The Microgenetic Dynamics of Cortical Attractor Landscapes

1. The Microgenetic Dynamics of Cortical Attractor Landscapes Mark H. Bickhard Lehigh University mark@bickhard.name http://bickhard.ws/

2. Abstract • Attractor landscapes are dispositional models of neural processes, but those landscapes themselves have a dynamics. I will outline how such landscapes are ongoingly created and modified, and how primitive representation emerges from these processes.

3. Context:The Broader Model • Ontological Emergence • Conceptual barriers from Pre-Socratics • Hume • Kim • Emergence of Normativity • Also ancient problems • Biological function • Representation

4. Representation • Cognition and Representation emerge in interaction systems • Self-maintenant systems • Recursively self-maintenant systems • Selection of interaction = presupposition of appropriateness; anticipation of appropriateness • ‘Appropriateness’ is normative • Derives from underlying model of normative function • Yields truth value — representation

5. Pragmatism • An interaction based, pragmatic, model of representation • Kinship to Piaget • More complex representations • Objects • Abstractions: e.g., numbers

6. Interaction Requires Timing • Successful interaction requires timing coordination • This is coordinative, neither too fast nor too slow • Turing machines cannot handle timing • Computers have central clocks • Not plausible for the brain

7. Timing Requires Oscillators • Solution: Put clocks everywhere • But clocks are “just” oscillators • Functional relationships are relationships among oscillators: modulations • Trivially at least TM powerful • Need a tool kit of different forms and scales of modulation • Modulations of modulations … of oscillatory activity

8. And This is What We Find • Neurons are standardly modeled as: • Threshold switches • Connectionist nodes • Frequency encoders • All have in common the assumption that neurons are ‘just’ input processors • And that neurons are the only functional units

9. Both Are Wrong • Neurons and neural circuits are endogenously active • In multiple ways • They do not just process inputs • And neurons are not the only functional units • Glia, for example, are also functional, not just supportive

10. NeuronsAnd local circuits • Oscillators • Resonators • Multiple interesting implications • Modulations of endogenous activity, not switches of otherwise inert units

11. Neurons II • Silent neurons • Interneurons • Short connections • Volume transmitters • L-Dopa • Graded release of transmitters • Gap junctions • Why multiple transmitters if all synapses are classical? • Transmitters evolved from hormones • Classical synapses evolved from volume transmitters

12. Astrocytes (Glia) • Receive transmitters • Emit transmitters • Form functional “bubbles” • Gap junction connections • Calcium waves • Modulate synaptogenesis • Modulate synaptic functioning • Release, uptake, degree of volume diffusion, …

13. Confirmation of Implication of Model of Representation • So, we do find a rich toolbox of multiple scales of modulatory relations

14. Now In Reverse • CNS functioning implies anticipatory cognition

15. Multiple Scales • These are all modulatory influences at multiple scales • Large and small spatial scales • Slow and fast temporal scales • There are also variations in delay times • Evolution has created a large tool box of multiple kinds and scales of modulatory influences

16. Microgenesis:Large Temporal Scale • Larger and slower processes set the context for smaller and faster processes • They set the parameters for the faster and smaller processes • Ion and transmitter concentrations • Modes of synaptic functioning • They generate vast concurrent micro-(and meso-) modes of processing across the brain: Microgenesis

17. Dynamic Programming • Parameter setting for dynamic processes is the dynamic equivalent of programming in a discrete system • Microgenesis sets and changes the programs across the brain • Microgenesis is ongoing and occurs in real time

18. Functional Anticipation • Microgenetic set-up may or may not be appropriate to the actual flow of interactive processing that occurs in the organism • Microgenesis is functionally anticipatory • The anticipation is that the microgenetic set-up will be appropriate

19. Emergence of Truth Value • Microgenetic anticipations can be true or false • And can be functionally determined to be false if the interaction violates anticipations • This is the emergence of representational truth value out of pragmatic functional success and failure

20. Content • Microgenetic anticipations will be true in some environmental conditions, and false in others • Microgenetic anticipations, then, presuppose that the appropriate conditions — whatever they are — obtain in the current environment. • The flow of anticipated conditions is implicit in the flow of microgenesis • Those conditions constitute the content of the representing • An implicit content

21. How Does This Differ? • Endogenously active • Interaction based, not input processing • Future oriented, not past oriented “spectator” model (Dewey) • Inherently modal: anticipations of interaction possibilities, not foundationally built on encoding correspondences with actual particulars • Implicit, thus unbounded, not explicit • Frame problems • Etc.

22. Two Way Implication • So, analysis of representation yields a required substrate of multi-scale modulatory, interactive brain processes • And an oscillatory/modulatory tool kit is precisely what we find • And, analysis of how the brain functions yields an anticipatory, interactive model of representation • Each implies the other

23. Microgenesis:Larger Spatial Scale —Attractor Landscapes • The slower scale processes engage in microgenetic programming of faster processes • The larger scale of these processes — astrocytes, volume transmitters, short range connections, reciprocal connections with thalamus, etc. — induces weak coupling among oscillatory processes • Such weak coupling induces attractor landscapes • Within which faster processes proceed

24. Modulation of Attractor Landscapes • Modulation of microgenesis, therefore, modulates attractor landscapes • \ Modulation of slower, larger scale process — astrocytes, etc. — modulates attractor landscapes • Provides a new framework for interpreting functionality of prefrontal - basal ganglia - thalamus - cortex loops • As engaged in modulation of attractor landscapes

25. Thought • These loops generate a kind of internal interaction with the dynamic spaces within which other CNS processes take place • This fits well with Pragmatic/Piagetian conception of thought as internal (inter)action

26. Further Issues • Other models of representation • Millikan • Dretske • Fodor • Cummins • Encodingism

27. Further Issues II • Other phenomena of mind • Perception • Memory • Motivation • Learning • Emotions • Reflective consciousness • Language • Rationality • Social ontology • Personality, psychopathology • Ethics

28. Conclusion • In being intrinsically interactive, representation and cognition are inherently: • Future oriented, anticipative • Pragmatic • Modal • Situated • Embodied • …

29. Conclusion II • And they are realized in: • Internal interactive modulations of • Attractor landscapes for • Oscillatory/ modulatory control of • Interactions of organism with environment

30. Fini

31. What’s Wrong with Standard Models of Representation? • Encodingism • Error, system detectable error — radical skeptical argument • Which correspondence? • Copy argument — Piaget • Externally related content: regress of interpreters • Partial recognition of problems: empty symbol problem, grounding problem

32. What’s Wrong With Standard Models? II • Millikan • Representation as function • Etiological function is causally epiphenomena • Dretske • Etiological function again, learning history rather than evolutionary history • Fodor • Asymmetrically dependent counterfactual relations • Counter example of crank molecule

33. What’s Wrong With Standard Models? III • Error • From observer perspective • Millikan OK • Dretske OK • Fodor Sort of OK • System detectable error • Content is not system accessible for any of these models • Comparing content with what is supposed to be being represented to determine truth or error is representational problem all over again • They are circular with respect to this criterion

34. What’s Wrong With Standard Models? IV • Symbol system hypothesis • Transduced encoding • Connectionism • Trained encoding

35. What’s Wrong With Standard Models? V • Dynamic systems • The interactive model is clearly a dynamic, process model • Dynamic approaches, however, are often anti-representational • E.g., Van Gelder, Thelen

36. Dynamic Systems Approaches • But, dynamic systems as agents must select interactions, •  must functionally indicate interaction potentialities, •  must yield representational truth value •  must involve normative representation, whether that terminology is used or not • Criticisms of representation are in fact criticisms of encodingist approaches to representation

37. Encodingism • Encodings do exist • But they borrow content • E.g., Morse code • They cannot generate emergent content • Serious problem for learning • E.g., Fodor’s innatism • Encodingism assumes that all representation is of encoding form • Encodingism does not work

38. Further Issues • Contemporary work pervasively assumes encodingism: • Perception • Rationality • Language • Memory • Learning • Emotions • Consciousness • …

39. Conclusion I • Representation is interactive, future oriented, pragmatic, non-encoding, modal, situated, embodied, and so on.

40. Conclusion II • These force multiple further changes: • Perception • Language • Memory • Motivation • Learning • Models of Brain Processes • And so on

41. Conclusion III • A major reworking of our models of and approaches to the whole person is required • The Whole Person