1. Cholinergic Modulation of the Hippocampus Computational Models of Neural Systems Lecture 2.5 David S. Touretzky September, 2007

2. 11/22/2016 Computational Models of Neural Systems 2 A Theory of Hippocampus ● Suppose CA1 is a hetero- associator that learns: – to mimic EC patterns, and – to map CA3 patterns to learned EC patterns ● Imagine a partial/noisy pattern in EC triggering a partial/noisy response in CA3, cleaned up by auto-association in CA3 recurrent collaterals ● CA1 could use the EC response to call up the complete, correct EC pattern What happens if recall isn't turned off during learning?

3. 11/22/2016 Computational Models of Neural Systems 3 Acetylcholine Effects (1) Acetylcholine (ACh) has a variety of effects on HC: ● Suppresses synaptic transmission in CA1: – Mostly at Schaffer collaterals in stratum radiatum – Less so for perforant path input in stratum lacunosum-moleculare patch clamp recording

4. 11/22/2016 Computational Models of Neural Systems 4 Effect of Carbachol ● Carbachol is a cholinergic agonist. ● Can use carbachol to test the effects of ACh. ● It only activates metabotropic ACh receptors. ● Brain slice recording experiments show that carbachol suppresses synaptic transmission in CA1. 46.0% suppression90.7% suppression 54.6% suppression 87.6% suppression Experiment 1 Experiment 2

5. 11/22/2016 Computational Models of Neural Systems 5 Effect of Atropine ● Atropine affects muscarinic- type ACh receptors, not nicotinic type. ● Blocks the suppression of synaptic transmission by carbachol. ● Therefore, cholinergic suppression in s. rad. and s. l.-m. is by muscarinic ACh receptors. same

6. 11/22/2016 Computational Models of Neural Systems 6 Summary of Blockade Experiments

7. 11/22/2016 Computational Models of Neural Systems 7 Acetylcholine Effects (2) Acetylcholine also: ● Reduces neuronal adaptation in CA1 by suppressing voltage and Ca 2+ dependent potassium currents. – This keeps the cells excitable. ● Enhances synaptic modification in CA1 – possibly by affecting NMDA currents. ● Activates inhibitory interneurons – but decreases inhibitory synaptic transmission.

8. 11/22/2016 Computational Models of Neural Systems 8 Hasselmo's Model: Block Diagram EC CA1 CA3 Medial Septum ACh fimbria/fornix pp Sch

9. 11/22/2016 Computational Models of Neural Systems 9 Hasselmo's Model

10. 11/22/2016 Computational Models of Neural Systems 10 Initial CA1 Activation Function g(x)

11. 11/22/2016 Computational Models of Neural Systems 11 S. Radiatum Learning Rule ● Note: the only learning in this model is in R ik, the weights on the CA3  CA1 connections. Two factors: – Linear potentiation when pre- and post-synaptic cells are simultaneously active. – Exponential decay whenever the postsynaptic cell is active.

12. 11/22/2016 Computational Models of Neural Systems 12 Learning Rule: Hebbian Facilitation Plus Synaptic Decay 0 1 1 0 Presynaptic Postsynaptic  

13. 11/22/2016 Computational Models of Neural Systems 13 Exponential Weight Decay

14. 11/22/2016 Computational Models of Neural Systems 14 Control of Cholinergic Modulation ● Cholinergic modulation  was controlled by the amount of activity in CA1: ● This is an inverted sigmoid activation function of form 1 - 1/(1+exp(x)): – With no CA1 activity,  is close to 1. – With maximal CA1 activity,  is close to 0.

15. 11/22/2016 Computational Models of Neural Systems 15 ACh Modulation of Recall

16. 11/22/2016 Computational Models of Neural Systems 16 ACh Modulation of Learning

17. 11/22/2016 Computational Models of Neural Systems 17 What Do These Terms Look Like?

18. 11/22/2016 Computational Models of Neural Systems 18 Train Train Test Test Recovery from weight decay caused by recall of pattern 2.

19. 11/22/2016 Computational Models of Neural Systems 19 Weak suppression in s. rad. and none in s. l.-m. Result: unwanted learning causes memory interference. Strong suppression in s. rad. and also in s. l.-m. Result: retrieval fails.

20. 11/22/2016 Computational Models of Neural Systems 20 Larger Simulation Learned 5 patterns. After learning, CA3 input is sufficient to recall the patterns.

21. 11/22/2016 Computational Models of Neural Systems 21 Memory Performance Average overlap with all stored patterns.

22. 11/22/2016 Computational Models of Neural Systems 22 C L vs. C R Parameter Space ● Performance is plotted on z axis. ● Grey line shows C L = C R. ● White line shows dose- response plot from carbachol experiment.

23. 11/22/2016 Computational Models of Neural Systems 23 Comparison with Marr Model ● Distinguishing learning vs. recall: – Marr assumed recall would always use small subpatterns, perhaps one tenth the size of a full memory pattern. Not enough activity to trigger learning. – Hasselmo assumes that unfamiliar patterns only weakly activate CA1, and that leads to elevated ACh which enhances learning. ● Input patterns: – Marr assumes inputs are sparse and random, so nearly orthogonal. – Hasselmo's simulations use small vectors so there is substantial overlap between patterns. Uses ACh modulation to suppress interference.

24. 11/22/2016 Computational Models of Neural Systems 24 A Model of Episodic Memory

25. 11/22/2016 Computational Models of Neural Systems 25 ACh Prevents Overlap w/Previously Stored Memories from Interfering with Learning

26. 11/22/2016 Computational Models of Neural Systems 26 Simulation of ACh Effects 10 input neurons 2 inhibitory neurons 1 ACh signal

27. 11/22/2016 Computational Models of Neural Systems 27 Episodic Memory Simulation ● Each layer contains both Context and Item units. ● Train on list of 5 patterns. ● During recall, supply ony the context. ● An adaptation process causes recalled items to eventually fade so that another item can become active.

28. 11/22/2016 Computational Models of Neural Systems 28 “Consolidation” Train model on set of 6 patterns. During consolidation, use free recall to train slow-learning recurrent connections in EC layer IV. After training, a partial input pattern (not shown) recalls the full pattern in layer cortex. poor good

29. 11/22/2016 Computational Models of Neural Systems 29 Summary ● Unwanted recall of old patterns can interfere with storing new ones. ● The hippocampus must have some way of preventing this interference. ● Cholinergic modulation in CA1 (and also CA3) affects both synaptic transmission and LTP. ● Acetylcholine may serve as the “novelty” signal: – Unfamiliar patterns  high ACh  learning – Familiar patterns  low ACh  recall ● CA1 might serve as a comparator of current EC input with reconstructed input from CA3 projection to determine pattern familiarity.