Presentation on Integrated Information Theory of Consciousness, part of the Complex Systems Seminar, at Chalmers University of Technology

1. Consciousness as Integrated Information
Complex Systems Seminar|SHAMIT BAGCHI |05-Nov-2014
Φ
"We cannot doubt of our existence while we doubt …”
René Descartes (1596 –1650)
MSc, Complex Adaptive Systems, Chalmers University of Technology

2. Communication between brain networks in people given psilocybin –psychedelic drug called magic mushrooms (right) or a non-psychedelic compound (left). Petri et al./Proceedings of the Royal Society Interface
A hyperconnectedbrain
A normal brain
Consciousness Alteration -Psychedelic Experiences
•Dots and colorscorrespond to connection-rich networks
•Greater communication across the whole brain after psilocybin (magic mushroom) intake
•Psychedelic experiences, of sensory mix-up: tasting colors, feeling sounds, seeing smells etc.

3. Agenda:
•Consciousness
•Here, There, Everywhere?
•Subjective Experience
•What is it?
•Where Subjective Experience arises: NCC
•Subjective States and Neural Correlates
•Integrated Information
•Integrated Information Theory (IIT)
•Ф, EI, Complexes and Qualia Space
•The Quale as Conscious Experience
•Quantity and Quality of Consciousness

4. Consciousness is Here
•Consciousness is everything we experience!
•The past two centuries of clinical and laboratory studies reveal an intimate relationship between the conscious mind and the brain
•The exact nature of this relationship remains elusive.

5. Conscious is There!
Less Conscious
Conscious too?

6. But Not Everywhere!
•Or is consciousness perhaps everywhere?
•Pervading the cosmos, as in an old Panpsychisttradition …

7. How can Subjective Experience be explained?
•Perhaps consciousness emerges when an organism is immersed in some complex sensorimotor loop including the environment
•Perhaps Consciousness may arise when one part of the brain, acting as the “subject” (front) looks upon another part as its “object” (the back) and evaluates or reflects upon its activity

8. However …
•Neurological evidence indicates that neither sensory inputs nor motor outputs are needed to generate consciousness.
•When watching an engrossing movie, we become so immersed that we may lose the sense of self, the inner voice.

9. Neural Correlates of Consciousness (NCC)
•The minimal neuronal mechanisms jointly sufficient for any one specific conscious percept
•Implies neural structures and neural activity patterns underlie consciousness.
•Phenomenology= Subjective Experiences

10. NCC for ‘Subjective States’
•Every phenomenal, subjective state will have an associated NCC.
•State N1 of system N is a neural correlate of phenomenal propertyP if N's being in N1 directly correlates with the subject experiencing P.

11. Neural activity patterns as NCC
•Sustained Activity
•Neural activity may contribute to consciousness only if it is sustainedfor a minimum period of time, perhaps around a few hundred ms.
•Re-entrant Activity
•Awareness of a stimulus happens because of the occurrence of a “reentrant” wave of activity from higher to lower cortical areas.
•Synchronization
•Consciousness may require the synchronization, at a fine temporal scale, of large populations of neuronsdistributed over many cortical areas

12. Two Main Aspects of Consciousness
•The Quantity or Levelof Consciousness
Conditions that determine to what extent a system has consciousness.
•The Quality or Contentof Consciousness
Conditions that determine what kind of consciousness a system has.
How redisyourred?

13. Integrated Information give Experiences their Subjective flavour
•When somebody smells chocolate the effect that it has on their brain is integrated across many aspects of their memory.
•An olfactory experience is not localised to any one part of the brain
•They are instead widely dispersed and inextricably intertwined with all the rest of the person’s memories.
•This unique integration of a stimulus with existing memories is what gives experiences their subjective (i.e. observer specific) flavour.
Is Consciousness Computable? Quantifying Integrated Information Using Algorithmic Information Theory, Phil Maguire, Philippe Moser, Department of Computer Science
NUI Maynooth, Ireland, Rebecca Maguire, School of Business, National College of Ireland, IFSC, Dublin 1, Ireland, Virgil Griffith, Computation and Neural Systems, Caltech, Pasadena, California

14. A Top-Down Approach works better!
•Starting from the brain how it could possibly give rise to experience, the problemmay indeed be ‘impossible’ to solve.
•Instead Start from consciousness→ identify its essential properties→ what kinds of physical mechanismsaccount for the properties

15. Integrated Information Theory (IIT)of Consciousness
•IIT starts from Phenomenologyand tries to give a Mathematical expressionto the fundamental properties of Experience.
•According to the theory, the most important property of consciousness is that it is extraordinarily informative.
•Classically, the reduction of uncertainty among a number of alternatives constitutesinformation.

16. Motivation Examples
•When a human identifies a smell as chocolate they are generating a response which distinguishes between 10,000 possible states, yielding 푙표푔210,000 = 13.3 bits of information. vs
•A photodiode enters one of its two possible alternative states –on
•A human facing it enters one out of an extraordinarily large number of possible states
•The photodiode's repertoire is minimally differentiated
Entropy measure:The average amount of information contained in each message drawn from a distribution or data stream. Entropy characterizes our uncertainty about our source of information.

17. Ф-Measure of integrated information
•A measure of integrated information called Ф, quantifying the reduction of uncertaintywhen a system enters a particular state through causal interactions among its parts
•The parts should be chosen in such a way that they can account for as much non-integrated (independent) information as possible.

18. How to measure Φ? –Take a System
•Consider a neural system.
•Each element could be a cortical minicolumn
•Imagine the system is disconnected from external inputs, like the brain is virtually disconnected from the environment when dreaming.

19. How to measure Φ? –Methodology
•Consider asubset S of elements + diagram of causal interactions among them…
•How to measure the information generated when S enters a particular state from causal interactions within the system?
•Divide S into two complementary parts A and B, and evaluate the responses of B that can be caused by all possible inputs from A.

20. Effective information (EI)
Measuring EI(A→B):
•Inject maximum entropy Hmaxinto the outgoing connections from A
•Measure the entropy of the states of B that is due to the input from A
EI for the bipartition:
EI(A B) = EI(A→B) + EI(B→A).

21. Minimum Information Bipartition (MIB)
•Subset S = {1,2,3,4}
•Horizontal bipartition {1,3}/{2,4} yields a positive value of EI
•Bipartition {1,2}/{3,4} yields EI = 0 (min information bipartition -MIB)
•Other bipartitions of subset S have EI > 0

22. Analysis of Complexes
•Consider all subsets of system X, identify its complexes and rank them by the respective values of Φ –value of EI for their MIB
•Assume other elements in X are disconnected, observe Φ > 0 for subset {3,4} and {1,2}
•Subsets {3,4} and {1,2} not part of a larger subset having higher Φ, and constitute complexes

23. Quantity of Consciousness
•The level of consciousness of a physical system is related to the repertoire of causal states available to the system as a whole
•The quantityof consciousness is determined by the capacity to integrate information, which can be measured as the Φ value of a complex of elements
•Φ is defined as the amount of causally effective informationthat can be exchanged across the minimum information bipartitionof a complex (its informational weakest link). A complex is a subset of elements with Φ > 0 and no inclusive subset of higher Φ.

24. Information Integration Architecture
a. Functional Specialization and Integration
•Heterogeneous arrangement of the incoming and outgoing connections:
•Each element is connected to a different subset of elements, with different weights.
b. No functional Specialization
•The same amount of connectivity, distributed homogeneously to eliminate functional specialization,
•Yields a complex with much lower values of Φ (Φ = 20 bits)
c. No functional Specialization or Integration
•Reduction of information integration through loss of integration

25. Brain –Comprising of Complexes
•The brain is likely to contain more than one complex, many small ones with low values, and perhaps a few large ones
•At any given time in the brain, there is a complex of comparatively much higher Ф, which we call the main complex Φ.
•The complex, and not its elements, is the locus of consciousness.

26. •All bipartitionsare indicated by listing one part (subset A) on the upper row and the complementary part (subset B) on the lower row.
•In between are values of effective information from A to B and from B to A for each bipartition.
Effective Information Matrix of a Complex
Complex 1
Complex 2

27. Qualia as Effective Information Matrix & Quality of Consciousness
•The effective information matrix defines the set of informational relationships, or "qualia space" for each complex.
•This relational space is sufficient to specify the quality of conscious experience.
•The qualia space specifies the probability of past and future states of the system.

28. State diagram for Complexes & ‘Quale’
•5 representative states of a complex, each represented by 4 elements
•Assume the 5 states evolve in time due to intrinsic dynamics of the system or due to inputs from the environment
•Last 4 columns:special states corresponding to activation of one element at a time, correspond most closely to the specific "quale" / quality contributed by that particular element in its corresponding complex.
Active Elements
Inactive Elements
Complex 1
Complex 2

29. Quale & Conscious Experience
•These highly selective states represent the closest approximation to experiencing that element's specific contribution to consciousness – its quality or "quale".
•A conscious experienceis specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.

30. Summarizing IIT
•Consciousness corresponds to a system’s capacity to integrate information.
•This is indicated by two key phenomenological properties of consciousness:
•Differentiation-the availability of a very large number of conscious experiences; and
•Integration-the unity of each such experience.

31. Summarizing IIT
•The quantityof consciousness is determined by the capacity to integrate information, measured as the Φ value of a complex of elements.
•Φ is the amount of causally effective informationthat can be exchanged across the minimum information bipartitionof a complex (its informational weakest link). A complex is a subset of elements with Φ > 0 and no inclusive subset of higher Φ.
•The qualityof consciousness is determined by the effective information matrix of a complex, which specifies all informational relationships among its elements.

32. References
•An information integration theory of consciousness
•Giulio Tononi*, Department of Psychiatry, University of Wisconsin, Madison, USA
•The Neural Correlates of Consciousness -An Update
•GIULIO TONONI, Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, USA
•CHRISTOF KOCH, Division of Biology and the Division of Engineering and Applied Science, California. Institute of Techn
•Is Consciousness Computable? Quantifying Integrated Information Using Algorithmic Information Theory
•Phil Maguire and Philippe Moser Department of Computer Science, NUI Maynooth, Ireland
•Rebecca Maguire, School of Business, National College of Ireland, IFSC, Dublin 1, Ireland
•Virgil Griffith, Computation and Neural Systems, Caltech, Pasadena, Californiaology, Pasadena, California, USA