2. We all live under the same roof PALEOCORTEX p  C / [a ln(1/a)] i p  N a ln(1/a) I/CN  O(1 bit)

3. DG CA3 CA1 platypus What makes us non-lizards?

4. hippocampal reorganization includes a spatial migration......it does not lead to a new type of cortex...

5. but it is, fundamentally, a granulation. the medial wall of cortex reorganizes into the hippocampus by inserting the fascia dentata, with its granule cells, at the input note that the granule cells are (excitatory) interneurons

6. watch evolution on-line, in the opossum

7. David Marr, over 30 years ago, suggested to start from the function In humans, the hippocampus had long been implicated in the formation of episodic and autobiographical memories (here, data by Graham & Hodges)

8. Over the last few years, imaging evidence has corroborated traditional neuropsychological evidence (here, fMRI study of verbal encoding into episodic memory by Fernandez et al)

9. In rats, the evidence from neurophysiological recordings indicates a primary role in spatial memory (here, data from simultaneous recordings by Matt Wilson & Bruce McNaughton)

10. (although a minority view has emphasized a more active role in spatial computation; here, data by Neil Burgess & John O’Keefe)

11. In monkeys, Edmund Rolls et al have found spatial view cells, suggestive of a hippocampal role intermediate between the human and the rat description

12. David Marr’s perspective was the same adopted by most of his followers... (diagram by Jaap Murre, 1996)

13. If the Marr approach is correct the function should explain this structure

14. Yet, birds use their hippocampus, which has a simpler structure, in a similar way ?!?

15. So, let us follow the same functional hypothesis... …but let us try to be quantitative

16.  is a Content Addressable Memory, which can be minimally implemented as an autoassociator with Hebbian plasticity on its recurrent collaterals. A device able to: generate, on line, compressed representations of cortical activity store them on line, in a single “shot” hold multiple representations retrieve each one from partial cues send back the retrieved information in a robust format I ~ N a ln(1/a) CAM associative (CA3?)

17. CA3 is dominated by recurrent collaterals

18. The analysis of large- scale recordings (here, by Skaggs & McNaughton) shows that the information content of hippocampal representations grows linearly with population size, before saturating at the ceiling set by the experiment. Francesco Battaglia has quantified the full I item for place cells, using an analytical model, and he has shown how to map the storage capacity for continuous attractors (“charts”) into that for discrete ones (“episodes”).

19.  requires a dedicated preprocessor that sparsifies and decorrelates input activity generate, on line, compressed representations  store them on line, in a single “shot”  hold multiple representations  retrieve each one from partial cues send back the retrieved information in a robust format PP inputs (from EC) modify during storage and relay the cue at retrieval MF inputs (from DG) force informative storage and are irrelevant for retrieval

20. The crucial prediction is consistent with recordings from normal rats

21. but it is difficult to test it in dentate lesioned rats (Tucson data by Jim Knierim)

23.  is greatly facilitated by expansion recoding with additional associative ‘polishing’  generate, on line, compressed representations  store them on line, in a single “shot”  hold multiple representations  retrieve each one from partial cues the read-out of the information retrieved in CA3 CA3 DG ?

27. CA3 CA1 Analytical models predict an optimal plasticity level for CA3->CA1 (Schaffer) collaterals, but are not yet constrained enough to predict the observed memory activation differences information gain

28. Why the CA3-CA1 differentiation? the answer may lie in the predictive ability that several models assign to the hippocampus. An undifferentiated CA network can both retrieve and predict, but a differentiation may help: although CA3 may predict future “contexts” as well as CA1, this may conflict with devoting its recurrent collaterals to retrieve the current “context”. It could be, thus, that a CA3-CA1 differentiation brings about a quantitative advantage. A simplified neural network simulation is the most efficient approach to address the issue.

29. CA1 CA3 DG perforant path uniform mossy fibers collaterals PP differentiated RC SC MF CA

30. 2 noisy input cue `bump’ moves 0.5cm=0.2 unit per 12.5msec iteration mossy fibers point-to-point, and active only during training perforant path modifies with no trace rule CA1 CA3 EC (DG) the model connections (initially all random) 20 units collaterals: come only from CA3 in the differentiated model, and are 66% suppressed in training

31. LTP (STDP) present past future + present p f A1 present past future + adaptation B storage retrieval LTP present past future pres. reverb. p f no rev. + present A2 but first, what mechanism can yield prediction? there are at least 3 candidates...

32. LTP (STDP) present past future + present p f A1 storage retrieval STDP (at least when modelled with a simple trace rule) is not quite effective enough, here, to produce prediction STDP

33. LTP present past future pres. reverb. p f no rev. + present A2 storage retrieval reverberation delays are no good either modulated at retrieval storage

34. present past future + adaptation B firing rate adaptation can do it!

35. present past future + adaptation B and differentiation does not help

36. though it does improve localization, just a bit

37. the advantage depends on the relative strength of collateral connections during storage...

38. and during retrieval, in a non-trivial way

39. each representation has its optimal sparsity: CA DG EC

40. The mammalian hippocampus appears to be handsomely crafted but why it needed 2 separate CA fields, we do not quite understand Gyuri Buzsaki might know and Lokendra Shastri would have us believe there are even more...

41. ...and should anyone take away from such words and predictions, God shall take away his part out of the book of life, and out of the holy city   Revelations of John, XXII, 19-20 The last words Says the experimenter this: Yes, I shall come quickly Moser lab, Trondheim Knierim lab, Texas CA3  CA1