosis of caterpillars into butterflies the inflammatory spread of epidemics or behavioural fads

Combination of “New” Methods? Self-Organisation and Synergetics Attractor Neural Networks Causal Networks Something else?

New Method 1: S-O and Synergetics Synergetics and self-organization of brain function and cognition (Haken, Kelso, Freeman, Lewis) Circular causality describes bidirectional causation between different levels of a system (Haken, 1977). Maurice Merleau- Ponty introduced the concept, claiming that every action and every sensation is both a cause and an effect. Brain dynamics is governed by an adaptive order parameter that regulates everywhere neocortical mean neural firing rates at the microscopic level, finding expression in the maintenance of a global state of self-organized criticality (Freeman, 2004) The concept of circular causality should be discarded (Bakker) Circular causality suggests an interaction between separable entities that does not exist. The micro-macro relationship is one of correspondence or association rather than causation

New Method 2 – Attractor Neural Networks Hopfield introduced the general concept of an attractor neural network (ANN) In his 1982 paper on neural networks as physical systems with emergent computational abilities, he defined an associative memory model based on formal neurons  the first mathematical formalisation of Hebb’s ideas and proposals on the neural assembly, the learning rule, the role of connectivity in the assembly and the neural dynamics. Attractor neural networks are being used to confirm the hypothesis that a collective phenomenon is at the origin of our memory function (Amit and others). Important associated concepts are: Synaptic plasticity – based on Hebbian rules Continuous ANNs

New Method 3: Causal Networks Neurons engage in causal interactions with one another (self-organization) and with the surrounding body and environment (adaptation) Neural systems can thus be analyzed in terms of causal networks, without assumptions about info processing; e.g. using Granger causality & graph theory A neurobiotic model of the hippocampus & surrounding area identified shifting causal pathways during learning of a spatial navigation task: Selection of specific causal pathways – “causal cores” Causal network approach may help to characterise the complex neural dynamics underlying consciousness: Causal density as a candidate measure of neural complexity The Neurosciences Institute  Seth, Edelman, Tononi

Conclusions re our Workshop series Causality and complexity have been discussed at length by several scholars in the field of neuroscience especially linear versus circular circularity especially with respect to neural nets and causal networks At the forefront of causality discussions have been: Walter Freeman, UC Berkeley Hermann Haken, U of Stuttgart Steve Bressler, Florida Atlantic U – accepted Anil Seth, U of Sussex – accepted for Paris workshop Several scholars at The Neurosciences Institute (San Diego) George Lakoff, UC Berkeley All the above have been invited to join us

Thank you Dr. David Batten CSIRO, Australia Phone: Contact Us Phone: or Web: Thank you!