Neural assemblies in hippocampus encode positions. During rest, the hippocam-
pus replays sequences of neural activity seen during awake behavior. This replay
is linked to memory consolidation and mental exploration of the environment. Re-
current networks can be used to model the replay of sequential activity. Multiple
sequences can be stored in the synaptic connections. To achieve a high mem-
ory capacity, recurrent networks require a pattern separation mechanism. Such a
mechanism is global remapping, observed in place cell populations. A place cell
fires at a particular position of an environment and is silent elsewhere. Multiple
place cells usually cover an environment with their firing fields. Small changes in
the environment or context of a behavioral task can cause global remapping, i.e.
profound changes in place cell firing fields. Global remapping causes some cells to
cease firing, other silent cells to gain a place field, and other place cells to move
their firing field and change their peak firing rate. The effect is strong enough to
make global remapping a viable pattern separation mechanism.
We model two mechanisms that improve the memory capacity of recurrent net-
works. The effect of inhibition on replay in a recurrent network is modeled using
binary neurons and binary synapses. A mean field approximation is used to de-
termine the optimal parameters for the inhibitory neuron population. Numerical
simulations of the full model were carried out to verify the predictions of the mean
field model. A second model analyzes a hypothesized global remapping mecha-
nism, in which grid cell firing is used as feed forward input to place cells. Grid
cells have multiple firing fields in the same environment, arranged in a hexagonal
grid. Grid cells can be used in a model as feed forward inputs to place cells to produce place fields. In these grid-to-place cell models, shifts in the grid cell firing
patterns cause remapping in the place cell population. We analyze the capacity of
such a system to create sets of separated patterns, i.e. how many different spatial
codes can be generated. The limiting factor are the synapses connecting grid cells
to place cells. To assess their capacity, we produce different place codes in place
and grid cell populations, by shuffling place field positions and shifting grid fields
of grid cells. Then we use Hebbian learning to increase the synaptic weights be-
tween grid and place cells for each set of grid and place code. The capacity limit
is reached when synaptic interference makes it impossible to produce a place code
with sufficient spatial acuity from grid cell firing. Additionally, it is desired to
also maintain the place fields compact, or sparse if seen from a coding standpoint.
Of course, as more environments are stored, the sparseness is lost. Interestingly,
place cells lose the sparseness of their firing fields much earlier than their spatial
acuity.
For the sequence replay model we are able to increase capacity in a simulated
recurrent network by including an inhibitory population. We show that even
in this more complicated case, capacity is improved. We observe oscillations in
the average activity of both excitatory and inhibitory neuron populations. The
oscillations get stronger at the capacity limit. In addition, at the capacity limit,
rather than observing a sudden failure of replay, we find sequences are replayed
transiently for a couple of time steps before failing. Analyzing the remapping
model, we find that, as we store more spatial codes in the synapses, first the
sparseness of place fields is lost. Only later do we observe a decay in spatial
acuity of the code. We found two ways to maintain sparse place fields while
achieving a high capacity: inhibition between place cells, and partitioning the
place cell population so that learning affects only a small fraction of them in
each environment. We present scaling predictions that suggest that hundreds of
thousands of spatial codes can be produced by this pattern separation mechanism.
The effect inhibition has on the replay model is two-fold. Capacity is increased, and
the graceful transition from full replay to failure allows for higher capacities when
using short sequences. Additional mechanisms not explored in this model could
be at work to concatenate these short sequences, or could perform more complex operations on them. The interplay of excitatory and inhibitory populations gives
rise to oscillations, which are strongest at the capacity limit. The oscillation
draws a picture of how a memory mechanism can cause hippocampal oscillations
as observed in experiments. In the remapping model we showed that sparseness of
place cell firing is constraining the capacity of this pattern separation mechanism.
Grid codes outperform place codes regarding spatial acuity, as shown in Mathis et
al. (2012). Our model shows that the grid-to-place transformation is not harnessing
the full spatial information from the grid code in order to maintain sparse place
fields. This suggests that the two codes are independent, and communication
between the areas might be mostly for synchronization. High spatial acuity seems
to be a specialization of the grid code, while the place code is more suitable for
memory tasks.
In a detailed model of hippocampal replay we show that feedback inhibition can
increase the number of sequences that can be replayed. The effect of inhibition
on capacity is determined using a meanfield model, and the results are verified
with numerical simulations of the full network. Transient replay is found at the
capacity limit, accompanied by oscillations that resemble sharp wave ripples in
hippocampus. In a second model
Hippocampal replay of neuronal activity is linked to memory consolidation and
mental exploration. Furthermore, replay is a potential neural correlate of episodic
memory. To model hippocampal sequence replay, recurrent neural networks are
used. Memory capacity of such networks is of great interest to determine their
biological feasibility. And additionally, any mechanism that improves capacity has
explanatory power. We investigate two such mechanisms.
The first mechanism to improve capacity is global, unspecific feedback inhibition
for the recurrent network. In a simplified meanfield model we show that capacity
is indeed improved.
The second mechanism that increases memory capacity is pattern separation. In
the spatial context of hippocampal place cell firing, global remapping is one way
to achieve pattern separation. Changes in the environment or context of a task
cause global remapping. During global remapping, place cell firing changes in unpredictable ways: cells shift their place fields, or fully cease firing, and formerly
silent cells acquire place fields. Global remapping can be triggered by subtle
changes in grid cells that give feed-forward inputs to hippocampal place cells.
We investigate the capacity of the underlying synaptic connections, defined as the
number of different environments that can be represented at a given spatial acuity.
We find two essential conditions to achieve a high capacity and sparse place fields:
inhibition between place cells, and partitioning the place cell population so that
learning affects only a small fraction of them in each environments. We also find
that sparsity of place fields is the constraining factor of the model rather than
spatial acuity. Since the hippocampal place code is sparse, we conclude that the
hippocampus does not fully harness the spatial information available in the grid
code. The two codes of space might thus serve different purposes.
| Identifer | oai:union.ndltd.org:MUENCHEN/oai:edoc.ub.uni-muenchen.de:18454 |
| Date | 29 June 2015 |
| Creators | Kammerer, Axel |
| Publisher | Ludwig-Maximilians-Universität München |
| Source Sets | Digitale Hochschulschriften der LMU |
| Detected Language | English |
| Type | Dissertation, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://edoc.ub.uni-muenchen.de/18454/ |
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