1. What is the role of the Dentate Gyrus? The role of DG CA1 Sb CA3 DG
Trisynaptic circuit in the hippocampus
Perforant Path
(input from EC)
CA1
Mossy fibers
(DG-CA3 connections) Sb
Schaffer collaterals
(CA3-CA1 connections)
Recurrent collaterals
(CA3-CA3 connections)
Erika Cerasti
CA3 DG
Introduction
The study of the info has been performed through simulations and analytical calculation, here you can see the results.
Regarding the dependence on the connectivity... in this graph
What is the role of
the Dentate Gyrus?

2. it does not seem to be a new frame of mind
Rodriguez et al, 2002 and others from the Sevilla group

3. The entire Hippocampus, or rather Medial Pallium..
Reptiles Mammals Birds
neurogenesis neurogenesis neurogenesis
Zinc Zinc Zinc
acetylcholine acetylcholine acetylcholine
recurrent nets recurrent nets recurrent nets
spatial memory spatial memory spatial memory
Turtles spatial memory
Font et al, 2001 Lindsey and Tropepe, Bingman et al, 2005
Smeets et al, Martinez-Garcia and Olucha, Montagnese et al, 1993
Reiner 1993 Krebs et al, Sherry et al, 1993
Rodriguez et al, 2002
No Dentate Gyrus!
Goldfish spatial memory

4. The new mammalian contraption is the Dentate Gyrus Georg Striedter
J Comp Neurology
(2016)
Evolution of the
hippocampus in
reptiles and birds
The new mammalian
contraption is
the Dentate Gyrus

5. Autoassociative network stored in the synaptic weights of the network
Introduction
The role of DG
RC contribution
Autoassociative network
CA3
Input pattern 2
Input pattern 1
Recurrent connections
(David Marr,
Bruce McNaughton,
Edmund Rolls)
different patterns
(memories)
stored in the synaptic weights of the network
Hebbian learning
HOPFIELD Model
To try to answer this question we refer to theories which consider CA3 to work as an autoassociator to perform memory abilities.
Through the presence of a large number of RC in the areas.
when an external input comes it impose an activity to the network;
this pattern of activity is the network representation of the external input
Hebbian learning that is modification of synaptic weights
Storage capacity – the maximum number of storable patterns
depends on the connectivity of the network and on the sparseness of its representations

6. Introduction
The role of DG
RC contribution
the Hopfield model does not say how representations are established
Memory storage is difficult to implement in CA3 by itself because of the interference
due to previously stored memories
(and recurrent connection dominance)‏
Differentiation of storage and retrieval phase
External Input activity should prevail during storage
Recurrent collateral activity should prevail during retrieval
CA3
External input
CA3
mossy
fibers DG
Some studies suggest the DG could also
help CA3 to form memory representation
McNaughton’s detonator synapses
The Acetylcholine
hypothesis
(Hasselmo et al)
However the hopfield model does not say what determines the activity distribution relative to each pattern or external input
That is how the storage occurs
In particular for dg some studies suggest can help in solving this problem
DG can impose in the CA3 activity the new pattern to store
MF have strong and sparse synapses, suitable for that, as shown in the work of treves and rolls 1992
DENTATE GYRUS
a preprocessing stage can separate storage and retrieval, imposing to CA3 the new pattern to store
MF have strong and sparse synapses

7. MF inputs establish new representations
Introduction
The role of DG
RC contribution
Functional differentiation of the afferent inputs to CA3 - Information measures
the information available for retrieval should be at least equal to the amount
which is indeed retrievable by the net
Information vs CA3 sparseness (a)
Comparison of the amount of storable information carried by afferent inputs to CA3
the Perforant Path input is too weak to force learning
and that mf input to CA3 is the one crucial to establish informative representation in CA3, as shown in treves… this theoretical work involves only discrete representations
Treves & Rolls, Hippocampus 1992
Binary distribution of DG activity - Discrete patterns
Hypothesis:
MF inputs establish new representations
PP inputs relay the cue for retrieval

8. Spatial tasks in rodents
Introduction
The role of DG
RC contribution
Experimental tests of the hypothesis:
Spatial tasks in rodents
While the experimental support to this hypothesis come from studies with rodents performing spatial task, involving spatial representations.
So we want to concentrate in our study on the role of DG in the storage of spatial representations in CA3.
We can do that because now we know the firing code of DG, As leutgeb et al shows in their work the dg cells present multiple firing fields

9. Spatial tasks in rodents
Introduction
The role of DG
RC contribution
Experimental tests of the hypothesis:
Spatial tasks in rodents
While the experimental support to this hypothesis come from studies with rodents performing spatial task, involving spatial representations.
So we want to concentrate in our study on the role of DG in the storage of spatial representations in CA3.
We can do that because now we know the firing code of DG, As leutgeb et al shows in their work the dg cells present multiple firing fields

10. Spatial tasks in rodents
Introduction
The role of DG
RC contribution
Experimental tests of the hypothesis:
Spatial tasks in rodents
Acquisition Index: Errors (T1-5 D1) - (T6-10 D1)
Retrieval Index: Errors (T6-10 D1) - (T1-5 D2)
While the experimental support to this hypothesis come from studies with rodents performing spatial task, involving spatial representations.
So we want to concentrate in our study on the role of DG in the storage of spatial representations in CA3.
We can do that because now we know the firing code of DG, As leutgeb et al shows in their work the dg cells present multiple firing fields

11. Discrete representations Continuous representations
Introduction
The role of DG
RC contribution
Information from DG relative to spatial locations
Discrete representations Continuous representations
(x0 y0)
Continuous 2-Dimensional Attractor
(x1 y1)
In an IDEAL continuous attractor each position in space corresponds to an attractor for the activity in the network.
The ensemble of such attractors for
all locations in space comprises the memory of the environment (chart).
The theoretical study discrete representation while the experimental work involve spatial representation in rodents
We want in our study on the role of DG we want to pass from discrete representations to continuous representation in the network and
to do that we have to refer to the concept of the 2D continuous attractor
in the storage of spatial representations in CA3
If we want to represent spatial locations in the network we need configurations of the network to be change continuously, as the spatial input does.
as the space we want to represent
(x2 y2)
Attractor state for the population vector
Memory of multiple environments may require
the storage of multiple charts.
Multi-charts model (Samsonovich and MNaughton 1997)

12. Multiple firing fields for DG cells
Introduction
The role of DG
RC contribution
Spatial Representations in DG :
Firing activity in DG:
spatial localized receptive fields, place-like.
Multiple firing fields for DG cells
Coding model:
While the experimental support to this hypothesis come from studies with rodents performing spatial task, involving spatial representations.
So we want to concentrate in our study on the role of DG in the storage of spatial representations in CA3.
We can do that because now we know the firing code of DG. As leutgeb et al shows in their work the dg cells present multiple firing fields
Sparse level of firing p ≃ 0.03
Number of firing fields given
by Poisson probability with q ≃ 1.7
Leutgeb et al, Science 2007

13. Model

14. The role of DG Storing a new map δ Introduction RC contribution DG
(x)‏
Hypothesis
Mossy fibers drive the storage of new information
External Input
Modeling assumption
only DG inputs carry the
relevant information about a new spatial map to be stored in CA3.
CA3 DG c δ
noise
AMOUNT of INFORMATION
about a new spatial map
mossy fibers
The model we build is composed by a layer of DG cells projecting to a layer of CA3 cell, the connectivity level is indicated by c that is the number of dg cells projecting to a single ca3 cell.
We remove the recurrent connections in ca3 and consider them as noise. Then we define the fields and study how the amount of info depend on the paramenters storage of a new map, representative of an environment

15. c = # DGCs projecting to a single CA3 cell
The role of DG
Introduction
RC contribution c δ
CA3 DG c δ
AMOUNT of INFORMATION
about a new spatial map
Sum of Gaussian functions
noise
c = # DGCs projecting to a single CA3 cell DG
pDG ≃ 0.03
The model we build is composed by a layer of DG cells projecting to a layer of CA3 cell, the connectivity level is indicated by c that is the number of dg cells projecting to a single ca3 cell. Then we define the fields and study how the amount of info depend on the parameters
A Gaussian for each fields on the dg unit
The firing in CA3 then results from this formula
Poisson distribution
with q = 1.7
CA3
Threshold-linear units
Qj = number of field for cell j

16. Analytical Calculation

17. The role of DG Mutual Information Introduction RC contribution
We calculate the amount of information about a new environment (x) that is established in CA3 cells (η) by DG spatial activity (β)‏
Mutual Information
ENTROPY
CONDITIONAL ENTROPY

18. The role of DG
Introduction
RC contribution ?

19. m-fields decomposition
The role of DG
Introduction
RC contribution
Mutual Information per single CA3 cell
<I> = ∑mCm < Dm>
m-fields decomposition
m-fields contribution to the information
<> Quenched variables
average
Decomposed in pieces each one considering the contribution of a fixed number of input fields x y
Dm - spatial signal produced by m DG fields
Cm - combination of Poisson coefficients

20. Simulations

21. The role of DG CA1 Sb CA3 DG Introduction RC contribution
Trisynaptic circuit in the hippocampus
Perforant Path
(input from EC)
CA1
Mossy fibers
(DG-CA3 connections) Sb
Schaffer collaterals
(CA3-CA1 connections)
Recurrent collaterals
(CA3-CA3 connections)
CA3 DG
A spatial random number generator
dependence on c, the connectivity DG  CA3:
The study of the info has been performed through simulations and analytical calculation, here you can see the results.
Regarding the dependence on the connectivity... in this graph
Simulations
Analytical Result
Erika Cerasti & AT,
2010, 2013

22. Recurrent Collaterals Continuous Attractors?
Adding
Recurrent Collaterals
Continuous Attractors?
Now we look at what happen when we add the RC collateral in our system,
Pre-wired vs self-organized

23. Enhanced effect of Neurogenesis?DG
CA3
New potentially neurogenesis-related observations:
(by Charlotte Alme in the Moser lab)
Sizable fraction of «overactive» CA3 units
(perhaps the majority of IEG-active ones, as seen by Nora Abrous)

24. number of rooms in which a CA3 cell is active
frequency

25. Enhanced effect of Neurogenesis?DG
CA3
New potentially neurogenesis-related observations:
(by Charlotte Alme in the Moser lab)
Sizable fraction of «overactive» CA3 units
(perhaps the majority of IEG-active ones, as seen by Nora Abrous)
Hypothesis:
They are those driven by 1, 2+ youthful DG units
(see model by S Temprana, E Kropff & A Schinder)

26. + + number of rooms in which a CA3 cell is active frequency
2 ha GC inputs + +
1 ha GC input
No hyperactive adult-born GC input

27. to the different habitat ?
Future plans: differences among rodents…
burrows of a
Norway rat
…may they be related
to the different habitat ?
CA1
CA3 DG
burrows of a
Cape mole-rat
lab of Irmgard Amrein, Zurich

28. If not before, then stop here, please.

29. RC contribution Learning RC
Introduction
The role of DG
New Maps
(x)‏
Retrieving a stored map
New Map
(x)‏
External Input c MF
Learning
AMOUNT of INFORMATION
about the stored spatial map RC
Till now we considered only the storage now and in particular the retrieval of the spatial representations established in CA3 by the DG
Now we add the activity of RC, allowing the plasticity oh them when the input is present and then
So the model is the same as before, with the add of such terms in the firing of CA3 units.
Now we have the input from the DG and the input coming from the other CA3 unit that determine the activity of a CA3 unit, with noise and a threshold as before.
Jij weights
Differently from before we allow plasticity during the presentation of the input
So we have an external input coming from dg about a new map, a learning phase in which J are allowed to change, and then a recover phase in which the input is turned off and the system have to maintain the information
We see the amount of information the RC activity contain about a previously presented map, in absence of input.
So we study the retrieval of a single spatial map when a single map is stored and when multiple maps are stored
RC + 

30. Pre-wired connectivity
RC contribution
Introduction
The role of DG
LEARNING ?
Pre-wired connectivity
Hebbian Learning
1d 2d established in several model
The learning could be represented by a pre-wired connectivity or a hebbian learning
We study the system and compare the two cases.
For the pre-wired connectivity the weights are chosen as exponential decreasing functions of the distance between place centers,
What happen is quite well established by previous studies on 1D or 2D models
While for the self organizing connectivity we allow weights to change in the learning phase according to this formula.
Where a trace term is present.

31. recurrent connections preserve it, but such randomly established
representations are distorted...
accurate distorted
MF inputs alone,
acting as a spatial
random number generator,
produce a reasonable
spatial code

32. Lack of precision is a finite size effect Real CA3 attractors
can be precise
λ=1
self-org
λ=2
Self-organized ones
may not be accurate

33. Such lack of accuracy comes with less spatial information
..but much more contextual information than for grid cells

34. Can one iron out the wrinkles in CA3 charts, with RC learning?
More learning makes attractors
more informative about position
More learning makes attractors
less informative about context
 we should pay attention to Rene Hen’s distinction between
position and context
position
context
Cerasti & Treves, 2013