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Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA

The dynamics of decision-making by leech neurons Neurobiology of Decision-Making CSH 24 May 2005. Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA. We make all kinds of choices:. (respond or ignore) .

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Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA

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  1. The dynamics of decision-making by leech neurons Neurobiology of Decision-Making CSH 24 May 2005 Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA

  2. We make all kinds of choices: (respond or ignore) Choosing whether Choosing what (feed on it, fight it, love it) Choosing how (direct/indirect, strongly/subtly) Choosing when (now, later) Choosing which (regular/diet, large/medium, left/right)

  3. We make all kinds of choices: Choosing whether (respond or ignore) Choosing what (feed on it, fight it, love it) Choosing how (direct/indirect, strongly/subtly) Choosing when (now, later) Choosing which (regular/diet, large/medium, left/right)

  4. Leeches crawl in shallow water (3 mm)

  5. Leeches swim in deep water (33 mm)

  6. Anatomy of the leech CNS

  7. Leech preparations

  8. Elongation (E) Crawling Swimming Contraction (C) G10 C C C Dorsal G10 E E E Ventral G13 C C C 1 sec 10 sec Leech locomotory behaviors

  9. Swim initiation by a command neuron, cell 204 10 mV Cell 204 DE motor neuron 5 sec Weeks & Kristan, 1978

  10. Command neurons Command neurons Invertebrate model of choice: inhibition of command neurons

  11. Problem #1: some “command neurons” are activated in incompatible behaviors. Shaw & Kristan, 1997

  12. Semi-intact preparation Stimulate cell R3b1: Intracellular Extracellular Esch,Mesce & Kristan, 2002 Problem #2: Some “command neurons” are bifunctional Shallow water Deep water

  13. Decisions appear to be made interactively by multiplexed neurons using a combinatorial code

  14. Head brain Use isolated nerve cord Stimulate (S) one nerve electrically , record (R) from another Swimming Imaging Crawling R S G15 Tail brain Stimulus Briggman, Abarbanel & Kristan, 2005 Leech nervous system can swim or crawl to the same stimulus • same stimulation elicits different responses:

  15. Optical activity in motor neurons during swimming Data of Adam Taylor

  16. Swim Crawl dF/F (%) Cell number Cell number Time (msec) Time (msec) Membrane potential trajectories of 144 neurons Briggman, Abarbanel & Kristan, 2005

  17. Discrimination by single neurons p > 0.000001 p < 0.000001 Nerve DT Single cells with early DTs Swim Trials Crawl Trials Single cell DT Early Discriminating (ED) Non-discriminating (ND) Late Discriminating (LD) Transiently Discriminating (TD) Briggman, Abarbanel & Kristan, 2005

  18. PC1 PC2 PC3 Cell Number Principal component analysis, across neurons

  19. Discrimination of single cells vs. neuronal populations LDA DTs Earliest cell DTs Nerve DTs Single cell DTs { { Cells contributing to Linear Discriminant Single cells with early DTs

  20. Hyperpolarized Trials (-1.5 nA) Depolarized Trials (+1.5 nA) Polarizing cell 208 biases behavioral choice Intracellular Stimulation DP Nerve Stimulus Briggman, Abarbanel & Kristan, 2005

  21. Cell Active during Command 208 swimming “Do something!” shortening Leeches make behavioral choices sequentially R3b1 swimming “Locomote” crawling 204 swimming “Swim!” 28 swimming “Bend up (down)”

  22. Swim CPG Crawl CPG Decision making Stimulation and dynamically: z y x Rest state

  23. CONCLUSIONS • Many leech neurons take part in both swimming and crawling. • About half of them have different activity in the two behaviors. • Only a few differ early (good candidates for being decision-makers). • Decision-making may depend on dynamic interactions -- among multiplexed neurons -- using a combinatorial code.

  24. Henry Abarbanel Vertex Pharmaceuticals by Dyes provided (Aurora Biosciences ) (PanVera) Cast of characters KRISTAN LAB: COLLABORATORS: Brian Shaw David Kleinfeld Tim Cacciatore Roger Tsien Adam Taylor Tito Gonzalez Teresa Esch Peter Brodfuehrer Gary Cottrell Karen Mesce Briggman Kevin

  25. Linear Discriminant Analysis (LDA) Cell A LD Direction LD Direction c b a Cell B Cell C

  26. PC1 PC1 PC2 PC2 PC2 B A PC3 Cell Number PC1 Principal component analysis, by participating neurons: Briggman, Abarbanel & Kristan, 2005

  27. What’s ahead? Input to decision makers: - risks) x (benefit of A x p(success of A) p(choice A) = p(any behavior) x (positive - negative) modulation x sensory processing p(choice A) = response threshold • source of modulation site of action • effects of feeding • classical conditioning: bias to swim or crawl

  28. KRISTAN LAB: COLLABORATORS: Brian Shaw David Kleinfeld Cacciatore Tim Tsien Roger Adam Taylor Tito Gonzalez Teresa Esch Peter Brodfuehrer Gary Cottrell Karen Mesce Kevin Briggman Henry Abarbanel Vertex Pharmaceuticals (Aurora Biosciences (PanVera) Dyes provided ) Cast of characters by

  29. Discrimination by single neurons p > 0.000001 p < 0.000001 Nerve DT Swim Trials Crawl Trials Single cell DT (Data of Kevin Briggman) Early Discriminating (ED) Non-discriminating (ND) Late Discriminating (LD) Transiently Discriminating (TD)

  30. Discrimination by single neurons Single cells with early DTs

  31. Cell number dF/F (%) Time (msec) Swimming… in 144 neurons Data of Kevin Briggman

  32. Is leech decision-making hierarchical? Cell Stimulation type: produces: Active during: Command: A swimming swimming “Do something!” shortening B swimming or swimming “Get out of here!” crawling crawling C swimming swimming “Swim!” D bending swimming “Move this muscle group”

  33. Data of Brian Shaw Cell 204 is inhibited during shortening….

  34. Swim Shorten Record optically from 91 neurons simultaneously (Data of Kevin Briggman)

  35. Swim – Shorten Subtract no-swim from swim traces for each neuron Swim Shorten (Data of Kevin Briggman)

  36. Reasonable hypothesis: Decision-making neurons have different activity trajectories in different behaviors.

  37. Swimming Shapes of the first 3 components: PCA2 PCA1 Component amplitude PCA3 Crawling Time (Data of Kevin Briggman) Use principal component analysis (PCA) to detect neurons with different activity trajectories in different behaviors.

  38. Head brain G12 G15 Tail brain Response Variability (Data of Kevin Briggman)

  39. Swim Crawl (Data of Kevin Briggman) A few neurons have different trajectories in swimming and crawling:

  40. Another reasonable hypothesis: Decision-making neurons have the earliest differences in their trajectories.

  41. For assemblies of neurons, we plot their PCA components: With just 3 neurons, we could plot their activity on separate axes: 1st Frame Stimulus delivered 1st Swim Burst C3 PC 3 C1 C2 PC 2 PC 1 Use PCA analysis to follow the trajectories of neuronal classes over time.

  42. Do we make choices hierarchically? (sequential, linear) “Choice-makers” will be active in sequence …..or interactively? (feedback, resetting) “Choice-makers” activity will bounce back and forth …..or simultaneously? (nonlinearity, overlapping function) “Choice-makers” will all be active at the same time

  43. Neuronal circuits for whole-body shortening, swimming Mechanosensory neurons Trigger interneurons Gating interneurons Oscillator interneurons Motor neurons

  44. Shortening and swimming circuits overlap

  45. Measuring voltage changes with FRET dyes (FRET = fluorescence resonance energy transfer) (Developed by Tito Gonzalez & Roger Tsien)

  46. FRET VSD optical signals (Figure provided by Tim Cacciatore)

  47. Tr2 terminates swimming

  48. Swimming…. in 90 neurons Data of Kevin Briggman

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