1. The Microgenetic Dynamics of Cortical Attractor Landscapes Mark H. Bickhard Lehigh University mark@bickhard.namemark@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. Neurons And 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