Complex ‘2009 Workshop on “Causality in Complex Systems” (Complex CCS) A Review-in-Progress by David Batten CSIRO, Australia Causality and Complexity in Adaptive Neural Systems

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 Walter 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

Linear Causality of the Observer Source: Walter Freeman (1999)

Linear Causality in Action A stimulus S n initiates a chain of events including Activation of receptors Transmission by serial synapses to cortex Integration with memory Selection of a motor pattern Descending transmission to motor neurons Activation of muscles At nodes along the chain, awareness occurs, and meaning and emotion are attached to the response Temporal sequencing is crucial; no effect can precede or occur simultaneously with its cause At some instant, each effect becomes a cause This conceptualization is inherently limited, because awareness cannot be defined at a point in time.

Circular Causality of the Self Source: Walter Freeman (1999) real time death

Circular Causality in Action The double dot shows a point moving counterclockwise on a trajectory idealized as a circle, showing that an event exists as a state through a period of inner time, which we reduce to a point in real time. Stimuli from the outside world impinge on this state. So also do stimuli arising from the self-organizing, interactive dynamics within the brain. Most stimuli are ineffective, but occasionally one does succeed as a "hit" on the brain state, and a response occurs. The impact and motor action are followed by a change in brain structure that begins a new orbit. So, changing our (state of) mind changes the neural structure of our brains

Circular Causality = Systemic Causality? A succession of orbits can be conceived as a cylinder with its axis in real time, extending from birth to death in an individual and its brain Trajectories in inner time may be viewed as fusing past and future into an extended present by way of state transitions Circular causality expresses the interrelations between levels in a hierarchy A top-down macroscopic state simultaneously influences its microscopic elements, and The microscopic elements create and sustain the macroscopic state from the bottom up The circular and hierarchical relationship between such microscopic and macroscopic entities is essential for explaining brains; also lasers (see Haken, 1983).

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 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

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 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. ANNs 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

Distinguishing Causal Interactions (Seth)

Granger Causality Clive Granger – Nobel prizewinner in economics for his work in econometrics on time-series analysis Granger causality is a method for determining whether one time series is useful in forecasting another Ordinarily, regressions reflect "mere" correlations, but Granger argued that there is an interpretation of a set of tests that can reveal something useful about causality. Statistical, not physical Causality can be unidirectional or reciprocal Many extensions to suit neurodynamics: e.g. Multivariate Granger causality e.g. Nonlinear Granger causality Granger causality interactions can be represented as a directed graph

Lakoff on Frames and Metaphors “Frames” are mental models of limited scope e.g. our traditional frame for war includes semantic roles like nations at war, leaders, armies with soldiers and commanders, weapons, attacks, battlefields, etc. Such frames + metaphors (e.g. “nerves of steel”) in our brain define our “common sense” Human thinking in frames and metaphors gives rise to inferences that don’t fit the laws of logic or deductive rationality as e.g. economists have formulated them Because facts matter, undistorted framing is needed to communicate the truth about our economic, social and political realities Differing worldviews or aspirations often lead to the proliferation of distorted frames and metaphors

Two Competing Worldviews There may be as many worldviews as human beings? In the social sciences, a few worldviews crop up time and again: Sheep and Explorers (in traffic) Imitators and Innovators (in technology) Cartesians and Stochasts (in fishing strategies) Conservatives and Progressives (in politics) They correspond to 2 extremes in terms of risk-taking behaviour or creativity Lakoff: 2 parenting models  2 worldviews Strict father model  Conservatives  Linear Causality Nurturant parent model  Progressives  Systemic Causality Many people retain active versions of both models in different parts of their brain, and use them in different parts of their lives

Conclusions for our Workshop series Causality and complexity have been discussed at length by scholars in the field of neuroscience especially linear versus circular circularity especially with respect to neural nets and causal networks Thus it could be worth focusing on neuroscience as a subtheme at one of our workshops At the forefront of causality discussions have been: Walter Freeman, UC Berkeley Hermann Haken, U of Stuttgart Anil Seth, U of Sussex Steve Bressler, Florida Atlantic U Several scholars at The Neurosciences Institute, San Diego Several others could be worth our attention: e.g. George Lakoff, UC Berkeley

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

Three Worldviews Individualism Reduce all social constructs to collections of individuals (micro, no emergence) “There is no such thing as society” – Thatcher Holism Structure dominates composition (macro, no emergence) “Any society does not consist of individuals but expresses the sum of relationships [and] conditions that the individual actor is forming” – Marx Systemism Model entities by composition, environment, structure and mechanism (micro and micro, emergence) “Systemism makes room for both agency and structure” – Bugne Source: Alex Ryan (2007)

What is a System? Interdisciplinary concept with 2 core influences: Emergence and Hierarchy (General Systems Theory) Communication and Control (Cybernetics)

Contemporary Systems Approaches